ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 52,2026 Issue 9
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- The Future Projections of Extreme Weather, Climate and Water Events and Strategic Responses
- A Review of Downburst Genesis Mechanism and Warning
- The Pattern Structure and Thermodynamic and Dynamic Processes of Severe Storms Associated with Linear Convective Gales
- Analysis on Extremity and Characteristics of the “21·7” Severe Torrential Rain in Henan Province
- Diagnostic Analysis on Water Vapor and Jet Characteristics of the July 2021 Severe Torrential Rain in Henan Province
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2026,52(9):1033-1050, DOI: 10.7519/j.issn.1000-0526.2026.060101
Abstract:
Based on the 24 h surface precipitation observation data from 08:00 BT to 08:00 BT of the next day and upper-air sounding data at 08:00 BT and 20:00 BT during 2016-2023 as well as ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), this paper identifies a total of 83 torrential rain days in Beijing during the study period. On this basis, Beijing torrential rain events are classified according to the synoptic situations at 500 hPa, and a statistical analysis is conducted on the specific humidity characteristics at 925 hPa, 850 hPa, 700 hPa, 500 hPa and 200 hPa during the torrential rain days. The results are as follows. The torrential rain events in Beijing are divided into six types: torrential rain induced by Mongolian low vortex and trough type, subtropical high and westerly trough type, Hetao low-vortex type, northeast cold vortex type, tropical cyclone (typhoon) type, and westerly trough and typhoon type. The Hetao low-vortex type and tropical cyclone (typhoon) type tend to produce more extensive extreme torrential rains, while the northeast cold vortex type is dominated by localized torrential rain. In terms of specific humidity, it is markedly higher at all atmospheric levels during torrential rain periods than that during non-torrential rain periods. Among the six types of torrential rain events, tropical cyclone (typhoon) type and Hetao low-vortex type feature the highest specific humidity, which corresponds to the strongest precipitation intensity and the largest rainfall coverage of these two categories. Furthermore, based on hourly precipitation data, the precipitation conditions within one hour before and after the upper-air observation time (08:00 BT and 20:00 BT) are categorized into three grades: torrential rain (≥10 mm), light rain (0.1-9.9 mm) and no precipitation. The specific humidity is the highest at all levels during torrential rain period, followed by that in light rain period, and the lowest specific humidity is found in no precipitation period. In addition, the specific humidity of torrential rain events corresponding to winds from eight directions at different levels is statistically analyzed, and the results indicate that the specific humidity reaches its maximum when the wind direction ranges from 90° to 225° with jet streams, and drops to its minimum when the wind direction is between 270° and 315° accompanied by jet streams. Overall, this study has not only analyzed the specific humidity characteristics below 700 hPa during torrential rain events, but more specially found that the increase in specific humidity at 500 hPa can serve as a significant predictor for torrential rain forecasting.
Based on the 24 h surface precipitation observation data from 08:00 BT to 08:00 BT of the next day and upper-air sounding data at 08:00 BT and 20:00 BT during 2016-2023 as well as ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), this paper identifies a total of 83 torrential rain days in Beijing during the study period. On this basis, Beijing torrential rain events are classified according to the synoptic situations at 500 hPa, and a statistical analysis is conducted on the specific humidity characteristics at 925 hPa, 850 hPa, 700 hPa, 500 hPa and 200 hPa during the torrential rain days. The results are as follows. The torrential rain events in Beijing are divided into six types: torrential rain induced by Mongolian low vortex and trough type, subtropical high and westerly trough type, Hetao low-vortex type, northeast cold vortex type, tropical cyclone (typhoon) type, and westerly trough and typhoon type. The Hetao low-vortex type and tropical cyclone (typhoon) type tend to produce more extensive extreme torrential rains, while the northeast cold vortex type is dominated by localized torrential rain. In terms of specific humidity, it is markedly higher at all atmospheric levels during torrential rain periods than that during non-torrential rain periods. Among the six types of torrential rain events, tropical cyclone (typhoon) type and Hetao low-vortex type feature the highest specific humidity, which corresponds to the strongest precipitation intensity and the largest rainfall coverage of these two categories. Furthermore, based on hourly precipitation data, the precipitation conditions within one hour before and after the upper-air observation time (08:00 BT and 20:00 BT) are categorized into three grades: torrential rain (≥10 mm), light rain (0.1-9.9 mm) and no precipitation. The specific humidity is the highest at all levels during torrential rain period, followed by that in light rain period, and the lowest specific humidity is found in no precipitation period. In addition, the specific humidity of torrential rain events corresponding to winds from eight directions at different levels is statistically analyzed, and the results indicate that the specific humidity reaches its maximum when the wind direction ranges from 90° to 225° with jet streams, and drops to its minimum when the wind direction is between 270° and 315° accompanied by jet streams. Overall, this study has not only analyzed the specific humidity characteristics below 700 hPa during torrential rain events, but more specially found that the increase in specific humidity at 500 hPa can serve as a significant predictor for torrential rain forecasting.
2026,52(9):1051-1065, DOI: 10.7519/j.issn.1000-0526.2026.070401
Abstract:
This study conducts a high-resolution numerical simulation and sensitivity experiments on a typical landing convective storm that occurred on 8 August 2022, and investigates the impact of sea surface temperature (SST) variation on the intensity of landing convective precipitation by increasing and decreasing the SST of the Yellow Sea and Bohai Sea. The results show that, compared with the control experiment, the increased SST can enhance the intensity of landing convective precipitation. When SST is raised by 1℃ (2℃), the average accumulated precipitation in the coast increases by 2.6 mm (15.7 mm) and the maximum precipitation increases by 38.2 mm (53.7 mm). When SST is lowered by 1℃ (2℃), the corresponding precipitation decreases by 3.7 mm (8.3 mm), but the maximum precipitation does not show any significant changes. Mechanism analysis indicates that, relative to the control experiment, a rise of 2℃ SST significantly strengthens the coastal boundary layer temperature gradient, wind convergence lifting, as well as the intensity of convective available potential energy and southward-moving cold pools, leading to pronounced reinforcement of the front zone at the cold pool leading edge and dynamic lifting. In contrast, a rise of 1℃ SST does not evidently enhance boundary layer wind convergence and southward-moving cold pool intensity, which results in weaker enhancement of the front zone and dynamic lifting than that under the condition of 2℃ SST increase. When SST decreases by 1℃ and 2℃, the mesoscale environmental conditions and southward-moving cold pool intensity weaken substantially. The front zone at the cold pool leading edge becomes indistinct, and dynamic lifting is greatly weakened or even transformed into downdrafts, ultimately reducing the intensity of landing convective precipitation. The findings of this paper can provide a theoretical references for the intensity forecasting of landing convective precipitation.
This study conducts a high-resolution numerical simulation and sensitivity experiments on a typical landing convective storm that occurred on 8 August 2022, and investigates the impact of sea surface temperature (SST) variation on the intensity of landing convective precipitation by increasing and decreasing the SST of the Yellow Sea and Bohai Sea. The results show that, compared with the control experiment, the increased SST can enhance the intensity of landing convective precipitation. When SST is raised by 1℃ (2℃), the average accumulated precipitation in the coast increases by 2.6 mm (15.7 mm) and the maximum precipitation increases by 38.2 mm (53.7 mm). When SST is lowered by 1℃ (2℃), the corresponding precipitation decreases by 3.7 mm (8.3 mm), but the maximum precipitation does not show any significant changes. Mechanism analysis indicates that, relative to the control experiment, a rise of 2℃ SST significantly strengthens the coastal boundary layer temperature gradient, wind convergence lifting, as well as the intensity of convective available potential energy and southward-moving cold pools, leading to pronounced reinforcement of the front zone at the cold pool leading edge and dynamic lifting. In contrast, a rise of 1℃ SST does not evidently enhance boundary layer wind convergence and southward-moving cold pool intensity, which results in weaker enhancement of the front zone and dynamic lifting than that under the condition of 2℃ SST increase. When SST decreases by 1℃ and 2℃, the mesoscale environmental conditions and southward-moving cold pool intensity weaken substantially. The front zone at the cold pool leading edge becomes indistinct, and dynamic lifting is greatly weakened or even transformed into downdrafts, ultimately reducing the intensity of landing convective precipitation. The findings of this paper can provide a theoretical references for the intensity forecasting of landing convective precipitation.
2026,52(9):1066-1078, DOI: 10.7519/j.issn.1000-0526.2026.070901
Abstract:
A rare left-moving supercell generated by storm splitting occurred in central and northern Jiangxi Province from 14:00 BT to 18:00 BT 10 May 2021. Using dual-polarization radar data from the Ji’an CINRAD/SC radar, upper-air and surface observations and ERA5 reanalysis data, this paper analyzes the environmental conditions, structural characteristics, and dynamic mechanisms of this event. The results show that the left-moving supercell was generated in an environment with high convective available potential energy (>3000 J·kg-1) and strong 0-6 km vertical wind shear (>20 m·s-1). The vertical wind shear vector of 3.0-4.9 km exhibited counterclockwise rotation with height, generating significant negative horizontal vorticity, which was a key factor for the selective intensification of the left-moving supercell. During its mature stage, the storm displayed a “dual-vortex” structure at mid-to-upper levels, with a mesoanticyclone located in the leading (northern) sector corresponding to a strong updraft, and a mesocyclone in the trailing (southern) sector associated with the forward-flank downdraft (FFD). Its overall appearance was approximately mirror-symmetrical to the structure of classic right-moving supercell. Dual-polarization parameters demonstrate that the large differential reflectivity factor (ZDR) zone in the lower levels was related to particle sorting under strong vertical wind shear, while the ZDR ring and ZDR column at mid-levels indicated strong rotation and updraft within the meso-anticyclone, respectively. The formation of the mesoanticyclone was attributed to the tilting of negative horizontal vorticity into negative vertical vorticity by the robust updraft. The leftward motion of the storm was closely related to the location of FFD and the Magnus effect resulting from the pressure gradient force induced by the asymmetry between the northern and southern vortices. This study provides a valuable case for understanding the development, structure, and microphysical characteristics of left-moving supercells in China.
A rare left-moving supercell generated by storm splitting occurred in central and northern Jiangxi Province from 14:00 BT to 18:00 BT 10 May 2021. Using dual-polarization radar data from the Ji’an CINRAD/SC radar, upper-air and surface observations and ERA5 reanalysis data, this paper analyzes the environmental conditions, structural characteristics, and dynamic mechanisms of this event. The results show that the left-moving supercell was generated in an environment with high convective available potential energy (>3000 J·kg-1) and strong 0-6 km vertical wind shear (>20 m·s-1). The vertical wind shear vector of 3.0-4.9 km exhibited counterclockwise rotation with height, generating significant negative horizontal vorticity, which was a key factor for the selective intensification of the left-moving supercell. During its mature stage, the storm displayed a “dual-vortex” structure at mid-to-upper levels, with a mesoanticyclone located in the leading (northern) sector corresponding to a strong updraft, and a mesocyclone in the trailing (southern) sector associated with the forward-flank downdraft (FFD). Its overall appearance was approximately mirror-symmetrical to the structure of classic right-moving supercell. Dual-polarization parameters demonstrate that the large differential reflectivity factor (ZDR) zone in the lower levels was related to particle sorting under strong vertical wind shear, while the ZDR ring and ZDR column at mid-levels indicated strong rotation and updraft within the meso-anticyclone, respectively. The formation of the mesoanticyclone was attributed to the tilting of negative horizontal vorticity into negative vertical vorticity by the robust updraft. The leftward motion of the storm was closely related to the location of FFD and the Magnus effect resulting from the pressure gradient force induced by the asymmetry between the northern and southern vortices. This study provides a valuable case for understanding the development, structure, and microphysical characteristics of left-moving supercells in China.
2026,52(9):1079-1089, DOI: 10.7519/j.issn.1000-0526.2026.062801
Abstract:
On 4 August 2024, heavy rainstorm occurred at Wucheng Station in Dezhou, Shandong Province, breaking the historical records for both daily precipitation and maximum hourly precipitation at the station. Utilizing the observation data from automatic weather stations, Doppler weather radar and wind-profiling radar, as well as ERA5 reanalysis data and GDAS data, combined with the HYSPLIT model, this study reveals the multi-scale physical mechanisms that enabled the long-lasting backward propagating convective system and the extreme precipitation it brought. The results indicate that this event occurred under a large-scale circulation pattern characterized by a rearward-tilted trough confronting the subtropical high. Low level jet (LLJ) and ultra-low-level jet (ULLJ) persistently transported abundant water vapor from the South China Sea and the Bay of Bengal to northwestern Shandong, establishing an extremely unstable environment with high energy, high temperature and high humidity within the warm sector ahead of surface front. The cold pool outflow generated by precipitation interacted with the ambient warm, moist airflow, forming a quasi-stationary convergence line. Coupled with temporal and spatial pulsations of the LLJ, this interaction continuously drove new convective cells to propagate southwestward, directly causing the extreme heavy precipitation. The dynamic reconstruction of energy was the key to maintaining the system. Rainfall consumed unstable energy, but the persistent warm, moist advection transported by ULLJ allowed convective available potential energy to be rapidly rebuilt and maintained at its peak. Thus, a positive feedback self-sustaining mechanism of “energy transport-convection consumption-energy reconstruction” was formed, and it was this mechanism that led to the sustained occurrence of the back-propagating heavy precipitation.
On 4 August 2024, heavy rainstorm occurred at Wucheng Station in Dezhou, Shandong Province, breaking the historical records for both daily precipitation and maximum hourly precipitation at the station. Utilizing the observation data from automatic weather stations, Doppler weather radar and wind-profiling radar, as well as ERA5 reanalysis data and GDAS data, combined with the HYSPLIT model, this study reveals the multi-scale physical mechanisms that enabled the long-lasting backward propagating convective system and the extreme precipitation it brought. The results indicate that this event occurred under a large-scale circulation pattern characterized by a rearward-tilted trough confronting the subtropical high. Low level jet (LLJ) and ultra-low-level jet (ULLJ) persistently transported abundant water vapor from the South China Sea and the Bay of Bengal to northwestern Shandong, establishing an extremely unstable environment with high energy, high temperature and high humidity within the warm sector ahead of surface front. The cold pool outflow generated by precipitation interacted with the ambient warm, moist airflow, forming a quasi-stationary convergence line. Coupled with temporal and spatial pulsations of the LLJ, this interaction continuously drove new convective cells to propagate southwestward, directly causing the extreme heavy precipitation. The dynamic reconstruction of energy was the key to maintaining the system. Rainfall consumed unstable energy, but the persistent warm, moist advection transported by ULLJ allowed convective available potential energy to be rapidly rebuilt and maintained at its peak. Thus, a positive feedback self-sustaining mechanism of “energy transport-convection consumption-energy reconstruction” was formed, and it was this mechanism that led to the sustained occurrence of the back-propagating heavy precipitation.
2026,52(9):1090-1102, DOI: 10.7519/j.issn.1000-0526.2026.051501
Abstract:
Based on hourly precipitation data from high-density national and regional stations in Guizhou Province during the flood season (April-September) from 2010 to 2023, as well as Shuttle Radar Topography Mission (SRTM) elevation data and ERA5 reanalysis data, this study systematically investigates the fine-scale spatio-temporal characteristics of rainstorms and their relationships with terrain in Guizhou Province by using multivariate terrain combination analysis and a geographically weighted regression (GWR) model. The results show that rainstorms in Guizhou Province are distributed unevenly with three rainstorm centers. The maximum frequency and intensity of rainstorms are concentrated in the southeastern edge area and extremely heavy rainstorms are scattered. The rainstorm proportion is higher in the northeastern and central-southern parts of Guizhou Province. Rainstorm days show an increasing interannual variability, peaking in 2020-2021. Rainstorms occur mainly in June and July, often accompanied by short-time heavy precipitation and lasting for a long time, which poses high disaster risks. Diurnal variation of precipitation is unimodal and dominated by nocturnal rain (22:00 BT-08:00 BT), and the rainfall intensity often reaches its peaks around 03:00 BT. Short-time heavy precipitation exhibits a pronounced nocturnal peak, and the nighttime precipitation is roughly twice the daytime precipitation, mainly concentrated in the west and south of Guizhou Province and featured with eastward propagation. The Guizhou Province rainstorm-prone areas correspond to the topographic uplift zones of the Beipan River, Leigong Mountain, and Fanjing Mountain, reflecting terrain-wind coupling. Single terrain factors are weak to be directly correlated to precipitation. However, through the analysis of multiple terrain combinations, the explanatory power of precipitation distribution has been enhanced. Slope, aspect, elevation, and relief exert nonlinear effects. Rainstorm stations are the most in south aspects. Rainstorm frequency increases when slope is <20°, elevation ranges in 800-1000 m, and relief is <200 m, but decreases beyond these thresholds. The combined analysis indicates that the steep/moderate slope, south aspect, medium elevation, large/medium relief, and windward slope has the strongest triggering effect. The GWR model diagnosis suggests that there is obvious spatial heterogeneity in the influence of topographic factors. Elevation is the dominant factor in most areas of Guizhou Province, and the local influence of relief, slope and aspect is strong, mostly concentrated in the vicinity of large relief or near rivers and mountains.
Based on hourly precipitation data from high-density national and regional stations in Guizhou Province during the flood season (April-September) from 2010 to 2023, as well as Shuttle Radar Topography Mission (SRTM) elevation data and ERA5 reanalysis data, this study systematically investigates the fine-scale spatio-temporal characteristics of rainstorms and their relationships with terrain in Guizhou Province by using multivariate terrain combination analysis and a geographically weighted regression (GWR) model. The results show that rainstorms in Guizhou Province are distributed unevenly with three rainstorm centers. The maximum frequency and intensity of rainstorms are concentrated in the southeastern edge area and extremely heavy rainstorms are scattered. The rainstorm proportion is higher in the northeastern and central-southern parts of Guizhou Province. Rainstorm days show an increasing interannual variability, peaking in 2020-2021. Rainstorms occur mainly in June and July, often accompanied by short-time heavy precipitation and lasting for a long time, which poses high disaster risks. Diurnal variation of precipitation is unimodal and dominated by nocturnal rain (22:00 BT-08:00 BT), and the rainfall intensity often reaches its peaks around 03:00 BT. Short-time heavy precipitation exhibits a pronounced nocturnal peak, and the nighttime precipitation is roughly twice the daytime precipitation, mainly concentrated in the west and south of Guizhou Province and featured with eastward propagation. The Guizhou Province rainstorm-prone areas correspond to the topographic uplift zones of the Beipan River, Leigong Mountain, and Fanjing Mountain, reflecting terrain-wind coupling. Single terrain factors are weak to be directly correlated to precipitation. However, through the analysis of multiple terrain combinations, the explanatory power of precipitation distribution has been enhanced. Slope, aspect, elevation, and relief exert nonlinear effects. Rainstorm stations are the most in south aspects. Rainstorm frequency increases when slope is <20°, elevation ranges in 800-1000 m, and relief is <200 m, but decreases beyond these thresholds. The combined analysis indicates that the steep/moderate slope, south aspect, medium elevation, large/medium relief, and windward slope has the strongest triggering effect. The GWR model diagnosis suggests that there is obvious spatial heterogeneity in the influence of topographic factors. Elevation is the dominant factor in most areas of Guizhou Province, and the local influence of relief, slope and aspect is strong, mostly concentrated in the vicinity of large relief or near rivers and mountains.
2026,52(9):1103-1116, DOI: 10.7519/j.issn.1000-0526.2026.050702
Abstract:
In view of the characteristics of X-band phased array radar, which offers high spatio-temporal resolution but limited observation coverage and various errors that affect the reliability of data, this study proposes a method of high-quality data fusion between X-band phased array radar and S-band operational radar. First, the optical flow method is applied to extrapolate the motion trends of S-band radar data, enhancing its temporal resolution from 6 min to 1.5 min, synchronized with X-band radar. Subsequently, the optimal interpolation algorithm and pyramid transform algorithm are employed for the spatial fusion of dual-band radar data, and the corresponding products SXnet-O and SXnet-K are obtained. Statistical analysis based on nearly 5000 radar volume scans from the Guangdong-Hong Kong-Macao Greater Bay Area radar network from May to June 2022 demonstrates that the time-matching results extrapolated by the optical flow method outperform traditional persistence forecasting in terms of forecasting scores and correlation coefficients in the temporal dimension. In the spatial dimension, the fused data effectively compensate for the detection blind zones of a single radar, increasing the low-altitude (500 m) echo coverage by approximately 2.5 times compared to the S-band radar. Comparative results indicate that SXnet-O significantly outperforms SXnet-K in system bias control (92% of deviations within ±2 dB), parameter consistency, and boundary stability. Compared with the single-band network data, SXnet-O greatly reduces the mean deviation from the S-band reference. Moreover, the structural precision of X-band and the observational stability of S-band get effectively integrated.
In view of the characteristics of X-band phased array radar, which offers high spatio-temporal resolution but limited observation coverage and various errors that affect the reliability of data, this study proposes a method of high-quality data fusion between X-band phased array radar and S-band operational radar. First, the optical flow method is applied to extrapolate the motion trends of S-band radar data, enhancing its temporal resolution from 6 min to 1.5 min, synchronized with X-band radar. Subsequently, the optimal interpolation algorithm and pyramid transform algorithm are employed for the spatial fusion of dual-band radar data, and the corresponding products SXnet-O and SXnet-K are obtained. Statistical analysis based on nearly 5000 radar volume scans from the Guangdong-Hong Kong-Macao Greater Bay Area radar network from May to June 2022 demonstrates that the time-matching results extrapolated by the optical flow method outperform traditional persistence forecasting in terms of forecasting scores and correlation coefficients in the temporal dimension. In the spatial dimension, the fused data effectively compensate for the detection blind zones of a single radar, increasing the low-altitude (500 m) echo coverage by approximately 2.5 times compared to the S-band radar. Comparative results indicate that SXnet-O significantly outperforms SXnet-K in system bias control (92% of deviations within ±2 dB), parameter consistency, and boundary stability. Compared with the single-band network data, SXnet-O greatly reduces the mean deviation from the S-band reference. Moreover, the structural precision of X-band and the observational stability of S-band get effectively integrated.
2026,52(9):1117-1129, DOI: 10.7519/j.issn.1000-0526.2026.052501
Abstract:
Weather radar systems operating continuously over long periods are often affected by equipment failures, external interference, and other factors, which may lead to abnormal echoes and limit their effectiveness in monitoring, warning, and forecasting severe weather.In this study, two types of abnormal echoes, radial and annular echoes, were manually selected and labeled from historical radar mosaic data. A weather radar abnormal echo dataset for radar mosaics, WRAED, containing 20 000 images, was then constructed using data augmentation techniques.An improved model was developed based on the semantic segmentation model DeepLabV3+. A simplified ResNet50 architecture was adopted as the backbone network to reduce computational cost. A simple attention mechanism, SimAM, was added to the backbone feature extraction layers to help the model focus on important echo features. In addition, an intermediate layer was introduced into the decoder to incorporate more detailed and contextual background information.The improved model, named DeepLab-ARER, was designed for abnormal echo recognition in radar mosaics. Experimental results show that DeepLab-ARER achieves good performance in identifying abnormal echoes, with a mean pixel accuracy of 96.75% and a mean intersection over union of 93.95%, representing a clear improvement over DeepLabV3+.The DeepLab-ARER model provides effective technical support for the automatic identification of abnormal echoes in radar mosaic.
Weather radar systems operating continuously over long periods are often affected by equipment failures, external interference, and other factors, which may lead to abnormal echoes and limit their effectiveness in monitoring, warning, and forecasting severe weather.In this study, two types of abnormal echoes, radial and annular echoes, were manually selected and labeled from historical radar mosaic data. A weather radar abnormal echo dataset for radar mosaics, WRAED, containing 20 000 images, was then constructed using data augmentation techniques.An improved model was developed based on the semantic segmentation model DeepLabV3+. A simplified ResNet50 architecture was adopted as the backbone network to reduce computational cost. A simple attention mechanism, SimAM, was added to the backbone feature extraction layers to help the model focus on important echo features. In addition, an intermediate layer was introduced into the decoder to incorporate more detailed and contextual background information.The improved model, named DeepLab-ARER, was designed for abnormal echo recognition in radar mosaics. Experimental results show that DeepLab-ARER achieves good performance in identifying abnormal echoes, with a mean pixel accuracy of 96.75% and a mean intersection over union of 93.95%, representing a clear improvement over DeepLabV3+.The DeepLab-ARER model provides effective technical support for the automatic identification of abnormal echoes in radar mosaic.
2026,52(9):1130-1141, DOI: 10.7519/j.issn.1000-0526.2026.030904
Abstract:
To address the issue that existing radar-based precipitation extrapolation techniques can not adequately characterize the evolution of thunderstorms and cause systematic biases in heavy precipitation forecasts, this paper proposes a heavy precipitation extrapolation and correction method which integrates the evolution features of thunderstorms. Based on the storm cell identification and tracking (SCIT) algorithm, a thunderstorm development trend discrimination model is constructed for different thunderstorms by combining key factors including strong gradient zones, newly generated cells, and echo area changes. By utilizing radar reflectivity factor, optical flow vectors and dynamic Z-I relationship, we implement evolutionary constraints and corrections during the extrapolating process of heavy precipitation. Verification on the precipitation forecasts in the main flood season of 2025 shows that in the 0-1 h forecasts, the TS scores of the corrected products at the level of ≥20 mm·h-1 and ≥50 mm·h-1 are both higher than those of the optical flow system, with the advantage increasing with precipitation intensity. In the 1-2 h forecasts, the TS scores at ≥20 mm·h-1 and ≥50 mm·h-1 are still significantly improved compared with those of the optical system, but the score at ≥50 mm·h-1 is only 0.004, indicating very limited operational guidance value. Further analysis of typical cases of heavy precipitation demonstrates that the correction technique can capture precipitation characteristics of convective systems rapidly moving and developing to a greater extent and effectively alleviate the problems of underestimating intensity and coverage that exist inherently in traditional extrapolation methods.
To address the issue that existing radar-based precipitation extrapolation techniques can not adequately characterize the evolution of thunderstorms and cause systematic biases in heavy precipitation forecasts, this paper proposes a heavy precipitation extrapolation and correction method which integrates the evolution features of thunderstorms. Based on the storm cell identification and tracking (SCIT) algorithm, a thunderstorm development trend discrimination model is constructed for different thunderstorms by combining key factors including strong gradient zones, newly generated cells, and echo area changes. By utilizing radar reflectivity factor, optical flow vectors and dynamic Z-I relationship, we implement evolutionary constraints and corrections during the extrapolating process of heavy precipitation. Verification on the precipitation forecasts in the main flood season of 2025 shows that in the 0-1 h forecasts, the TS scores of the corrected products at the level of ≥20 mm·h-1 and ≥50 mm·h-1 are both higher than those of the optical flow system, with the advantage increasing with precipitation intensity. In the 1-2 h forecasts, the TS scores at ≥20 mm·h-1 and ≥50 mm·h-1 are still significantly improved compared with those of the optical system, but the score at ≥50 mm·h-1 is only 0.004, indicating very limited operational guidance value. Further analysis of typical cases of heavy precipitation demonstrates that the correction technique can capture precipitation characteristics of convective systems rapidly moving and developing to a greater extent and effectively alleviate the problems of underestimating intensity and coverage that exist inherently in traditional extrapolation methods.
2026,52(9):1142-1152, DOI: 10.7519/j.issn.1000-0526.2026.062601
Abstract:
The accuracy of typhoon location and intensity estimation, as well as the accuracy of typhoon track and intensity forecasts, is verified for the 26 named typhoons over the Northwest Pacific and the South China Sea in 2024. The forecast methods that are verified include subjective forecast methods, numerical weather prediction (NWP) models, statistical forecasting methods, dynamical-statistical forecasting methods, multi-model ensemble forecasting methods, and AI-based weather prediction (AIWP) models. The results show that the overall mean error of typhoon location estimation by the five official typhoon forecasting agencies was 22.3 km, and the overall mean absolute error of typhoon intensity estimation was 2.5 m·s-1. The performance of 48 h and 72 h typhoon track forecasts by the National Meteorological Centre (NMC) of China Meteorological Administration reached the highest level in records. Among global NWP models, ECMWF-IFS exhibited the best performance in typhoon track forecasts. The AIWP model “Fengqing” achieved track forecast performance comparable to ECMWF-IFS within 96 h lead time, and slightly outperformed ECMWF-IFS with 60 h to 96 h in advance. The intensity forecast performance of NWP models and dynamical-statistical forecasting methods was superior to that of ensemble prediction systems and AIWP models. Specifically, AIWP models systematically underestimated the typhoon intensity.
The accuracy of typhoon location and intensity estimation, as well as the accuracy of typhoon track and intensity forecasts, is verified for the 26 named typhoons over the Northwest Pacific and the South China Sea in 2024. The forecast methods that are verified include subjective forecast methods, numerical weather prediction (NWP) models, statistical forecasting methods, dynamical-statistical forecasting methods, multi-model ensemble forecasting methods, and AI-based weather prediction (AIWP) models. The results show that the overall mean error of typhoon location estimation by the five official typhoon forecasting agencies was 22.3 km, and the overall mean absolute error of typhoon intensity estimation was 2.5 m·s-1. The performance of 48 h and 72 h typhoon track forecasts by the National Meteorological Centre (NMC) of China Meteorological Administration reached the highest level in records. Among global NWP models, ECMWF-IFS exhibited the best performance in typhoon track forecasts. The AIWP model “Fengqing” achieved track forecast performance comparable to ECMWF-IFS within 96 h lead time, and slightly outperformed ECMWF-IFS with 60 h to 96 h in advance. The intensity forecast performance of NWP models and dynamical-statistical forecasting methods was superior to that of ensemble prediction systems and AIWP models. Specifically, AIWP models systematically underestimated the typhoon intensity.
2026,52(9):1153-1164, DOI: 10.7519/j.issn.1000-0526.2026.081201
Abstract:
In June 2026, the polar vortex in the Northern Hemisphere exhibited a dipole distribution with the primary vortex located over the Arctic Ocean near the pole, showing stronger-than-normal intensity. The Eurasian mid-high latitudes featured a two-trough and one-ridge circulation pattern, and the average geopotential height ridge from West Siberia to northern Xinjiang was abnormally strong. The western Pacific subtropical high was zonally distributed, with greater intensity and a more westward and northward position than normal. The national average temperature was 20.7℃, 0.3℃ higher than the normal value. The national average precipitation was 99 mm, 3.7% below the climatological average. However, cold vortices behaved actively in North China and Northeast China resulting in frequent convective activities. In southern China, heavy precipitation processes occurred frequently, with highly overlapping affected-areas and locally extreme precipitation. During this month, there were five heavy precipitation events and six severe convection events. Regions such as the middle and lower reaches of the Yangtze River, South China, and eastern Southwest China experienced regional heavy precipitation and local extreme precipitation. Thunderstorm, gale and hail events occurred more in Northeast China, North China and the Huanghuai Region. Two typhoons were generated over the western North Pacific and the South China Sea, but neither made landfall in China. In addition, this paper briefly analyzes the evolution of circulation patterns, causes and forecasting difficulties of the extreme precipitation in South China from 14 to 18 June and the first spell of heavy precipitation in the Meiyu season over the middle and lower reaches of the Yangtze River from 19 to 22 June.
In June 2026, the polar vortex in the Northern Hemisphere exhibited a dipole distribution with the primary vortex located over the Arctic Ocean near the pole, showing stronger-than-normal intensity. The Eurasian mid-high latitudes featured a two-trough and one-ridge circulation pattern, and the average geopotential height ridge from West Siberia to northern Xinjiang was abnormally strong. The western Pacific subtropical high was zonally distributed, with greater intensity and a more westward and northward position than normal. The national average temperature was 20.7℃, 0.3℃ higher than the normal value. The national average precipitation was 99 mm, 3.7% below the climatological average. However, cold vortices behaved actively in North China and Northeast China resulting in frequent convective activities. In southern China, heavy precipitation processes occurred frequently, with highly overlapping affected-areas and locally extreme precipitation. During this month, there were five heavy precipitation events and six severe convection events. Regions such as the middle and lower reaches of the Yangtze River, South China, and eastern Southwest China experienced regional heavy precipitation and local extreme precipitation. Thunderstorm, gale and hail events occurred more in Northeast China, North China and the Huanghuai Region. Two typhoons were generated over the western North Pacific and the South China Sea, but neither made landfall in China. In addition, this paper briefly analyzes the evolution of circulation patterns, causes and forecasting difficulties of the extreme precipitation in South China from 14 to 18 June and the first spell of heavy precipitation in the Meiyu season over the middle and lower reaches of the Yangtze River from 19 to 22 June.
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Available online: October 03, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.092201
Abstract:
In view of the limitation that the traditional historical frequency matching method (Hist-FMM) cannot effectively correct the positional deviation of precipitation areas, an improved frequency matching correction scheme based on low-level circulation similarity (Sim-FMM) is proposed, which uses wind direction and speed at 925 hPa as dynamic constraints. By analyzing the coupling relationship between precipitation and the wind field at 925 hPa, together with sensitivity experiments, the optimal parameter scheme for Sim-FMM was established. For general precipitation correction, a wide tolerance window (30° for wind direction and 4 m·s?1 for wind speed) without seasonal constraints is adopted to ensure sample representativeness. For heavy rainfall correction, a narrower tolerance window (10° for wind direction and 1 m·s?1 for wind speed) combined with seasonal constraints is applied to balance circulation similarity and climatological characteristics. Based on surface observations and ECMWF forecast data from December 2023 to May 2026, the correction performance of Sim-FMM was evaluated. The results show that Sim-FMM outperforms Hist-FMM in precipitation correction. For winter rainfall events, Sim-FMM effectively reduces widespread false alarms by matching analogous circulation samples, increasing the rain/no-rain accuracy by 1.9% relative to Hist-FMM. For local heavy rainfall events in spring and summer, Sim-FMM alleviates the systematic underestimation of rainstorm magnitude, improving the rainstorm threat score (TS) by 8.4% relative to Hist-FMM during 2024-2026. Nevertheless, Sim-FMM performs slightly worse for autumn heavy rainfall, which may be attributed to the highly concentrated seasonal features of autumn rainfall over Hainan Island and the inability of the regional mean wind field at a single time to fully depict the heterogeneous structures of complex systems such as typhoons and shear lines.
In view of the limitation that the traditional historical frequency matching method (Hist-FMM) cannot effectively correct the positional deviation of precipitation areas, an improved frequency matching correction scheme based on low-level circulation similarity (Sim-FMM) is proposed, which uses wind direction and speed at 925 hPa as dynamic constraints. By analyzing the coupling relationship between precipitation and the wind field at 925 hPa, together with sensitivity experiments, the optimal parameter scheme for Sim-FMM was established. For general precipitation correction, a wide tolerance window (30° for wind direction and 4 m·s?1 for wind speed) without seasonal constraints is adopted to ensure sample representativeness. For heavy rainfall correction, a narrower tolerance window (10° for wind direction and 1 m·s?1 for wind speed) combined with seasonal constraints is applied to balance circulation similarity and climatological characteristics. Based on surface observations and ECMWF forecast data from December 2023 to May 2026, the correction performance of Sim-FMM was evaluated. The results show that Sim-FMM outperforms Hist-FMM in precipitation correction. For winter rainfall events, Sim-FMM effectively reduces widespread false alarms by matching analogous circulation samples, increasing the rain/no-rain accuracy by 1.9% relative to Hist-FMM. For local heavy rainfall events in spring and summer, Sim-FMM alleviates the systematic underestimation of rainstorm magnitude, improving the rainstorm threat score (TS) by 8.4% relative to Hist-FMM during 2024-2026. Nevertheless, Sim-FMM performs slightly worse for autumn heavy rainfall, which may be attributed to the highly concentrated seasonal features of autumn rainfall over Hainan Island and the inability of the regional mean wind field at a single time to fully depict the heterogeneous structures of complex systems such as typhoons and shear lines.
Available online: September 30, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.073102
Abstract:
Based on the Local Climate Zone (LCZ) framework, this study systematically analyzes the characteristics of temperature differences across various underlying surface types and their response mechanisms to meteorological conditions. Using hourly temperature, wind speed, and precipitation data (2010–2021) from 37 meteorological stations within the Fifth Ring Road of Beijing, combined with field surveys and satellite imagery, the stations were classified into five LCZ types. The results indicate that the main urban area exhibits a temperature gradient dominated by building density. The compact high-rise zone (LCZ 1) serves as a stable heat center, with average temperatures significantly higher than those of vegetated zones. Thermal differences among LCZ types follow a seasonal pattern of intensifying in autumn and winter while weakening in spring and summer, with the most pronounced disparities occurring during winter nights. Furthermore, precipitation significantly reduces thermal differences between LCZs through evaporative cooling, while simultaneously highlighting the dominant role of surface properties. Finally, weak wind conditions favor the maintenance of thermal gradients, whereas strong wind environments promote the homogenization of the temperature field.
Based on the Local Climate Zone (LCZ) framework, this study systematically analyzes the characteristics of temperature differences across various underlying surface types and their response mechanisms to meteorological conditions. Using hourly temperature, wind speed, and precipitation data (2010–2021) from 37 meteorological stations within the Fifth Ring Road of Beijing, combined with field surveys and satellite imagery, the stations were classified into five LCZ types. The results indicate that the main urban area exhibits a temperature gradient dominated by building density. The compact high-rise zone (LCZ 1) serves as a stable heat center, with average temperatures significantly higher than those of vegetated zones. Thermal differences among LCZ types follow a seasonal pattern of intensifying in autumn and winter while weakening in spring and summer, with the most pronounced disparities occurring during winter nights. Furthermore, precipitation significantly reduces thermal differences between LCZs through evaporative cooling, while simultaneously highlighting the dominant role of surface properties. Finally, weak wind conditions favor the maintenance of thermal gradients, whereas strong wind environments promote the homogenization of the temperature field.
Available online: September 29, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.092801
Abstract:
Based on observation data from 76 national meteorological stations in Hubei Province and ERA5 reanalysis data during 2000-2024, 108 rain-snow events are classified by means of the T-mode principal component analysis (TPCA). The temporal distributions, circulation configurations, and impact characteristics of the rain-snow events under different circulation types are then analyzed. The results show that over the 25 years (2000—2024), Hubei Province experienced 4.3 rain-snow events on average annually, with a clear inter-annual variability. These events occurred mainly in January and February, accounting for 74% of the total. Based on the circulation patterns at 500 hPa, these rain-snow events are classified into three types: the Lake Baikal High-Ridge Type (48.1%), Lake Baikal Low-Trough Type (30.6%), and Lake Baikal Northwesterly Flow Type (21.3%). In the composite mean fields of the three circulation types, the Lake Baikal High-Ridge Type features the deepest southern-branch trough at 500 hPa and the strongest influence of the surface cold high. The Lake Baikal Low-Trough Type is characterized by the strongest southwesterly jet at 700 hPa, with a maximum wind speed of 20 m·s?1, and the most favorable dynamical conditions. The Lake Baikal Northwesterly Flow Type has the weakest southern-branch trough. The three circulation types of rain-snow events produces distinct impacts in Hubei Province. The Lake Baikal Low-Trough Type has a higher daily average maximum precipitation (15.34 mm) and deeper average maximum snow depth (9.33 cm) than the other two types and is more prone to produce deep snow cover. The Lake Baikal High-Ridge Type has the largest average value of total precipitation (19.63 mm), the highest mean numbers of snow-covered and wire-icing stations, and the greatest wire-icing intensity with a mean maximum wire-icing diameter of 3.83 mm. It also has the longest duration of rain-snow event, lasting for 2.22 d on average. The Lake Baikal Northwesterly Flow Type generally generates smaller precipitation amount, the weakest snow accumulation and wire icing, and the shortest event duration. These findings could provide a reference for circulation-pattern-based forecasting of rain-snow events and disaster risk assessment in Hubei Province.
Based on observation data from 76 national meteorological stations in Hubei Province and ERA5 reanalysis data during 2000-2024, 108 rain-snow events are classified by means of the T-mode principal component analysis (TPCA). The temporal distributions, circulation configurations, and impact characteristics of the rain-snow events under different circulation types are then analyzed. The results show that over the 25 years (2000—2024), Hubei Province experienced 4.3 rain-snow events on average annually, with a clear inter-annual variability. These events occurred mainly in January and February, accounting for 74% of the total. Based on the circulation patterns at 500 hPa, these rain-snow events are classified into three types: the Lake Baikal High-Ridge Type (48.1%), Lake Baikal Low-Trough Type (30.6%), and Lake Baikal Northwesterly Flow Type (21.3%). In the composite mean fields of the three circulation types, the Lake Baikal High-Ridge Type features the deepest southern-branch trough at 500 hPa and the strongest influence of the surface cold high. The Lake Baikal Low-Trough Type is characterized by the strongest southwesterly jet at 700 hPa, with a maximum wind speed of 20 m·s?1, and the most favorable dynamical conditions. The Lake Baikal Northwesterly Flow Type has the weakest southern-branch trough. The three circulation types of rain-snow events produces distinct impacts in Hubei Province. The Lake Baikal Low-Trough Type has a higher daily average maximum precipitation (15.34 mm) and deeper average maximum snow depth (9.33 cm) than the other two types and is more prone to produce deep snow cover. The Lake Baikal High-Ridge Type has the largest average value of total precipitation (19.63 mm), the highest mean numbers of snow-covered and wire-icing stations, and the greatest wire-icing intensity with a mean maximum wire-icing diameter of 3.83 mm. It also has the longest duration of rain-snow event, lasting for 2.22 d on average. The Lake Baikal Northwesterly Flow Type generally generates smaller precipitation amount, the weakest snow accumulation and wire icing, and the shortest event duration. These findings could provide a reference for circulation-pattern-based forecasting of rain-snow events and disaster risk assessment in Hubei Province.
Available online: September 23, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.081601
Abstract:
Since 2024, the operational operation of most new-generation weather radars in China has adopted three observation modes: precipitation mode 1 (VCP11D), precipitation mode 2 (VCP21D), and clear-air mode (VCP31D). However, a systematic evaluation of their specific data quality differences and the scientific validity of the automatic switching strategy is still lacking. This study uses 11,965 sets of observation data from 117 dual-polarization operational radars nationwide, collected before and after mode switching from April to July 2024, to compare and analyze the performance of the three observation modes in terms of ground clutter suppression capability, sensitivity, and measurement accuracy. Furthermore, by statistically calculating the proportion of effective precipitation echoes in newly added/missing elevation layers before and after mode switching, the study quantitatively evaluates the rationality of the current automatic switching strategy, which is based on thresholds of composite reflectivity (CR), echo top (ET), and vertically integrated liquid water content (VIL). The results show that: (1) In terms of data quality, the three modes exhibit comparable ground clutter suppression capabilities, but the clear-air mode significantly increases the area of ground clutter identification due to its longer pulse width. The clear-air mode has the highest sensitivity, followed by the precipitation modes; however, differences in radar models and refined modifications can reduce sensitivity. The number of pulse accumulations in different observation modes primarily affects the measurement accuracy of differential reflectivity (ZDR) and correlation coefficient (ρhv); fewer accumulations lead to greater dispersion of these two parameters. (2) Regarding switching strategies, the current fixed-threshold strategy fails to effectively capture the vertical structural changes in precipitation, resulting in low utilization of elevation resources. Taking the CINRAD/SAD radar as an example, the switching threshold between the two precipitation modes is set too low, resulting in a median effective precipitation echo proportion of only 27.13% in the newly added elevation layers of VCP11 (5.2°, 7.5°, 8.7°, 12°, and 16.7°), thus failing to fully leverage the advantages of vertical dense observations. Conversely, the threshold for switching from precipitation to clear-air mode is set too high, resulting in a median of 15.66% of precipitation echoes remaining in the missing elevation layers (6.0°, 9.9°, 14.6°, 19.5°) of VCP31 after switching. (3) To address the above issues, considering the limited computational capacity of the Radar Data Acquisition (RDA) subsystem, this paper proposes a simple switching indicator based on the spatial proportion of echoes in common elevation layers. Comparative verification shows that this indicator significantly improves the accuracy of mode switching and the utilization of elevation resources. This study provides a scientific basis for optimizing the observation strategies of China’s new-generation weather radars and enhancing the capability of severe weather monitoring and early warning.
Since 2024, the operational operation of most new-generation weather radars in China has adopted three observation modes: precipitation mode 1 (VCP11D), precipitation mode 2 (VCP21D), and clear-air mode (VCP31D). However, a systematic evaluation of their specific data quality differences and the scientific validity of the automatic switching strategy is still lacking. This study uses 11,965 sets of observation data from 117 dual-polarization operational radars nationwide, collected before and after mode switching from April to July 2024, to compare and analyze the performance of the three observation modes in terms of ground clutter suppression capability, sensitivity, and measurement accuracy. Furthermore, by statistically calculating the proportion of effective precipitation echoes in newly added/missing elevation layers before and after mode switching, the study quantitatively evaluates the rationality of the current automatic switching strategy, which is based on thresholds of composite reflectivity (CR), echo top (ET), and vertically integrated liquid water content (VIL). The results show that: (1) In terms of data quality, the three modes exhibit comparable ground clutter suppression capabilities, but the clear-air mode significantly increases the area of ground clutter identification due to its longer pulse width. The clear-air mode has the highest sensitivity, followed by the precipitation modes; however, differences in radar models and refined modifications can reduce sensitivity. The number of pulse accumulations in different observation modes primarily affects the measurement accuracy of differential reflectivity (ZDR) and correlation coefficient (ρhv); fewer accumulations lead to greater dispersion of these two parameters. (2) Regarding switching strategies, the current fixed-threshold strategy fails to effectively capture the vertical structural changes in precipitation, resulting in low utilization of elevation resources. Taking the CINRAD/SAD radar as an example, the switching threshold between the two precipitation modes is set too low, resulting in a median effective precipitation echo proportion of only 27.13% in the newly added elevation layers of VCP11 (5.2°, 7.5°, 8.7°, 12°, and 16.7°), thus failing to fully leverage the advantages of vertical dense observations. Conversely, the threshold for switching from precipitation to clear-air mode is set too high, resulting in a median of 15.66% of precipitation echoes remaining in the missing elevation layers (6.0°, 9.9°, 14.6°, 19.5°) of VCP31 after switching. (3) To address the above issues, considering the limited computational capacity of the Radar Data Acquisition (RDA) subsystem, this paper proposes a simple switching indicator based on the spatial proportion of echoes in common elevation layers. Comparative verification shows that this indicator significantly improves the accuracy of mode switching and the utilization of elevation resources. This study provides a scientific basis for optimizing the observation strategies of China’s new-generation weather radars and enhancing the capability of severe weather monitoring and early warning.
Available online: September 23, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.091601
Abstract:
This study investigates the decadal modulation of ENSO’s impact on intraseasonal summer precipitation over eastern China. During 1991–2024, early summer (June) following an El Ni?o event tends to see increased precipitation over the middle and lower reaches of the Yangtze River and regions to the south, whereas late summer (August) experiences a decrease in these same regions. This inverse-phase relationship underwent a significant decadal shift around the early 2000s. A comparison between the periods 1991–2003 and 2004–2024 reveals that in the latter period, the relationship between ENSO and precipitation in August of the following year in the middle and lower reaches of the Yangtze River and regions to the south weakened, making it less likely for widespread negative precipitation anomalies to occur in this region. By examining the concurrent changes in sea surface temperature (SST) and atmospheric circulation associated with ENSO events, this study finds that over the western North Pacific in August 2004–2024, weakened easterly anomalies lead to reduced latent heat flux from evaporation, resulting in a shift from negative to positive SST anomalies. Meanwhile, positive SST anomalies over the northern Indian Ocean are comparatively weaker. Consequently, the anomalous anticyclone over the western North Pacific is unable to persist and transitions into a cyclonic circulation, which is detrimental to precipitation deficits over the middle and lower reaches of the Yangtze River and adjacent southern regions. In terms of circulation configuration, at the 500 hPa level, the East Asia–Pacific/Pacific–Japan teleconnection pattern shifts from a “+-” to a “-+” pattern in August 2004–2024; at the 200 hPa level, the East Asian subtropical westerly jet weakens. Both changes are unfavorable for precipitation deficits over the middle and lower reaches of the Yangtze River and southern regions. This research clarifies the decadal modulation of ENSO’s influence on intraseasonal summer precipitation in eastern China, providing a theoretical basis for improving intraseasonal precipitation prediction.
This study investigates the decadal modulation of ENSO’s impact on intraseasonal summer precipitation over eastern China. During 1991–2024, early summer (June) following an El Ni?o event tends to see increased precipitation over the middle and lower reaches of the Yangtze River and regions to the south, whereas late summer (August) experiences a decrease in these same regions. This inverse-phase relationship underwent a significant decadal shift around the early 2000s. A comparison between the periods 1991–2003 and 2004–2024 reveals that in the latter period, the relationship between ENSO and precipitation in August of the following year in the middle and lower reaches of the Yangtze River and regions to the south weakened, making it less likely for widespread negative precipitation anomalies to occur in this region. By examining the concurrent changes in sea surface temperature (SST) and atmospheric circulation associated with ENSO events, this study finds that over the western North Pacific in August 2004–2024, weakened easterly anomalies lead to reduced latent heat flux from evaporation, resulting in a shift from negative to positive SST anomalies. Meanwhile, positive SST anomalies over the northern Indian Ocean are comparatively weaker. Consequently, the anomalous anticyclone over the western North Pacific is unable to persist and transitions into a cyclonic circulation, which is detrimental to precipitation deficits over the middle and lower reaches of the Yangtze River and adjacent southern regions. In terms of circulation configuration, at the 500 hPa level, the East Asia–Pacific/Pacific–Japan teleconnection pattern shifts from a “+-” to a “-+” pattern in August 2004–2024; at the 200 hPa level, the East Asian subtropical westerly jet weakens. Both changes are unfavorable for precipitation deficits over the middle and lower reaches of the Yangtze River and southern regions. This research clarifies the decadal modulation of ENSO’s influence on intraseasonal summer precipitation in eastern China, providing a theoretical basis for improving intraseasonal precipitation prediction.
Available online: September 18, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.081402
Abstract:
Aiming at the problems that the existing evaluation methods of the Shenyang vertical observation system are imperfect and mostly rely on single-equipment data, and the insufficient exploration of the synergistic application value of multi-source vertical observation data in the monitoring and early warning of severe convective weather under cold vortex conditions. This study intends to establish a multi-element data quality evaluation system suitable for the cold vortex circulation background. Meanwhile, it explores the monitoring performance of multi-device collaborative observations during cold-vortex severe convective rainstorms, so as to further enhance the regional three-dimensional vertical monitoring and nowcasting capabilities for cold vortex weather. Based on the observation data from the Shenyang Ground-based Remote Sensing Vertical Observation Comprehensive Station, this paper takes the extremely heavy regional rainstorm process in Liaoning from July 23 to 28, 2024 as an example, combines radiosonde observation data, establishes a multi-element data quality evaluation system for vertical observation suitable for the cold vortex background, and carries out systematic evaluation and application analysis. The results show that: (1) The operational availability of all five types of equipment in the vertical observation system reaches 100%, and the data quality meets the requirements of operational application and analysis; (2) Multi-element fusion analysis reveals that affected by the Northeast cold vortex, water vapor transport from the periphery of Typhoon Gaemi, and the subtropical high, the rapid accumulation of water vapor and the vigorous vertical development of cloud systems, combined with vertical temperature difference, low-level convergence and upward movement, as well as mid-level jet momentum transport, jointly trigger the severe convection process; (3) In terms of forecast and early warning, the wind profile radar can indicate changes in dynamic conditions 6-12 hours in advance; the microwave radiometer combined with GNSS/MET observation can capture water vapor and precursor signals 1.5 hours in advance; the millimeter-wave cloud radar can monitor the imminent development characteristics of convection 30-60 minutes in advance. The study shows that the established multi-element quality evaluation system can effectively support the data quality evaluation and application analysis of the vertical observation system under the cold vortex background, and the multi-equipment collaborative observation shows significant advantages in the identification of dynamic triggers, tracking of water vapor evolution, and imminent early warning of severe convective weather, providing important technical support for the refined monitoring and short-term forecasting of severe convective weather in Liaoning.
Aiming at the problems that the existing evaluation methods of the Shenyang vertical observation system are imperfect and mostly rely on single-equipment data, and the insufficient exploration of the synergistic application value of multi-source vertical observation data in the monitoring and early warning of severe convective weather under cold vortex conditions. This study intends to establish a multi-element data quality evaluation system suitable for the cold vortex circulation background. Meanwhile, it explores the monitoring performance of multi-device collaborative observations during cold-vortex severe convective rainstorms, so as to further enhance the regional three-dimensional vertical monitoring and nowcasting capabilities for cold vortex weather. Based on the observation data from the Shenyang Ground-based Remote Sensing Vertical Observation Comprehensive Station, this paper takes the extremely heavy regional rainstorm process in Liaoning from July 23 to 28, 2024 as an example, combines radiosonde observation data, establishes a multi-element data quality evaluation system for vertical observation suitable for the cold vortex background, and carries out systematic evaluation and application analysis. The results show that: (1) The operational availability of all five types of equipment in the vertical observation system reaches 100%, and the data quality meets the requirements of operational application and analysis; (2) Multi-element fusion analysis reveals that affected by the Northeast cold vortex, water vapor transport from the periphery of Typhoon Gaemi, and the subtropical high, the rapid accumulation of water vapor and the vigorous vertical development of cloud systems, combined with vertical temperature difference, low-level convergence and upward movement, as well as mid-level jet momentum transport, jointly trigger the severe convection process; (3) In terms of forecast and early warning, the wind profile radar can indicate changes in dynamic conditions 6-12 hours in advance; the microwave radiometer combined with GNSS/MET observation can capture water vapor and precursor signals 1.5 hours in advance; the millimeter-wave cloud radar can monitor the imminent development characteristics of convection 30-60 minutes in advance. The study shows that the established multi-element quality evaluation system can effectively support the data quality evaluation and application analysis of the vertical observation system under the cold vortex background, and the multi-equipment collaborative observation shows significant advantages in the identification of dynamic triggers, tracking of water vapor evolution, and imminent early warning of severe convective weather, providing important technical support for the refined monitoring and short-term forecasting of severe convective weather in Liaoning.
Evaluation and Verification of 2m Temperature Reanalysis Products over China during Summer 2021-2022
Available online: September 14, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.073101
Abstract:
Reanalysis products hold significant application value in extreme weather risk assessment, renewable energy planning, climate change research, and artificial intelligence. Systematic evaluation of the accuracy and applicability of 2m temperature reanalysis products is a prerequisite for their reliable utilization in scientific investigations. This study employs hourly 2m temperature observations from Chinese automatic weather stations (2021–2022) to evaluate three reanalysis products—CMA Regional Re-Analysis (CMA-RRA), ECMWF Reanalysis v5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications, v2 (MERRA2). Results indicate that: Summer high-temperatures are concentrated in the Yangtze-Huai River Basin (June) and South China and Sichuan Basin regions (July–August),with 2022 high-temperature intensity surpassing 2021 levels. CMA-RRA exhibiting the smallest RMSE (<2°C) and highest correlation coefficient (R≥0.9). For Sichuan Basin extreme high-temperatures, CMA-RRA effectively captures diurnal variations and spatial patterns, achieving the highest Critical Success Index. While ERA5 shows premature heat onset timing and spatial overestimation, and MERRA2 systematically underestimates both intensity and duration of high-temperature events.
Reanalysis products hold significant application value in extreme weather risk assessment, renewable energy planning, climate change research, and artificial intelligence. Systematic evaluation of the accuracy and applicability of 2m temperature reanalysis products is a prerequisite for their reliable utilization in scientific investigations. This study employs hourly 2m temperature observations from Chinese automatic weather stations (2021–2022) to evaluate three reanalysis products—CMA Regional Re-Analysis (CMA-RRA), ECMWF Reanalysis v5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications, v2 (MERRA2). Results indicate that: Summer high-temperatures are concentrated in the Yangtze-Huai River Basin (June) and South China and Sichuan Basin regions (July–August),with 2022 high-temperature intensity surpassing 2021 levels. CMA-RRA exhibiting the smallest RMSE (<2°C) and highest correlation coefficient (R≥0.9). For Sichuan Basin extreme high-temperatures, CMA-RRA effectively captures diurnal variations and spatial patterns, achieving the highest Critical Success Index. While ERA5 shows premature heat onset timing and spatial overestimation, and MERRA2 systematically underestimates both intensity and duration of high-temperature events.
Available online: September 11, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.040203
Abstract:
Based on daily precipitation data from 126 national meteorological stations in Yunnan Province from June 2023 to June 2025, this study evaluated the applicability of Fengyun-4B (FY-4B) quantitative precipitation estimation (QPE) daily precipitation products over Yunnan Province using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI), and compared FY-4B QPE with Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) and climate hazards group infrared precipitation with station data (CHIRPS) products. The results show that the precipitation detection capability of FY-4B QPE is superior to that of GPM IMERG and CHIRPS, with the highest CSI (0.44) and the lowest FAR (0.44). However, it exhibits a certain degree of precipitation underestimation (RB=?0.15). In comparison, GPM IMERG has the highest FAR (0.53), and CHIRPS shows a relatively low POD (0.41). FY-4B QPE exhibits a pattern of higher precipitation over the southwest and lower precipitation over the east of Yunnan Province. The precipitation detection capability in southwestern and southern Yunnan is superior to that in the central and eastern regions. The southwestern and southern areas, covering approximately 31% of the province, have an average CSI that is 0.14 higher than that of the central and eastern regions. The 500 to 1000 m elevation range is the optimal application area for FY-4B QPE. FY-4B QPE performs best under light rain, with pronounced underestimation under heavy precipitation. FY-4B QPE is sensitive to seasonal variations, with strong detection capability but a tendency toward underestimation in summer, while overall accuracy decreases somewhat in winter. The evaluation results can provide a scientific reference for the research and operational applications of FY-4B QPE for precipitation monitoring in complex terrain areas of Yunnan Province.
Based on daily precipitation data from 126 national meteorological stations in Yunnan Province from June 2023 to June 2025, this study evaluated the applicability of Fengyun-4B (FY-4B) quantitative precipitation estimation (QPE) daily precipitation products over Yunnan Province using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI), and compared FY-4B QPE with Global Precipitation Measurement Integrated Multi-Satellite Retrievals (GPM IMERG) and climate hazards group infrared precipitation with station data (CHIRPS) products. The results show that the precipitation detection capability of FY-4B QPE is superior to that of GPM IMERG and CHIRPS, with the highest CSI (0.44) and the lowest FAR (0.44). However, it exhibits a certain degree of precipitation underestimation (RB=?0.15). In comparison, GPM IMERG has the highest FAR (0.53), and CHIRPS shows a relatively low POD (0.41). FY-4B QPE exhibits a pattern of higher precipitation over the southwest and lower precipitation over the east of Yunnan Province. The precipitation detection capability in southwestern and southern Yunnan is superior to that in the central and eastern regions. The southwestern and southern areas, covering approximately 31% of the province, have an average CSI that is 0.14 higher than that of the central and eastern regions. The 500 to 1000 m elevation range is the optimal application area for FY-4B QPE. FY-4B QPE performs best under light rain, with pronounced underestimation under heavy precipitation. FY-4B QPE is sensitive to seasonal variations, with strong detection capability but a tendency toward underestimation in summer, while overall accuracy decreases somewhat in winter. The evaluation results can provide a scientific reference for the research and operational applications of FY-4B QPE for precipitation monitoring in complex terrain areas of Yunnan Province.
Available online: August 27, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.072001
Abstract:
Based on the C-band dual-polarization radar data from the Wutaishan Cloud Physics Experimental Base in Shanxi Province and the operational data of artificial rain enhancement, a fuzzy-logic hydrometeor classification algorithm was used to analyze the full-stage microphysical characteristics of a ground-based rocket rain enhancement operation during a mixed rain event on 22 May 2025. The results show that after the seeding of silver iodide (AgI) ice nuclei, ice crystal nuclei formed through heterogeneous nucleation, and the growth of ice-phase particles was promoted through the Bergeron process and collision-coalescence process. During the main impact stage of rain enhancement, ice crystals and wet snow particles showed an inverse relationship in number variation. Snow aggregate particles were mainly produced by the collisional growth of ice crystals, accounting for 38.9% of the total number and becoming the dominant ice-phase hydrometeors, while high-density graupel accounted for only 3.19%. Wet snow particles were mainly derived from aggregate particles above the 0degrees Celsius level, and their generation region was located at 3.6-4.1 km altitude. They were also an important source of the bright band near the 0degrees Celsius level. Based on the differences before and after AgI entered the cloud, a three-stage conceptual model of cloud microphysical evolution was established, including AgI ice nuclei seeded at the supercooled water accumulation zone, ice formation and aggregation growth, and mixed-phase transition near the 0degrees Celsius level with the formation of raindrop.
Based on the C-band dual-polarization radar data from the Wutaishan Cloud Physics Experimental Base in Shanxi Province and the operational data of artificial rain enhancement, a fuzzy-logic hydrometeor classification algorithm was used to analyze the full-stage microphysical characteristics of a ground-based rocket rain enhancement operation during a mixed rain event on 22 May 2025. The results show that after the seeding of silver iodide (AgI) ice nuclei, ice crystal nuclei formed through heterogeneous nucleation, and the growth of ice-phase particles was promoted through the Bergeron process and collision-coalescence process. During the main impact stage of rain enhancement, ice crystals and wet snow particles showed an inverse relationship in number variation. Snow aggregate particles were mainly produced by the collisional growth of ice crystals, accounting for 38.9% of the total number and becoming the dominant ice-phase hydrometeors, while high-density graupel accounted for only 3.19%. Wet snow particles were mainly derived from aggregate particles above the 0degrees Celsius level, and their generation region was located at 3.6-4.1 km altitude. They were also an important source of the bright band near the 0degrees Celsius level. Based on the differences before and after AgI entered the cloud, a three-stage conceptual model of cloud microphysical evolution was established, including AgI ice nuclei seeded at the supercooled water accumulation zone, ice formation and aggregation growth, and mixed-phase transition near the 0degrees Celsius level with the formation of raindrop.
Available online: August 25, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.061701
Abstract:
Based on the observation data from 126 national meteorological stations in Yunnan Province from 2016 to 2025 and and ERA5 reanalysis data for the same period, the spatial distribution characteristics of regional heavy fog in Yunnan were diagnosed and analyzed using Rotated Empirical Orthogonal Function (REOF), and fog zoning was carried out combined with topographic factors. Furthermore, machine learning methods such as random forest were employed to establish zonal heavy fog forecasting models, and the results were compared and verified with SCMOC and ECMWF numerical forecast products. The results show that: 1)Heavy fog in Yunnan Province exhibits four dominant spatial modes: an out-of-phase pattern between the southwest and the rest of the region, southeastern Yunnan, northeastern Yunnan, and northwestern Yunnan. Combined with topography, the province is finally divided into four fog zones: Zone 1 (western and northwestern Yunnan), Zone 2 (northeastern and central Yunnan), Zone 3 (southwestern and southern Yunnan), and Zone 4 (southeastern Yunnan). 2)The random forest model achieves an accuracy of 0.82~0.84 for modeling samples and 0.80~0.83 for test samples in each zone, which is significantly superior to decision tree, K-nearest neighbor, logistic regression, and Bayesian methods. 3)There are regional differences in the importance of forecast factors in each fog zone. High-altitude areas (Zone 1 and Zone 2) are strongly dependent on water vapor conditions at 600—700 hPa. Regions significantly affected by eastern industrial activities (Zone 2 and Zone 4) are largely contributed by initial visibility. In relatively low-altitude areas (Zone 3 and Zone 4), the importance of sea level pressure and temperature factors within the boundary layer is more prominent. 4)The comprehensive performance of the short-range heavy fog forecast model established based on random forest is superior to those of two mainstream forecast products, SCMOC and ECMWF. The improvement in forecast effect is more significant in autumn and winter, and Regions 3 and 4 exhibit higher forecast accuracy.. For a regional heavy fog event that lasted two consecutive days, the TS scores of the random forest model are 0.38 and 0.41, which are significantly higher than those of SCMOC (both < 0.1) and ECMWF (0.26 and 0.11, respectively).
Based on the observation data from 126 national meteorological stations in Yunnan Province from 2016 to 2025 and and ERA5 reanalysis data for the same period, the spatial distribution characteristics of regional heavy fog in Yunnan were diagnosed and analyzed using Rotated Empirical Orthogonal Function (REOF), and fog zoning was carried out combined with topographic factors. Furthermore, machine learning methods such as random forest were employed to establish zonal heavy fog forecasting models, and the results were compared and verified with SCMOC and ECMWF numerical forecast products. The results show that: 1)Heavy fog in Yunnan Province exhibits four dominant spatial modes: an out-of-phase pattern between the southwest and the rest of the region, southeastern Yunnan, northeastern Yunnan, and northwestern Yunnan. Combined with topography, the province is finally divided into four fog zones: Zone 1 (western and northwestern Yunnan), Zone 2 (northeastern and central Yunnan), Zone 3 (southwestern and southern Yunnan), and Zone 4 (southeastern Yunnan). 2)The random forest model achieves an accuracy of 0.82~0.84 for modeling samples and 0.80~0.83 for test samples in each zone, which is significantly superior to decision tree, K-nearest neighbor, logistic regression, and Bayesian methods. 3)There are regional differences in the importance of forecast factors in each fog zone. High-altitude areas (Zone 1 and Zone 2) are strongly dependent on water vapor conditions at 600—700 hPa. Regions significantly affected by eastern industrial activities (Zone 2 and Zone 4) are largely contributed by initial visibility. In relatively low-altitude areas (Zone 3 and Zone 4), the importance of sea level pressure and temperature factors within the boundary layer is more prominent. 4)The comprehensive performance of the short-range heavy fog forecast model established based on random forest is superior to those of two mainstream forecast products, SCMOC and ECMWF. The improvement in forecast effect is more significant in autumn and winter, and Regions 3 and 4 exhibit higher forecast accuracy.. For a regional heavy fog event that lasted two consecutive days, the TS scores of the random forest model are 0.38 and 0.41, which are significantly higher than those of SCMOC (both < 0.1) and ECMWF (0.26 and 0.11, respectively).
Available online: August 24, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.081801
Abstract:
IN the spring of 2026 (March to May), the temperature in most part of China was higher than normal, with a national mean temperature of 12.0°C, ranking as the fourth warmest since 1961. The national average precipitation was 166.3 mm, 15.8% above the normal. Spatially, precipitation in central and eastern China presented two main rainfall belts in the North and South: The northern rainfall belt is mainly located from the southern part of North China to the Huanghuai region, while the southern rainfall belt is located from the central-eastern part of Southwest China to the western part of Jiangnan. At 500hPa geopotential height field over the middle and high latitudes of Eurasia exhibited a “two ridges and one trough” pattern. Positive height anomalies prevail over the region from the Ural Mountains to Lake Balkhash and over Northeast Asia, whereas negative height anomalies were center over Lake Baikal and the central Siberia area to its north, a circulation pattern that facilitating the southward intrusion of cold air along the central and western paths into China. In the lower- tropospheric wind field, Northeast Asia was dominated by an anomalous anticyclonic circulation, which favors the transport of warm and moist water vapor from the western North Pacific to northern China. Combined with the southward-moving cold air, this led to significantly above-normal spring precipitation over areas from the southern part of North China to the Huanghuai region. The positive phase of the spring North Atlantic Tripole (NAT) server as an important external forcing signal responsible for the formation of the northern rainy belt over central and eastern China in spring.
IN the spring of 2026 (March to May), the temperature in most part of China was higher than normal, with a national mean temperature of 12.0°C, ranking as the fourth warmest since 1961. The national average precipitation was 166.3 mm, 15.8% above the normal. Spatially, precipitation in central and eastern China presented two main rainfall belts in the North and South: The northern rainfall belt is mainly located from the southern part of North China to the Huanghuai region, while the southern rainfall belt is located from the central-eastern part of Southwest China to the western part of Jiangnan. At 500hPa geopotential height field over the middle and high latitudes of Eurasia exhibited a “two ridges and one trough” pattern. Positive height anomalies prevail over the region from the Ural Mountains to Lake Balkhash and over Northeast Asia, whereas negative height anomalies were center over Lake Baikal and the central Siberia area to its north, a circulation pattern that facilitating the southward intrusion of cold air along the central and western paths into China. In the lower- tropospheric wind field, Northeast Asia was dominated by an anomalous anticyclonic circulation, which favors the transport of warm and moist water vapor from the western North Pacific to northern China. Combined with the southward-moving cold air, this led to significantly above-normal spring precipitation over areas from the southern part of North China to the Huanghuai region. The positive phase of the spring North Atlantic Tripole (NAT) server as an important external forcing signal responsible for the formation of the northern rainy belt over central and eastern China in spring.
Available online: August 21, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.081401
Abstract:
The May Day and National Day holidays are China""s golden tourism week with favorable climate conditions in most regions. However, the sudden and destructive feature of tourism meteorological disasters, combined with the concentrated travel during these periods, may have severe impact on the tourism industry. Daily observational data from national meteorological stations and information of landfall tropical cyclones over China since 1961 were used to reveal the spatiotemporal patterns of tourism meteorological disasters during the May Day and National Day holidays and further analyze long-term trends. The results indicate that the number of meteorological disaster days during the National Day holiday is generally less than that during the May Day holiday. Except for fog and cold wave events, all types of tourism meteorological disasters exert weaker impacts in the National Day holiday than in the May Day holiday. The number of meteorological disaster days decreases in the later stages of both the May Day and National Day holidays, which is favorable for tourism. There are obvious spatial differences in various tourism meteorological disasters across China. North China is primarily impacted by strong winds, dust storms and cold waves. Southwest China is greatly affected by severe convection and fog. Tourism meteorological disasters in South China are mainly dominated by rainstorm and high temperature. The number of national average meteorological disaster days during the two holidays shows a significant decreasing trend. The downward trends in strong winds and dust storm days are especially notable, while high temperature days increase significantly. Overall, the probability of tourism meteorological disaster is higher in northeastern Tibet, southern Qinghai, northwestern Sichuan and southern Yunnan, while it is low in Northeast China, North China, Huang-Huai, the southeastern part of Northwest China, and northern Xinjiang. A total of 18 tropical cyclones made landfall in China during the National Day holiday from 1961 to 2024, with the highest landfall frequency in Guangdong Province. Under the background of global climate warming, more attentions should be paid to the impact of high temperature and landfall tropical cyclones during the May Day and National Day holiday.
The May Day and National Day holidays are China""s golden tourism week with favorable climate conditions in most regions. However, the sudden and destructive feature of tourism meteorological disasters, combined with the concentrated travel during these periods, may have severe impact on the tourism industry. Daily observational data from national meteorological stations and information of landfall tropical cyclones over China since 1961 were used to reveal the spatiotemporal patterns of tourism meteorological disasters during the May Day and National Day holidays and further analyze long-term trends. The results indicate that the number of meteorological disaster days during the National Day holiday is generally less than that during the May Day holiday. Except for fog and cold wave events, all types of tourism meteorological disasters exert weaker impacts in the National Day holiday than in the May Day holiday. The number of meteorological disaster days decreases in the later stages of both the May Day and National Day holidays, which is favorable for tourism. There are obvious spatial differences in various tourism meteorological disasters across China. North China is primarily impacted by strong winds, dust storms and cold waves. Southwest China is greatly affected by severe convection and fog. Tourism meteorological disasters in South China are mainly dominated by rainstorm and high temperature. The number of national average meteorological disaster days during the two holidays shows a significant decreasing trend. The downward trends in strong winds and dust storm days are especially notable, while high temperature days increase significantly. Overall, the probability of tourism meteorological disaster is higher in northeastern Tibet, southern Qinghai, northwestern Sichuan and southern Yunnan, while it is low in Northeast China, North China, Huang-Huai, the southeastern part of Northwest China, and northern Xinjiang. A total of 18 tropical cyclones made landfall in China during the National Day holiday from 1961 to 2024, with the highest landfall frequency in Guangdong Province. Under the background of global climate warming, more attentions should be paid to the impact of high temperature and landfall tropical cyclones during the May Day and National Day holiday.
Available online: August 18, 2026 , DOI: 10.7519/j.issn.1000-0526.2025.111801
Abstract:
In response to the issue of insufficient accuracy in identifying tornadoes using conventional threshold methods,this study proposes a machine learning-based tornado identification method driven by multiple radar-derived storm features. The method first calculates and constructs a storm feature dataset based on radar base data from tornado cases in China between 2003 and 2023. The dataset includes three categories: (1) tornadic storms, (2) non-tornadic storms with significant three-dimensional vortex signature(3DVS), and (3) non-tornadic storms without significant 3DVS. Feature importance ranking is then performed using XGBoost, and the top 14 features are selected as model inputs after combining with PCA. A TFM+CNN deep learning model for tornado recognition is constructed by using the encoder part of the TFM architecture and utilizing CNN to increase the input vector dimension and adding multiple fully connected layers at the end of the model. Three binary classification experiments are conducted using this model: (1) tornado storms vs. non-tornadic storms without significant 3DVS, (2) tornado storms vs. non-tornadic storms with significant 3DVS, and (3) tornado storms vs. non-tornadic storms. Comparative experiments with the XGBoost model are also performed. The results show that the TFM+CNN model achieves CSI values of 84%, 71%, and 73%; POD values of 94%, 83%, and 83%; and FAR values of 11%, 16%, and 14% in the three binary classification experiments, respectively. Compared to the XGBoost model, the TFM+CNN model performs better in most metrics, except for a 5% higher FAR in experiments 1 and 3. Both models exhibit the worst performance in experiment 2. Both models performed worst in experiment 2, indicating that tornadic storms and non-tornadic storms with significant 3DVS are the most challenging to distinguish. Furthermore, the TFM+CNN model has a larger AUC under ROC, indicating stronger generalization ability. It can be seen that the TFM+CNN model has strong tornado recognition ability.
In response to the issue of insufficient accuracy in identifying tornadoes using conventional threshold methods,this study proposes a machine learning-based tornado identification method driven by multiple radar-derived storm features. The method first calculates and constructs a storm feature dataset based on radar base data from tornado cases in China between 2003 and 2023. The dataset includes three categories: (1) tornadic storms, (2) non-tornadic storms with significant three-dimensional vortex signature(3DVS), and (3) non-tornadic storms without significant 3DVS. Feature importance ranking is then performed using XGBoost, and the top 14 features are selected as model inputs after combining with PCA. A TFM+CNN deep learning model for tornado recognition is constructed by using the encoder part of the TFM architecture and utilizing CNN to increase the input vector dimension and adding multiple fully connected layers at the end of the model. Three binary classification experiments are conducted using this model: (1) tornado storms vs. non-tornadic storms without significant 3DVS, (2) tornado storms vs. non-tornadic storms with significant 3DVS, and (3) tornado storms vs. non-tornadic storms. Comparative experiments with the XGBoost model are also performed. The results show that the TFM+CNN model achieves CSI values of 84%, 71%, and 73%; POD values of 94%, 83%, and 83%; and FAR values of 11%, 16%, and 14% in the three binary classification experiments, respectively. Compared to the XGBoost model, the TFM+CNN model performs better in most metrics, except for a 5% higher FAR in experiments 1 and 3. Both models exhibit the worst performance in experiment 2. Both models performed worst in experiment 2, indicating that tornadic storms and non-tornadic storms with significant 3DVS are the most challenging to distinguish. Furthermore, the TFM+CNN model has a larger AUC under ROC, indicating stronger generalization ability. It can be seen that the TFM+CNN model has strong tornado recognition ability.
Available online: August 13, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.072201
Abstract:
This study develops a multi-model fusion quantitative precipitation forecast technique based on the 3D-Unet deep learning model. The method integrates upper-air and surface variables from the ECMWF-IFS global model and the CMA-SH9 regional model for the period 2019–2023. Furthermore, the Integrated Gradients (IG) method is introduced to enhance the interpretability of the model. The results indicate: (1) The 3D-Unet model can effectively enhance the performance of the original precipitation forecast of the model, and the multi-model fusion deep learning forecast model (DL) significantly outperforms the single-model correction model in terms of TS score for 5mm/h precipitation forecast. (2) Real-time operational verification results show that the precipitation forecast performance of the DL model is significantly better than that of the original model forecast and the traditional optimal weight fusion method (OFM). The TS scores for hourly precipitation forecasts of 5mm/h and 10mm/h have increased by approximately 70% and 26.8%, respectively, compared to OFM. For 24-hour cumulative precipitation forecasts, the TS scores of the DL model for moderate rain, heavy rain, and rainstorm levels are superior to those of single models and OFM products; especially for longer forecast horizons of 60 hours and 72 hours, its TS scores for rainstorms have increased by approximately 42.7% and 24.3% compared to the EC model, respectively. (3) The DL model can effectively correct the shape and location of the original forecast rain belt of the model (such as the southwest vortex rainstorm), but it tends to have a wider range of local extreme heavy precipitation. (4) Using the IG algorithm to analyze the importance of feature factors, it is found that the DL model can identify the differences in the effects of the same factors across different models. Factors related to boundary layer processes and local dynamic and thermal structures (such as CAPE and near-surface wind field) have higher importance in the CMA-SH9 regional model, while factors related to synoptic-scale circulation background and air mass thermal state (such as surface pressure and 2m temperature) contribute more prominently in the EC global model.
This study develops a multi-model fusion quantitative precipitation forecast technique based on the 3D-Unet deep learning model. The method integrates upper-air and surface variables from the ECMWF-IFS global model and the CMA-SH9 regional model for the period 2019–2023. Furthermore, the Integrated Gradients (IG) method is introduced to enhance the interpretability of the model. The results indicate: (1) The 3D-Unet model can effectively enhance the performance of the original precipitation forecast of the model, and the multi-model fusion deep learning forecast model (DL) significantly outperforms the single-model correction model in terms of TS score for 5mm/h precipitation forecast. (2) Real-time operational verification results show that the precipitation forecast performance of the DL model is significantly better than that of the original model forecast and the traditional optimal weight fusion method (OFM). The TS scores for hourly precipitation forecasts of 5mm/h and 10mm/h have increased by approximately 70% and 26.8%, respectively, compared to OFM. For 24-hour cumulative precipitation forecasts, the TS scores of the DL model for moderate rain, heavy rain, and rainstorm levels are superior to those of single models and OFM products; especially for longer forecast horizons of 60 hours and 72 hours, its TS scores for rainstorms have increased by approximately 42.7% and 24.3% compared to the EC model, respectively. (3) The DL model can effectively correct the shape and location of the original forecast rain belt of the model (such as the southwest vortex rainstorm), but it tends to have a wider range of local extreme heavy precipitation. (4) Using the IG algorithm to analyze the importance of feature factors, it is found that the DL model can identify the differences in the effects of the same factors across different models. Factors related to boundary layer processes and local dynamic and thermal structures (such as CAPE and near-surface wind field) have higher importance in the CMA-SH9 regional model, while factors related to synoptic-scale circulation background and air mass thermal state (such as surface pressure and 2m temperature) contribute more prominently in the EC global model.
Available online: August 12, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.071201
Abstract:
Sub-seasonal to Seasonal(S2S) hydrological prediction is a core supporting technology for watershed water resource scheduling and flood control & disaster reduction. A semi-distributed hydrological modeling system for the Huaihe River Basin was established based on the SWAT2012 model. Runoff prediction was carried out by driving the SWAT model with historical hindcast data from three sub-seasonal to seasonal (S2S) climate models (CMA-CPSv3, CMME-S2S, and AH-EDDS) from 2006 to 2024 (AH-EDDS from 2009 to 2024). The simulation accuracy of the hydrological model and the prediction skills of the climate models were systematically evaluated. The results show that: the calibrated SWAT model exhibited excellent simulation accuracy during both the calibration period (1981-2010) and validation period (2011-2020), with a coefficient of determination (R2)≥0.88 and Nash-Sutcliffe efficiency (NSE)≥0.85. Four parameters, including groundwater delay time, are the most sensitive parameters. Runoff prediction skills driven by S2S models show significant seasonal differences: the effective lead time in the dry season can reach 40 days, while the effective lead time of CMA-CPSv3 and CMME-S2S is only 1–5 days and that of AH-EDDS is 6–10 days in the wet season. The overall prediction skill in the dry season is higher than that in the wet season. All three models show low hit rate, high false alarm rate and systematic underestimation for extreme precipitation, among which AH-EDDS performs the best in spatial precipitation prediction and comprehensive runoff prediction. The tercile probabilistic prediction and extreme event threshold method can effectively capture watershed hydrological risks, successfully capturing the flood signals in the wet seasons of 2008, 2016, 2020 and the drought signals in the dry seasons of 2009, 2011, 2014. The framework of “multi-model S2S climate prediction driving hydrological model” established in this study can effectively improve the reliability of sub-seasonal to seasonal runoff prediction in the Huaihe River Basin, which has been verified in real time during the 2025 wet season and shows potential for operational application.
Sub-seasonal to Seasonal(S2S) hydrological prediction is a core supporting technology for watershed water resource scheduling and flood control & disaster reduction. A semi-distributed hydrological modeling system for the Huaihe River Basin was established based on the SWAT2012 model. Runoff prediction was carried out by driving the SWAT model with historical hindcast data from three sub-seasonal to seasonal (S2S) climate models (CMA-CPSv3, CMME-S2S, and AH-EDDS) from 2006 to 2024 (AH-EDDS from 2009 to 2024). The simulation accuracy of the hydrological model and the prediction skills of the climate models were systematically evaluated. The results show that: the calibrated SWAT model exhibited excellent simulation accuracy during both the calibration period (1981-2010) and validation period (2011-2020), with a coefficient of determination (R2)≥0.88 and Nash-Sutcliffe efficiency (NSE)≥0.85. Four parameters, including groundwater delay time, are the most sensitive parameters. Runoff prediction skills driven by S2S models show significant seasonal differences: the effective lead time in the dry season can reach 40 days, while the effective lead time of CMA-CPSv3 and CMME-S2S is only 1–5 days and that of AH-EDDS is 6–10 days in the wet season. The overall prediction skill in the dry season is higher than that in the wet season. All three models show low hit rate, high false alarm rate and systematic underestimation for extreme precipitation, among which AH-EDDS performs the best in spatial precipitation prediction and comprehensive runoff prediction. The tercile probabilistic prediction and extreme event threshold method can effectively capture watershed hydrological risks, successfully capturing the flood signals in the wet seasons of 2008, 2016, 2020 and the drought signals in the dry seasons of 2009, 2011, 2014. The framework of “multi-model S2S climate prediction driving hydrological model” established in this study can effectively improve the reliability of sub-seasonal to seasonal runoff prediction in the Huaihe River Basin, which has been verified in real time during the 2025 wet season and shows potential for operational application.
Available online: August 10, 2026 , DOI: doi：10.7519/j.issn.1000-0526.2026.073001
Abstract:
During July 1-2, 2024, a regional torrential rainfall event occurred in Guangxi, characterized by extensive coverage, large cumulative rainfall, and obvious differences in the characteristics of the previous and subsequent stages. This study utilizes multi-source observational data, ERA5 reanalysis data, and numerical model forecast data to analyze the synoptic mechanisms. The results show that (1) The event consisted of frontal heavy rainfall over northern Guangxi and warm area heavy rainfall over southwestern Guangxi. The first stage is frontal heavy rainfall, which occurs in the northern part of Guangxi and is jointly affected by the upper-level trough, low-level shear line, and southwest low-level jet. The second stage is the warm area rainfall, which occurs in the south of Guangxi where the low-level jet continues to affect. (2) In the first stage, the east-west squall enters Guangxi from Guizhou and slowly moves southward, the convective system has strong organization and large east-west span. The stage has an obvious frontogenetic forcing, and boundary layer frontogenesis plays a dominant role in the movement of the rain belt; In the second stage of the process, the lower layer is controlled by southerly winds, forming a dual jet stream feature in the boundary layer and lower troposphere. At the same time, the low-level disturbance caused by the enhanced pulsation of the southerly jet stream in the boundary layer is strengthened, providing favorable conditions for convective triggering. (3) In both stages, conditional instability and high convective available potential energy (CAPE) in the middle and lower troposphere before convective initiation supported both the development and maintenance of convection. (4) Terrain forcing plays an important role in this process. In the first stage, the main upward motion in northern Guangxi is induced by flow over the windward slope due to orographic uplift. In the second stage, upward motion in southern Guangxi is primarily generated by boundary layer frictional convergence.
During July 1-2, 2024, a regional torrential rainfall event occurred in Guangxi, characterized by extensive coverage, large cumulative rainfall, and obvious differences in the characteristics of the previous and subsequent stages. This study utilizes multi-source observational data, ERA5 reanalysis data, and numerical model forecast data to analyze the synoptic mechanisms. The results show that (1) The event consisted of frontal heavy rainfall over northern Guangxi and warm area heavy rainfall over southwestern Guangxi. The first stage is frontal heavy rainfall, which occurs in the northern part of Guangxi and is jointly affected by the upper-level trough, low-level shear line, and southwest low-level jet. The second stage is the warm area rainfall, which occurs in the south of Guangxi where the low-level jet continues to affect. (2) In the first stage, the east-west squall enters Guangxi from Guizhou and slowly moves southward, the convective system has strong organization and large east-west span. The stage has an obvious frontogenetic forcing, and boundary layer frontogenesis plays a dominant role in the movement of the rain belt; In the second stage of the process, the lower layer is controlled by southerly winds, forming a dual jet stream feature in the boundary layer and lower troposphere. At the same time, the low-level disturbance caused by the enhanced pulsation of the southerly jet stream in the boundary layer is strengthened, providing favorable conditions for convective triggering. (3) In both stages, conditional instability and high convective available potential energy (CAPE) in the middle and lower troposphere before convective initiation supported both the development and maintenance of convection. (4) Terrain forcing plays an important role in this process. In the first stage, the main upward motion in northern Guangxi is induced by flow over the windward slope due to orographic uplift. In the second stage, upward motion in southern Guangxi is primarily generated by boundary layer frictional convergence.
Available online: August 05, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.062602
Abstract:
According to the “Regulations on Typhoon Operations and Services” (China Meteorological Administration, 2024), this study verified the accuracy of operational position and intensity estimation, as well as accuracy of track and intensity forecasts for the typhoons over the western North Pacific and the South China Sea in 2025. The mean error of typhoon position estimation by the National Meteorological Centre (NMC) was 13.5 ?km, and the mean absolute error (MAE) of typhoon intensity estimation was 1.1? m/s. The position and intensity estimation accuracies by the NMC were superior to those of other official typhoon forecasting agencies, including the Japan Meteorological Agency (JMA) and the Joint Typhoon Warning Center (JTWC). The mean track forecast errors by the NMC at 24?h and 48?h were 56.6? km and 95.8 ?km, respectively, and the MAE of intensity forecast at 24?h and 48?h were 3.6 ?m/s and 4.9? m/s, respectively. The track and intensity forecast performance of NMC at 24?h and 48?h reached the highest level on record. For AI-based Weather Prediction (AIWP) models, the 95th percentile track forecast errors were generally larger than those of Numerical Weather Prediction (NWP) models at short lead times, but smaller than those of NWP models at long lead times. Correcting the intensity forecasts of AIWP models with an adaptation model reduced the underestimation of typhoon intensity, thereby improving the accuracy of typhoon intensity forecasts.
According to the “Regulations on Typhoon Operations and Services” (China Meteorological Administration, 2024), this study verified the accuracy of operational position and intensity estimation, as well as accuracy of track and intensity forecasts for the typhoons over the western North Pacific and the South China Sea in 2025. The mean error of typhoon position estimation by the National Meteorological Centre (NMC) was 13.5 ?km, and the mean absolute error (MAE) of typhoon intensity estimation was 1.1? m/s. The position and intensity estimation accuracies by the NMC were superior to those of other official typhoon forecasting agencies, including the Japan Meteorological Agency (JMA) and the Joint Typhoon Warning Center (JTWC). The mean track forecast errors by the NMC at 24?h and 48?h were 56.6? km and 95.8 ?km, respectively, and the MAE of intensity forecast at 24?h and 48?h were 3.6 ?m/s and 4.9? m/s, respectively. The track and intensity forecast performance of NMC at 24?h and 48?h reached the highest level on record. For AI-based Weather Prediction (AIWP) models, the 95th percentile track forecast errors were generally larger than those of Numerical Weather Prediction (NWP) models at short lead times, but smaller than those of NWP models at long lead times. Correcting the intensity forecasts of AIWP models with an adaptation model reduced the underestimation of typhoon intensity, thereby improving the accuracy of typhoon intensity forecasts.
Available online: July 15, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.070601
Abstract:
Based on the data from the CMA-CPSv3 and ECMWF-S2S models, a comprehensive assessment of their predictive skills regarding the climatological state of summer subseasonal precipitation, pentad-by-pentad precipitation, precipitation during the key period of the Meiyu season, and precipitation processes in Anhui Province was conducted using multiple scoring methods. Additionally, their predictive capabilities for key circulation systems were analyzed. The results revealed that the climatological state of the summer daily precipitation rate was underestimated by both models, while the frequency of light rain was significantly overestimated, and the frequency of no-rain conditions was significantly underestimated. The predictive skills of the models for precipitation in the 1st to 4th pentads were found to decrease with an increase in the lead time, after which little change was observed. When compared, it was found that the predictive skills of the CMA model for precipitation in the 4th to 5th pentads were generally lower than those of the EC model, with little difference in predictive skills between the two models thereafter. The assessment results for precipitation prediction during the key period of the Meiyu season were similar, and it was found that multi-time ensemble averaging could improve predictive skills to a certain extent. Fifteen to twenty-five days in advance, the Threat Score (TS) for predicting rain/no-rain conditions and significant precipitation events was found to be lower for the CMA model than for the EC model, with little difference observed thereafter. The overall lower predictive skills of the CMA model for precipitation in the 4th to 5th pentads may be attributed to deficiencies in its prediction of key circulation systems such as the East Asian-Pacific (EAP) pattern and the subtropical high.
Based on the data from the CMA-CPSv3 and ECMWF-S2S models, a comprehensive assessment of their predictive skills regarding the climatological state of summer subseasonal precipitation, pentad-by-pentad precipitation, precipitation during the key period of the Meiyu season, and precipitation processes in Anhui Province was conducted using multiple scoring methods. Additionally, their predictive capabilities for key circulation systems were analyzed. The results revealed that the climatological state of the summer daily precipitation rate was underestimated by both models, while the frequency of light rain was significantly overestimated, and the frequency of no-rain conditions was significantly underestimated. The predictive skills of the models for precipitation in the 1st to 4th pentads were found to decrease with an increase in the lead time, after which little change was observed. When compared, it was found that the predictive skills of the CMA model for precipitation in the 4th to 5th pentads were generally lower than those of the EC model, with little difference in predictive skills between the two models thereafter. The assessment results for precipitation prediction during the key period of the Meiyu season were similar, and it was found that multi-time ensemble averaging could improve predictive skills to a certain extent. Fifteen to twenty-five days in advance, the Threat Score (TS) for predicting rain/no-rain conditions and significant precipitation events was found to be lower for the CMA model than for the EC model, with little difference observed thereafter. The overall lower predictive skills of the CMA model for precipitation in the 4th to 5th pentads may be attributed to deficiencies in its prediction of key circulation systems such as the East Asian-Pacific (EAP) pattern and the subtropical high.
Available online: July 02, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.063001
Abstract:
The Yellow Sea, as a typical semi-enclosed shelf shallow sea, is subject to the combined influence of monsoon climates and complex sea-land interactions.Its western coastal zone, characterized by complex multi-scale weather processes and significant socio-economic importance, necessitates focused analysis to improve meteorological prediction and support regional resilience. This study focuses on three basic marine meteorological elements of the Western Coastal Yellow Sea, including air temperature (AT), sea-level pressure (SLP), and sea surface temperature (SST). Based on 5 buoy stations and contemporaneous ERA5 data covering the period from March 2023 to February 2024, we analyze the multi-scale variation characteristics and response mechanisms of the three elements, conduct a comparative analysis between the in-situ data and ERA5 data, and evaluate the applicability of ERA5 data in the shallow waters of the western Yellow Sea. The analysis results indicate that 1) There is a significant interdependence among the three variables: AT and SST show a strong positive correlation (r≈0.9), while AT inversely correlates with SLP (r≈-0.8), and SST demonstrates a moderate negative relationship with atmospheric pressure (r≈-0.55); 2) Both AT and SST exhibit unimodal diurnal variations, and they reach the peak in summer- autumn, decrease in winter-spring. However, SST shows smaller diurnal and seasonal thermal amplitudes than AT, and the valley values of SST also lag behind that of AT by 1 hour in diurnal minima and 1 month in seasonal minima. In contrast, SLP exhibits double-peak and double-valley diurnal variations, and it reaches the peak in autumn and winter, decreases in spring and summer; 3）The sea-air temperature gradient (SST-AT) exhibits a regular diurnal cycle, expanding at night and peaking at 06:00, then shrinking during the day and reaching a trough at 16:00. On an annual scale, SST exceeds AT from August to February of the following year, accounting for approximately 58%; 4) In terms of responses to typical weather processes, key elements exhibited distinct response patterns: During the sea fog event in June, warm-moist advection over a cold sea surface drove the SST-AT to -3.31 ℃, forming the thermodynamic conditions for advective cooling fog; Under the influence of the Jiang-Huai cyclone in August, the elements responded in a clear sequence: AT > SLP > SST, while the recovery periods are similar between AT and SLP but longer for SST; Throughout the cold wave event in November, AT showed a “cliff-like” decrease of 14.95 ℃ within 40 hours, while SST decreased by only 2.41 ℃, highlighting the ocean''s significant thermal inertia. 5) Compared to in situ observations, ERA5 fails to resolve SST diurnal variability, exhibits systematic biases (-0.19 ℃ AT, -0.26 hPa SLP , -0.14 ℃ SST). This study is based on data from March 2023 to February 2024, which has certain limitations in temporal coverage. Future work will focus on analyzing longer-term time-series data to enhance statistical robustness. Besides, we will further quantify the threshold of SST-AT for sea fog/low cloud formation, to provide more representative insights into Yellow Sea air-sea interaction mechanisms and their climatic implications.
The Yellow Sea, as a typical semi-enclosed shelf shallow sea, is subject to the combined influence of monsoon climates and complex sea-land interactions.Its western coastal zone, characterized by complex multi-scale weather processes and significant socio-economic importance, necessitates focused analysis to improve meteorological prediction and support regional resilience. This study focuses on three basic marine meteorological elements of the Western Coastal Yellow Sea, including air temperature (AT), sea-level pressure (SLP), and sea surface temperature (SST). Based on 5 buoy stations and contemporaneous ERA5 data covering the period from March 2023 to February 2024, we analyze the multi-scale variation characteristics and response mechanisms of the three elements, conduct a comparative analysis between the in-situ data and ERA5 data, and evaluate the applicability of ERA5 data in the shallow waters of the western Yellow Sea. The analysis results indicate that 1) There is a significant interdependence among the three variables: AT and SST show a strong positive correlation (r≈0.9), while AT inversely correlates with SLP (r≈-0.8), and SST demonstrates a moderate negative relationship with atmospheric pressure (r≈-0.55); 2) Both AT and SST exhibit unimodal diurnal variations, and they reach the peak in summer- autumn, decrease in winter-spring. However, SST shows smaller diurnal and seasonal thermal amplitudes than AT, and the valley values of SST also lag behind that of AT by 1 hour in diurnal minima and 1 month in seasonal minima. In contrast, SLP exhibits double-peak and double-valley diurnal variations, and it reaches the peak in autumn and winter, decreases in spring and summer; 3）The sea-air temperature gradient (SST-AT) exhibits a regular diurnal cycle, expanding at night and peaking at 06:00, then shrinking during the day and reaching a trough at 16:00. On an annual scale, SST exceeds AT from August to February of the following year, accounting for approximately 58%; 4) In terms of responses to typical weather processes, key elements exhibited distinct response patterns: During the sea fog event in June, warm-moist advection over a cold sea surface drove the SST-AT to -3.31 ℃, forming the thermodynamic conditions for advective cooling fog; Under the influence of the Jiang-Huai cyclone in August, the elements responded in a clear sequence: AT > SLP > SST, while the recovery periods are similar between AT and SLP but longer for SST; Throughout the cold wave event in November, AT showed a “cliff-like” decrease of 14.95 ℃ within 40 hours, while SST decreased by only 2.41 ℃, highlighting the ocean''s significant thermal inertia. 5) Compared to in situ observations, ERA5 fails to resolve SST diurnal variability, exhibits systematic biases (-0.19 ℃ AT, -0.26 hPa SLP , -0.14 ℃ SST). This study is based on data from March 2023 to February 2024, which has certain limitations in temporal coverage. Future work will focus on analyzing longer-term time-series data to enhance statistical robustness. Besides, we will further quantify the threshold of SST-AT for sea fog/low cloud formation, to provide more representative insights into Yellow Sea air-sea interaction mechanisms and their climatic implications.
Available online: July 01, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.060102
Abstract:
Abstract：To enhance the understanding of the structural characteristics of downburst storms under weak dynamic forcing (controlled by subtropical high with vertical wind shear below 10 m·s-1), and to evaluate the probability of operational weather radar in downburst warning. Based on the observation data of 12 SA-Doppler weather radars, this study statistically analyzes the storm structure characteristics of the downburst samples in Jiangsu Province from June to September during 2019–2020. The results indicate that under weak vertical wind shear, downburst storms exhibit relatively high reflectivity, with a mean reflectivity core of 57 dBZ and a height of 5.2 km above the ground. Approximately 1/3 of the storms are pulse storms. Downburst storms often accompanied by a descending reflectivity core (DRC) and decreasing vertical integrated liquid water content (VIL), with slow descent speed and a duration of 1-2 volume, and the lead time for warning is 0-12 min. Gust fronts are detected in 47% of downburst storms, and the average motion speeds of both storms and gust fronts are 7 m·s?1. The proportion of middle altitude radial convergence(MARC) and other radar signatures is not high. About 1/3 of the downburst storms are associated with MARC, while weak echo region (WER), rear inflow (RI), and mesovortices each occur in fewer than 10% of cases. However, downbursts associated with pulse storms, MARC, or other features tend to be more intense, and severe downbursts often reoccur after the initial downbursts. The duration of MARC and other features primarily ranges between 12-36 min. The warning lead times for downbursts and the severe downbursts by MARC and other features are 0-24 min and 6-36 min, respectively. The above quantitative structural characteristics advance the understanding of downburst storms, and indicate that operational weather radars can only provide warnings for part of downbursts, revealing limitations in downbursts warning under weak dynamic forcing.
Abstract：To enhance the understanding of the structural characteristics of downburst storms under weak dynamic forcing (controlled by subtropical high with vertical wind shear below 10 m·s-1), and to evaluate the probability of operational weather radar in downburst warning. Based on the observation data of 12 SA-Doppler weather radars, this study statistically analyzes the storm structure characteristics of the downburst samples in Jiangsu Province from June to September during 2019–2020. The results indicate that under weak vertical wind shear, downburst storms exhibit relatively high reflectivity, with a mean reflectivity core of 57 dBZ and a height of 5.2 km above the ground. Approximately 1/3 of the storms are pulse storms. Downburst storms often accompanied by a descending reflectivity core (DRC) and decreasing vertical integrated liquid water content (VIL), with slow descent speed and a duration of 1-2 volume, and the lead time for warning is 0-12 min. Gust fronts are detected in 47% of downburst storms, and the average motion speeds of both storms and gust fronts are 7 m·s?1. The proportion of middle altitude radial convergence(MARC) and other radar signatures is not high. About 1/3 of the downburst storms are associated with MARC, while weak echo region (WER), rear inflow (RI), and mesovortices each occur in fewer than 10% of cases. However, downbursts associated with pulse storms, MARC, or other features tend to be more intense, and severe downbursts often reoccur after the initial downbursts. The duration of MARC and other features primarily ranges between 12-36 min. The warning lead times for downbursts and the severe downbursts by MARC and other features are 0-24 min and 6-36 min, respectively. The above quantitative structural characteristics advance the understanding of downburst storms, and indicate that operational weather radars can only provide warnings for part of downbursts, revealing limitations in downbursts warning under weak dynamic forcing.
Available online: June 29, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.022802
Abstract:
After long-term unremitting air pollution control, acid rain pollution situation in China has been significantly improved with the composition of atmospheric acid-causing pollutants changed. The causes and characteristics of acid rain in China are also undergoing profound changes. In order to understand the new characteristics of acid rain pollution in typical northern regions of China, based on the statistical analysis of precipitation pH and conductivity at Mt.Tai from 1993 to 2021 and at Tai"an Station from 2007 to 2021, this paper carried out synchronous precipitation chemical enhanced observations at the two stations from May 2018 to April 2019. The contributions of below-cloud processes and long-distance transport to precipitation acidification in the Taishan area were analyzed, combined with the early research results. The results show that the annual average value of precipitation pH in Mt.Tai varies from 4.36 to 6.47, and that in Tai"an Station are from 5.07 to 6.39. Since 2011, due to the incrementally decrease in SO2 and NOX emissions in China, the precipitation pH value has continued to rise and remain at a high level, and the conductivity has shown a continuous downward trend. The analysis results of ion components of precipitation samples obtained from May 2018 to April 2019 show that SO42- and NO3- are the main anions in precipitation, and NH4+ and Ca2+ are the main cations. The average chemical equivalent ratio of SO42- and NO3- in precipitation is about 1.21, which is significantly lower than the early observation results, indicating the increased influence of nitrogen oxides on acid precipitation. The below-cloud washing process leads the concentration of ion components in precipitation at Tai"an Station higher than that at Mt.Tai. The acid precipitation in the Taishan area is mainly affected by Shandong Province and surrounding provinces. The precipitation acidity at Mt.Tai and Tai"an Station is closely related to atmospheric pollutant emissions. The influence of below-cloud washing process on Tai"an Station is gradually decreasing, and? Mt.Tai is also affected by long-distance transport.
After long-term unremitting air pollution control, acid rain pollution situation in China has been significantly improved with the composition of atmospheric acid-causing pollutants changed. The causes and characteristics of acid rain in China are also undergoing profound changes. In order to understand the new characteristics of acid rain pollution in typical northern regions of China, based on the statistical analysis of precipitation pH and conductivity at Mt.Tai from 1993 to 2021 and at Tai"an Station from 2007 to 2021, this paper carried out synchronous precipitation chemical enhanced observations at the two stations from May 2018 to April 2019. The contributions of below-cloud processes and long-distance transport to precipitation acidification in the Taishan area were analyzed, combined with the early research results. The results show that the annual average value of precipitation pH in Mt.Tai varies from 4.36 to 6.47, and that in Tai"an Station are from 5.07 to 6.39. Since 2011, due to the incrementally decrease in SO2 and NOX emissions in China, the precipitation pH value has continued to rise and remain at a high level, and the conductivity has shown a continuous downward trend. The analysis results of ion components of precipitation samples obtained from May 2018 to April 2019 show that SO42- and NO3- are the main anions in precipitation, and NH4+ and Ca2+ are the main cations. The average chemical equivalent ratio of SO42- and NO3- in precipitation is about 1.21, which is significantly lower than the early observation results, indicating the increased influence of nitrogen oxides on acid precipitation. The below-cloud washing process leads the concentration of ion components in precipitation at Tai"an Station higher than that at Mt.Tai. The acid precipitation in the Taishan area is mainly affected by Shandong Province and surrounding provinces. The precipitation acidity at Mt.Tai and Tai"an Station is closely related to atmospheric pollutant emissions. The influence of below-cloud washing process on Tai"an Station is gradually decreasing, and? Mt.Tai is also affected by long-distance transport.
Available online: June 18, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.041602
Abstract:
Using conventional upper-air and surface observations, Doppler radar data, satellite nephogram and ERA5 reanalysis data, 451 drylines cases and 156 drylines cases triggering convection in Shandong Province from 2001 to 2022 were analyzed. Results are as follows: the total number of days for drylines in Shandong from 2001 to 2022 was 451 days, with 156 days triggering convective drylines. Drylines triggering convection occurred from April to September. May and the first ten-day period of May were the months and ten-day periods with the highest frequency of drylines occurrences. The primary occurrence time was between 14:00 and 17:00 (Beijing Time). The drylines are primarily distributed in central and northwestern Shandong. The formation of drylines is related to the influence of mountain ranges and the distribution of land and sea. The widths of convective and non-convective drylines are 75–106 km and 80–114 km, respectively, with dew point gradients of 11.8–16.7°C/(100 km) and 9.8–15°C/(100 km). Temperature distributions on both dry and wet sides of drylines range from 23 to 33°C, with a temperature gradient of 1–3 °C /(100 km), temperatures on the dry side are slightly higher. Compared to non-convective drylines, convective drylines exhibit higher temperatures and dew points on both the warm and cold sides, narrower widths, greater dew point gradients, this is related to the fact that the circulation associated with the force-driven component becomes stronger when the horizontal dew point gradient is large. The thresholds of the key environmental parameters for drylines triggering convection reveal that while vertical wind shear between 0-1 km and 0-3 km are difficult to distinguish due to minimal differences, other parameters including surface dew point temperature(Td), precipitable water (PW), CAPE, CIN, temperature difference between 850 and 500 hPa, DCAPE, severe weather threat index (SWEAT), and vertical wind shear between 0–6 km exhibit certain distinguishability between convective and non-convective drylines. The seasonal variation in convective parameters under unstable conditions is relatively small, but environmental parameters related to moisture conditions and vertical wind shear between 0–6 km exhibit significant seasonal differences. Therefore, their thresholds should be differentiated by season. Drylines frequently develop under conditions of northwest flow, ahead of westerly trough, and northeast low-pressure vortex at the upper-level 500 hPa. They are primarily influenced by the shear line of the low-pressure vortex or the northwest flow at the lower-level 700 hPa and 850 hPa, with the boundary layer often accompanied by shear lines. The surface situation is primarily situated within the convergence flow field of continental warm low or at the bottom or front of low-pressure systems.
Using conventional upper-air and surface observations, Doppler radar data, satellite nephogram and ERA5 reanalysis data, 451 drylines cases and 156 drylines cases triggering convection in Shandong Province from 2001 to 2022 were analyzed. Results are as follows: the total number of days for drylines in Shandong from 2001 to 2022 was 451 days, with 156 days triggering convective drylines. Drylines triggering convection occurred from April to September. May and the first ten-day period of May were the months and ten-day periods with the highest frequency of drylines occurrences. The primary occurrence time was between 14:00 and 17:00 (Beijing Time). The drylines are primarily distributed in central and northwestern Shandong. The formation of drylines is related to the influence of mountain ranges and the distribution of land and sea. The widths of convective and non-convective drylines are 75–106 km and 80–114 km, respectively, with dew point gradients of 11.8–16.7°C/(100 km) and 9.8–15°C/(100 km). Temperature distributions on both dry and wet sides of drylines range from 23 to 33°C, with a temperature gradient of 1–3 °C /(100 km), temperatures on the dry side are slightly higher. Compared to non-convective drylines, convective drylines exhibit higher temperatures and dew points on both the warm and cold sides, narrower widths, greater dew point gradients, this is related to the fact that the circulation associated with the force-driven component becomes stronger when the horizontal dew point gradient is large. The thresholds of the key environmental parameters for drylines triggering convection reveal that while vertical wind shear between 0-1 km and 0-3 km are difficult to distinguish due to minimal differences, other parameters including surface dew point temperature(Td), precipitable water (PW), CAPE, CIN, temperature difference between 850 and 500 hPa, DCAPE, severe weather threat index (SWEAT), and vertical wind shear between 0–6 km exhibit certain distinguishability between convective and non-convective drylines. The seasonal variation in convective parameters under unstable conditions is relatively small, but environmental parameters related to moisture conditions and vertical wind shear between 0–6 km exhibit significant seasonal differences. Therefore, their thresholds should be differentiated by season. Drylines frequently develop under conditions of northwest flow, ahead of westerly trough, and northeast low-pressure vortex at the upper-level 500 hPa. They are primarily influenced by the shear line of the low-pressure vortex or the northwest flow at the lower-level 700 hPa and 850 hPa, with the boundary layer often accompanied by shear lines. The surface situation is primarily situated within the convergence flow field of continental warm low or at the bottom or front of low-pressure systems.
Available online: June 05, 2026 , DOI: 10.7519/j.issn.1000-0526.2025.121001
Abstract:
Hail weather is characterized by sudden onset, pronounced locality, and significant destructive power, posing multiple adverse impacts on human production and daily life. Accurate and timely identification of hail weather holds critical importance for disaster early warning and prevention. Although Doppler weather radar observations play a vital role in hail identification, the limited spatiotemporal coverage of single-source radar data and the insufficient timeliness and accuracy of traditional identification methods remain challenging. To address the limitations of single data sources, this study proposes a hail identification method based on satellite and radar data fusion. Leveraging the spatiotemporal complementarity between satellite and radar data, the method combines threshold characteristics of satellite and radar observations before and after hailfall to achieve efficient multi-source data fusion and identification through deep learning algorithms. Experimental results demonstrate that the proposed method effectively integrates satellite and radar data, with the YOLOv7 model achieving a recognition accuracy of 90.83%. It successfully identifies hail-affected regions, providing crucial references for hail weather early warning. Notably, in areas where radar data are susceptible to terrain occlusion, the method significantly mitigates inaccurate hail region identification caused by poor data quality, demonstrating high practical value.
Hail weather is characterized by sudden onset, pronounced locality, and significant destructive power, posing multiple adverse impacts on human production and daily life. Accurate and timely identification of hail weather holds critical importance for disaster early warning and prevention. Although Doppler weather radar observations play a vital role in hail identification, the limited spatiotemporal coverage of single-source radar data and the insufficient timeliness and accuracy of traditional identification methods remain challenging. To address the limitations of single data sources, this study proposes a hail identification method based on satellite and radar data fusion. Leveraging the spatiotemporal complementarity between satellite and radar data, the method combines threshold characteristics of satellite and radar observations before and after hailfall to achieve efficient multi-source data fusion and identification through deep learning algorithms. Experimental results demonstrate that the proposed method effectively integrates satellite and radar data, with the YOLOv7 model achieving a recognition accuracy of 90.83%. It successfully identifies hail-affected regions, providing crucial references for hail weather early warning. Notably, in areas where radar data are susceptible to terrain occlusion, the method significantly mitigates inaccurate hail region identification caused by poor data quality, demonstrating high practical value.
Available online: June 04, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.060401
Abstract:
In November 2024, the precipitation in Heilongjiang Province reached 31.5 mm, ranking as the 4th highest in the same period since 1961. An extreme precipitation event during 25–29 November contributed 73% of the total monthly precipitation, with its 5-day accumulated precipitation being 13.5 times the climatological average for the same period. Analysis of the circulation evolution revealed a clear two-phase variation in the mid- to high-latitude circulation over Eurasia. In the early phase (1–24 November), precipitation was suppressed by a "negative in the west, positive in the east" geopotential height anomaly pattern, corresponding to below-normal precipitation. During the late phase ((25–29 November), the circulation adjusted to an "inverted Ω" pattern, characterized by the establishment of dual blocking highs over the Ural Mountains and the Sea of Okhotsk, which formed a stable configuration with the northeast cold vortex (NECV). This configuration guided the southward intrusion of polar cold air, which intensely converged with the warm and moist air transported by a low-level jet over Heilongjiang, enhancing moisture convergence and dynamic lifting. Influenced by the locked phase of the dual blocking highs, this weather system remained stagnant over the region, ultimately triggering a persistent, widespread heavy precipitation event. Statistics show that 4 of the 10 wettest Novembers in Heilongjiang since 1961 were dominated by single extreme precipitation events, revealing the critical role of synoptic-scale system adjustments in causing November precipitation anomalies. These results enhance our understanding of the causes of extreme precipitation in November in Heilongjiang Province and provide important references for its prediction.
In November 2024, the precipitation in Heilongjiang Province reached 31.5 mm, ranking as the 4th highest in the same period since 1961. An extreme precipitation event during 25–29 November contributed 73% of the total monthly precipitation, with its 5-day accumulated precipitation being 13.5 times the climatological average for the same period. Analysis of the circulation evolution revealed a clear two-phase variation in the mid- to high-latitude circulation over Eurasia. In the early phase (1–24 November), precipitation was suppressed by a "negative in the west, positive in the east" geopotential height anomaly pattern, corresponding to below-normal precipitation. During the late phase ((25–29 November), the circulation adjusted to an "inverted Ω" pattern, characterized by the establishment of dual blocking highs over the Ural Mountains and the Sea of Okhotsk, which formed a stable configuration with the northeast cold vortex (NECV). This configuration guided the southward intrusion of polar cold air, which intensely converged with the warm and moist air transported by a low-level jet over Heilongjiang, enhancing moisture convergence and dynamic lifting. Influenced by the locked phase of the dual blocking highs, this weather system remained stagnant over the region, ultimately triggering a persistent, widespread heavy precipitation event. Statistics show that 4 of the 10 wettest Novembers in Heilongjiang since 1961 were dominated by single extreme precipitation events, revealing the critical role of synoptic-scale system adjustments in causing November precipitation anomalies. These results enhance our understanding of the causes of extreme precipitation in November in Heilongjiang Province and provide important references for its prediction.
Available online: June 03, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.010801
Abstract:
本文基于注意力机制和编码-解码结构设计了OF-RainNet降水预报模型，实现了降水平流外推运动信息和深度学习模型的有效融合。以北京地区为试验区域，构建1km分辨率，逐6分钟的1h和2h的降水临近预报试验，得到如下结论： 1） 模型以SWAN的1hQPE和0-2hQPF作为输入数据，通过编码阶段显式输入SWAN外推产品，实现传统外推和深度学习模型的融合。模型引入SuperTokenAttention超级词元注意力模块结构，提取面向降水临近预报有意义的语义单元并计算多头注意力，设计分段权重的损失函数，最终实现降水的1h和2h临近预报。 2） 通过2024年7-8月和2025年7-8月北京试验区长时间序列对比试验发现，OF-RainNet在5mm/h，10mm/h，20mm/h，50mm/h的1h、2h预报时效的TS评分全部高于SWAN-QPF和PredRNNV2模型，其中在≥10mm/h的阈值段内，相对提升超过10%，说明OF-RainNet融合外推预报和深度学习模型后能获得正预报技巧，强降水预报性能提升明显。 3） 相较于未引入光流外推产品和STA模块的基础版本模型，OF-RainNet的TS评分显著提升，强降水Bias略高于1。说明模型通过引入STA模块和外推产品，能够有效提升模型的强降水预报能力。 4） 通过个例分析发现，OF-RainNet的预报性能优于PredRNNv2，对强降水持续阶段的预报能力优于局地触发阶段，1h预报优于2h预报OF-RainNet的1h预报明显优于2h预报，强降水持续阶段的预报能力优于强降水局地触发阶段。 预报试验结果表明，仅依赖雷达观测的外推预报，难以捕捉强降水的局地精细化发展演变特征，尤其是对局地触发的强降水预报技巧较低。本文通过深度学习模型构建多源预报数据融合框架，实现了不同预报方法数据的有效融合，为未来的工作提供了新的思路和技术方向。后续研究可在模型输入层扩展不同方法的预报产品，如“风雷”预报产品、数值模式环境场，物理量场和回波预报场等。通过深度学习模型挖掘各类预报产品的核心优势，实现多预报方法的集成融合，进一步提升临近客观预报对降水生消演变的预报能力。
本文基于注意力机制和编码-解码结构设计了OF-RainNet降水预报模型，实现了降水平流外推运动信息和深度学习模型的有效融合。以北京地区为试验区域，构建1km分辨率，逐6分钟的1h和2h的降水临近预报试验，得到如下结论： 1） 模型以SWAN的1hQPE和0-2hQPF作为输入数据，通过编码阶段显式输入SWAN外推产品，实现传统外推和深度学习模型的融合。模型引入SuperTokenAttention超级词元注意力模块结构，提取面向降水临近预报有意义的语义单元并计算多头注意力，设计分段权重的损失函数，最终实现降水的1h和2h临近预报。 2） 通过2024年7-8月和2025年7-8月北京试验区长时间序列对比试验发现，OF-RainNet在5mm/h，10mm/h，20mm/h，50mm/h的1h、2h预报时效的TS评分全部高于SWAN-QPF和PredRNNV2模型，其中在≥10mm/h的阈值段内，相对提升超过10%，说明OF-RainNet融合外推预报和深度学习模型后能获得正预报技巧，强降水预报性能提升明显。 3） 相较于未引入光流外推产品和STA模块的基础版本模型，OF-RainNet的TS评分显著提升，强降水Bias略高于1。说明模型通过引入STA模块和外推产品，能够有效提升模型的强降水预报能力。 4） 通过个例分析发现，OF-RainNet的预报性能优于PredRNNv2，对强降水持续阶段的预报能力优于局地触发阶段，1h预报优于2h预报OF-RainNet的1h预报明显优于2h预报，强降水持续阶段的预报能力优于强降水局地触发阶段。 预报试验结果表明，仅依赖雷达观测的外推预报，难以捕捉强降水的局地精细化发展演变特征，尤其是对局地触发的强降水预报技巧较低。本文通过深度学习模型构建多源预报数据融合框架，实现了不同预报方法数据的有效融合，为未来的工作提供了新的思路和技术方向。后续研究可在模型输入层扩展不同方法的预报产品，如“风雷”预报产品、数值模式环境场，物理量场和回波预报场等。通过深度学习模型挖掘各类预报产品的核心优势，实现多预报方法的集成融合，进一步提升临近客观预报对降水生消演变的预报能力。
Available online: April 27, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.030401
Abstract:
Northern Ningxia is situated in the temperate continental climate zone of northwestern China, where complex topography and the climatological characteristics of warm-season convective storms (CS) remain not fully understood. This study utilizes CINRAD/CD radar products and sounding data from Yinchuan during May–September from 2011 to 2016 to investigate the climatological characteristics of CS in northern Ningxia under different prevailing wind directions at 500 hPa and 700 hPa. For the first time in this research domain, we present a comprehensive analysis integrating mean circulation patterns, convective environments, and CS intensity. Results demonstrate that solar radiative heating and topographic forcing from the Helan Mountains dominate the formation of CS climatological characteristics in northern Ningxia. Upon this foundation, dynamically varying prevailing winds induce more complex and diverse patterns in CS occurrence frequency, occurrence efficiency (defined as the ratio of CS occurrence frequency under a specific prevailing wind direction to the total frequency of that wind direction), diurnal variation, and spatial distribution. Notably, when southwesterly winds prevail at 500 hPa while easterly winds prevail at 700 hPa, the influence of prevailing winds becomes particularly pronounced. The mean 500 hPa circulation pattern associated with CS occurrence under a given prevailing wind direction characterizes the typical synoptic–scale systems and convective environments favorable for CS development under that flow regime, thereby directly determining CS occurrence efficiency and intensity. When northerly or northwesterly winds prevail at 500 hPa, northern Ningxia is positioned behind the trough, resulting in the lowest CS occurrence efficiency, the highest CAPE and CIN, and the strongest CS intensity. Under southerly or southwesterly wind regimes, the region is situated between the ridge and trough, where favorable moisture transport conditions lead to maximum CS occurrence efficiency; however, the minimal vertical temperature difference results in relatively weaker CS development. During prevailing westerly flows, the mid–latitude circulation exhibits zonal characteristics with frequent upstream shortwave trough activity, yielding intermediate levels of CS occurrence efficiency, CS intensity, and environmental parameters. At 700 hPa, differences in CS occurrence efficiency and intensity across various prevailing wind directions are relatively smaller, with CS intensity showing a strong correspondence to the vertical temperature gradient.
Northern Ningxia is situated in the temperate continental climate zone of northwestern China, where complex topography and the climatological characteristics of warm-season convective storms (CS) remain not fully understood. This study utilizes CINRAD/CD radar products and sounding data from Yinchuan during May–September from 2011 to 2016 to investigate the climatological characteristics of CS in northern Ningxia under different prevailing wind directions at 500 hPa and 700 hPa. For the first time in this research domain, we present a comprehensive analysis integrating mean circulation patterns, convective environments, and CS intensity. Results demonstrate that solar radiative heating and topographic forcing from the Helan Mountains dominate the formation of CS climatological characteristics in northern Ningxia. Upon this foundation, dynamically varying prevailing winds induce more complex and diverse patterns in CS occurrence frequency, occurrence efficiency (defined as the ratio of CS occurrence frequency under a specific prevailing wind direction to the total frequency of that wind direction), diurnal variation, and spatial distribution. Notably, when southwesterly winds prevail at 500 hPa while easterly winds prevail at 700 hPa, the influence of prevailing winds becomes particularly pronounced. The mean 500 hPa circulation pattern associated with CS occurrence under a given prevailing wind direction characterizes the typical synoptic–scale systems and convective environments favorable for CS development under that flow regime, thereby directly determining CS occurrence efficiency and intensity. When northerly or northwesterly winds prevail at 500 hPa, northern Ningxia is positioned behind the trough, resulting in the lowest CS occurrence efficiency, the highest CAPE and CIN, and the strongest CS intensity. Under southerly or southwesterly wind regimes, the region is situated between the ridge and trough, where favorable moisture transport conditions lead to maximum CS occurrence efficiency; however, the minimal vertical temperature difference results in relatively weaker CS development. During prevailing westerly flows, the mid–latitude circulation exhibits zonal characteristics with frequent upstream shortwave trough activity, yielding intermediate levels of CS occurrence efficiency, CS intensity, and environmental parameters. At 700 hPa, differences in CS occurrence efficiency and intensity across various prevailing wind directions are relatively smaller, with CS intensity showing a strong correspondence to the vertical temperature gradient.
Available online: April 24, 2026 , DOI: 10.7519/j.issn.1000-0526.2026.041701
Abstract:
Based on the precipitation data of 155 meteorological stations in the eastern part of Northwest China from 1961 to 2022, as well as historical datasets including global atmospheric data, sea surface temperature (SST), and sea ice data, this study integrates the Temporal Convolutional Network(TCN) module and the Convolutional Block Attention Module(CBAM) into the Long Short-Term Memory (LSTM) deep learning algorithm. A climate-smart prediction model for summer precipitation in the eastern part of Northwest China(named CBAM-TCN-LSTM) based on the fusion of deep learning algorithms was thereby established. The predictive performance of the model was verified, and its predictive capability was compared with that of multiple other deep learning algorithms.The results show that the intelligent prediction model based on the fusion of multiple deep learning algorithms outperforms single-algorithm deep learning models. During the independent sample validation period(2018–2022), the PS score for summer precipitation prediction ranged from 60% to 80%, with an average value of 73.8%.The Anomaly Correlation Coefficient (ACC) was positive for all years except 2020, with a mean value of 0.14, representing a significant improvement over other models. When compared with 7 current mainstream models (including machine learning algorithms, deep learning networks, and time-series networks), the CBAM-TCN-LSTM model exhibited superior performance across all five evaluation metrics: PS, PC, ACC, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Furthermore, the CBAM-TCN-LSTM model was successfully applied in the 2023 flood season forecasting operation, accurately predicting the characteristic of below-normal summer precipitation in most areas of the eastern part of Northwest China, with a PS score of 90%.By building a precipitation prediction model that combines the TCN module and the CBAM module on the basis of the LSTM model (which has strong time-series predictive capability), this study provides a scientific basis and technical support for regional precipitation prediction, and the model holds good prospects for popularization and application.
Based on the precipitation data of 155 meteorological stations in the eastern part of Northwest China from 1961 to 2022, as well as historical datasets including global atmospheric data, sea surface temperature (SST), and sea ice data, this study integrates the Temporal Convolutional Network(TCN) module and the Convolutional Block Attention Module(CBAM) into the Long Short-Term Memory (LSTM) deep learning algorithm. A climate-smart prediction model for summer precipitation in the eastern part of Northwest China(named CBAM-TCN-LSTM) based on the fusion of deep learning algorithms was thereby established. The predictive performance of the model was verified, and its predictive capability was compared with that of multiple other deep learning algorithms.The results show that the intelligent prediction model based on the fusion of multiple deep learning algorithms outperforms single-algorithm deep learning models. During the independent sample validation period(2018–2022), the PS score for summer precipitation prediction ranged from 60% to 80%, with an average value of 73.8%.The Anomaly Correlation Coefficient (ACC) was positive for all years except 2020, with a mean value of 0.14, representing a significant improvement over other models. When compared with 7 current mainstream models (including machine learning algorithms, deep learning networks, and time-series networks), the CBAM-TCN-LSTM model exhibited superior performance across all five evaluation metrics: PS, PC, ACC, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Furthermore, the CBAM-TCN-LSTM model was successfully applied in the 2023 flood season forecasting operation, accurately predicting the characteristic of below-normal summer precipitation in most areas of the eastern part of Northwest China, with a PS score of 90%.By building a precipitation prediction model that combines the TCN module and the CBAM module on the basis of the LSTM model (which has strong time-series predictive capability), this study provides a scientific basis and technical support for regional precipitation prediction, and the model holds good prospects for popularization and application.
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2012,38(12):1482-1491, DOI: 10.7519/j.issn.1000-0526.2012.12.005
Abstract:
By using the conventional meteorological data, Doppler radar data and NCEP/NCAR reanalysis data, the characteristics of Doppler radar’s reflectivity, environmental condition and trigger mechanism of the heavy rain are analyzed and compared between two abrupt heavy rain processes occurring in Sichuan Basin on 3 July (7.3) and 23 July (7.23) 2011. The results show that: the “7.3” heavy rain happened under a typical circulation background, and moisture transporting to the heavy rain area from the South China Sea was smoothly, thus the heavy rainfall maintained so long, but the “7.23” heavy rain occurred behind the upper cold vortex, and convective unstable energy was abundant and vertical wind shear was strong, thus this heavy rain process happened with hail and thunderstorm weather accompanied, its radar reflectivity was 5 dBz stronger than “7.3” case and had the characteristics of severe storms such as the low level weak reflectivity and the upper echo overhang. As a whole, the non equilibrium force is contributed to the occurrence of heavy rain and it is the excited mechanism of the two heavy rainfalls, and the change of the divergence evolvement is consistent with the strength and the position of the heavy rain which would happen 6 hours later．
By using the conventional meteorological data, Doppler radar data and NCEP/NCAR reanalysis data, the characteristics of Doppler radar’s reflectivity, environmental condition and trigger mechanism of the heavy rain are analyzed and compared between two abrupt heavy rain processes occurring in Sichuan Basin on 3 July (7.3) and 23 July (7.23) 2011. The results show that: the “7.3” heavy rain happened under a typical circulation background, and moisture transporting to the heavy rain area from the South China Sea was smoothly, thus the heavy rainfall maintained so long, but the “7.23” heavy rain occurred behind the upper cold vortex, and convective unstable energy was abundant and vertical wind shear was strong, thus this heavy rain process happened with hail and thunderstorm weather accompanied, its radar reflectivity was 5 dBz stronger than “7.3” case and had the characteristics of severe storms such as the low level weak reflectivity and the upper echo overhang. As a whole, the non equilibrium force is contributed to the occurrence of heavy rain and it is the excited mechanism of the two heavy rainfalls, and the change of the divergence evolvement is consistent with the strength and the position of the heavy rain which would happen 6 hours later．
2017,43(7):769-780, DOI: 10.7519/j.issn.1000-0526.2017.07.001
Abstract:
The spatial distributions of severe convective wind (SCW) and nonsevere thunderstorms (NT) over South China, occurring between 08:00 BT and 20:00 BT during spring and summer in 2010-2014, were analyzed by using the observational data from China Meteorological Administration. And then, their environmental characteristics were compared between SCW and NT in spring and summer. It was found that SCW in summer is more frequently than that in spring and that NT in summer is about 3.6 times the counts of NT in spring. SCW events mainly concentrate in the western Guangdong to the Pearl River Delta Region. Compared to NT, SCW is generally associated with stronger baroclinity, instability and stronger dynamic forcing. The precipitable water and averaged relative humidity between 700-500 hPa of SCW tend to be higher than those of NT in spring, while the opposite is the case for the pattern in summer. In conclusion, it is obvious that the dynamic forcing for SCW in spring is much better than these in summer, while the thermal condition is more significant in summer.
The spatial distributions of severe convective wind (SCW) and nonsevere thunderstorms (NT) over South China, occurring between 08:00 BT and 20:00 BT during spring and summer in 2010-2014, were analyzed by using the observational data from China Meteorological Administration. And then, their environmental characteristics were compared between SCW and NT in spring and summer. It was found that SCW in summer is more frequently than that in spring and that NT in summer is about 3.6 times the counts of NT in spring. SCW events mainly concentrate in the western Guangdong to the Pearl River Delta Region. Compared to NT, SCW is generally associated with stronger baroclinity, instability and stronger dynamic forcing. The precipitable water and averaged relative humidity between 700-500 hPa of SCW tend to be higher than those of NT in spring, while the opposite is the case for the pattern in summer. In conclusion, it is obvious that the dynamic forcing for SCW in spring is much better than these in summer, while the thermal condition is more significant in summer.
2010,36(3):9-18, DOI: 10.7519/j.issn.1000-0526.2010.3.002
Abstract:
Potential vorticity (PV) is one of the important concepts in advanced synoptic and dynamic meteorology. This paper is a brief introduction to the theory of potential vorticity, including the concept of PV, the conservation and invertibility of PV, PV thinking, moist PV (MPV), and the application of PV theory.
Potential vorticity (PV) is one of the important concepts in advanced synoptic and dynamic meteorology. This paper is a brief introduction to the theory of potential vorticity, including the concept of PV, the conservation and invertibility of PV, PV thinking, moist PV (MPV), and the application of PV theory.
FU Jiaolan, MA Xuekuan, CHEN Tao, ZHANG Fang, ZHANG Xidi, SUN Jun, QUAN Wanqing, YANG Shunan, SHEN Xiaolin
2017,43(5):528-539, DOI: 10.7519/j.issn.1000-0526.2017.05.002
Abstract:
An extremely severe precipitation event took place in North China in 19-20 July 2016. It was characterized by large rainfall, persistent rainfall, warm cloud rainfall, strong local rainfall intensity and orographic precipitation. Its rainfall was larger than that of the extreme rainfall in 3-5 August 1996, and only next to the amount of the 2-7 August 1963 extreme rainfall event. It occurred under the circulation background of the South Asia high moving eastward, the West Pacific subtropical high moving northwestward and the low vortex in the westerlies developing in mid high latitude. The abnormal development of Huanghuai cyclone, southwest and southeast low level jets, and the abnormally abundant moisture indicates that the dynamic lifting and moisture conditions favored this severe rainfall process significantly. The whole rainfall event presented clearly the phase characteristics, and could be divided into two stages. The first stage was the orographic rainfall caused by the easterly winds ahead of the trough from the early morning to the daytime of 19 July, while the second part was produced by spiral rain bands in the north side of Huanghuai cyclone from the night of 19 to the daytime of 20 July. In the first stage, the easterly low level jet was lifted by the Taihang Mountains, which continuously triggered the convective cells along the east edge of the mountains. The weak dry and cold advection at mid level and the strong warm and wet advection at low level jointly maintained the convective instability. The cold pool generated by heavy rainfall and the mesoscale frontogenesis process created by local orographic effect provided favorable conditions for severe convections to occur continuously. The second stage rainfall was mainly related to the development of cut off vortex and Huanghuai cyclone. The blocking of the high pressure system slowed the steps of Huanghuai cyclone in North China, thus leading to the long lasting rainfall process.
An extremely severe precipitation event took place in North China in 19-20 July 2016. It was characterized by large rainfall, persistent rainfall, warm cloud rainfall, strong local rainfall intensity and orographic precipitation. Its rainfall was larger than that of the extreme rainfall in 3-5 August 1996, and only next to the amount of the 2-7 August 1963 extreme rainfall event. It occurred under the circulation background of the South Asia high moving eastward, the West Pacific subtropical high moving northwestward and the low vortex in the westerlies developing in mid high latitude. The abnormal development of Huanghuai cyclone, southwest and southeast low level jets, and the abnormally abundant moisture indicates that the dynamic lifting and moisture conditions favored this severe rainfall process significantly. The whole rainfall event presented clearly the phase characteristics, and could be divided into two stages. The first stage was the orographic rainfall caused by the easterly winds ahead of the trough from the early morning to the daytime of 19 July, while the second part was produced by spiral rain bands in the north side of Huanghuai cyclone from the night of 19 to the daytime of 20 July. In the first stage, the easterly low level jet was lifted by the Taihang Mountains, which continuously triggered the convective cells along the east edge of the mountains. The weak dry and cold advection at mid level and the strong warm and wet advection at low level jointly maintained the convective instability. The cold pool generated by heavy rainfall and the mesoscale frontogenesis process created by local orographic effect provided favorable conditions for severe convections to occur continuously. The second stage rainfall was mainly related to the development of cut off vortex and Huanghuai cyclone. The blocking of the high pressure system slowed the steps of Huanghuai cyclone in North China, thus leading to the long lasting rainfall process.
2012,38(10):1255-1266, DOI: 10.7519/j.issn.1000-0526.2012.10.012
Abstract:
Precipitation characteristics, environment conditions, generation and development of the mesoscale convective system that brought about the extreme torrential rain in Beijing on 21 July 2012 were analyzed comprehensively in this paper by using various conventional and unconventional data. The results showed that the extreme torrential rain had the characteristics of long duration, great rainfall and wide coverage area and its process consisted of warm area precipitation and frontal precipitation. The warm area rainfall started earlier, the severe precipitation center was scattered and lasted long while the frontal rainfallprocess contained several severe rainfall centers with high precipitation efficiency, lasting a short time.Environment conditions of the mesoscale convective system that triggered this extreme severe rainfall were analyzed. The results showed that interactions of high level divergence, the wind shear and convergence with the vortex in the lower troposphere and the surface wind convergence line provided favorable environment to the severe extreme rain. The warm humid airs from the tropical and sub tropical zones converged over the torrential rain region, continuous and sufficient water vapor manifested as high atmospheric column of precipitable water and strong low level water vapor convergence and other extreme vapor conditions for the torrential rain. In addition, the intense precipitation was triggered by the vortex wind shear, wind disturbance on low level jet, surface wind convergence line and the effect of terrain under the condition of the plentiful water vapour and maintained. With the cold front moved eastward, heavy frontal rainfall was brought by the development and evolution of convective system made by the cold air and the suitable vertical wind shear.Generation and development processes of the mesoscale convective system were also studied. The findings suggested that stratiform cloud precipitation and dispersed convective precipitation occurred firstly in the precipitation process. The warm and steady stratiform cloud precipitation changed to be highly organized convectional precipitation as the cold dry air invaded. Many small scale and mesoscale convective clusters developed into mesoscale convective complex (MCC), leading to the extreme severe precipitation. Since all the directions of the echo long axis, terrain and echo movement were parallel, train effect was obviously seen in the radar echo imegery during this precipitation process. Meanwhile, the radar echo had the characteristics of backward propagation and low centroid which was similar to tropical heavy rainfalls. Finally, a series of scientific problems were proposed according to the integrated analysis on the observation data of this rare torrential rain event, such as the causes for the extreme torrential rain and the extreme rich water vapor, mechanisms for the warm area torrential rain in the north of China, the mechanism for the train effect and backward propagation, mechanisms for the organization and maintenance of the convective cells, the simulation and analysis ability of the numerical models to extreme torrential rains and so on.
Precipitation characteristics, environment conditions, generation and development of the mesoscale convective system that brought about the extreme torrential rain in Beijing on 21 July 2012 were analyzed comprehensively in this paper by using various conventional and unconventional data. The results showed that the extreme torrential rain had the characteristics of long duration, great rainfall and wide coverage area and its process consisted of warm area precipitation and frontal precipitation. The warm area rainfall started earlier, the severe precipitation center was scattered and lasted long while the frontal rainfallprocess contained several severe rainfall centers with high precipitation efficiency, lasting a short time.Environment conditions of the mesoscale convective system that triggered this extreme severe rainfall were analyzed. The results showed that interactions of high level divergence, the wind shear and convergence with the vortex in the lower troposphere and the surface wind convergence line provided favorable environment to the severe extreme rain. The warm humid airs from the tropical and sub tropical zones converged over the torrential rain region, continuous and sufficient water vapor manifested as high atmospheric column of precipitable water and strong low level water vapor convergence and other extreme vapor conditions for the torrential rain. In addition, the intense precipitation was triggered by the vortex wind shear, wind disturbance on low level jet, surface wind convergence line and the effect of terrain under the condition of the plentiful water vapour and maintained. With the cold front moved eastward, heavy frontal rainfall was brought by the development and evolution of convective system made by the cold air and the suitable vertical wind shear.Generation and development processes of the mesoscale convective system were also studied. The findings suggested that stratiform cloud precipitation and dispersed convective precipitation occurred firstly in the precipitation process. The warm and steady stratiform cloud precipitation changed to be highly organized convectional precipitation as the cold dry air invaded. Many small scale and mesoscale convective clusters developed into mesoscale convective complex (MCC), leading to the extreme severe precipitation. Since all the directions of the echo long axis, terrain and echo movement were parallel, train effect was obviously seen in the radar echo imegery during this precipitation process. Meanwhile, the radar echo had the characteristics of backward propagation and low centroid which was similar to tropical heavy rainfalls. Finally, a series of scientific problems were proposed according to the integrated analysis on the observation data of this rare torrential rain event, such as the causes for the extreme torrential rain and the extreme rich water vapor, mechanisms for the warm area torrential rain in the north of China, the mechanism for the train effect and backward propagation, mechanisms for the organization and maintenance of the convective cells, the simulation and analysis ability of the numerical models to extreme torrential rains and so on.
2006,32(10):64-69, DOI: 10.7519/j.issn.1000-0526.2006.10.010
Abstract:
Based on the data of CINRAD Doppler Radar which located at Xinle of Hebei Province,the hail,strong wind and heavy rainfall weather events in mid-south Hebei in 2004 are statistically analyzed.The routine radar products,such as echo reflectivity,radial velocity,Vertically Integrated Liquid(VIL)Water,hail index,mesocyclone,velocity azimuth display wind profile,etc.are used in this statistics.The results show that hail's VIL value is larger than generic thunder storm's.At the same time,greater VIL value and longer sustaining will bring about greater diameter hail and larger effect area.It is the very useful index to indicate strong wind in mesocyclone products and the wind direction sudden change in radial velocity products.A reference based on analyzing this type synoptic forecast with radar system in future is proposed.
Based on the data of CINRAD Doppler Radar which located at Xinle of Hebei Province,the hail,strong wind and heavy rainfall weather events in mid-south Hebei in 2004 are statistically analyzed.The routine radar products,such as echo reflectivity,radial velocity,Vertically Integrated Liquid(VIL)Water,hail index,mesocyclone,velocity azimuth display wind profile,etc.are used in this statistics.The results show that hail's VIL value is larger than generic thunder storm's.At the same time,greater VIL value and longer sustaining will bring about greater diameter hail and larger effect area.It is the very useful index to indicate strong wind in mesocyclone products and the wind direction sudden change in radial velocity products.A reference based on analyzing this type synoptic forecast with radar system in future is proposed.
ZHANG Xiaoling, ZHANG Tao, LIU Xinhua, ZHOU Qingliang, CHEN Yun, ZHOU Xiaoxia, ZHENG Yongguang, ZHAO Surong
2010,36(7):143-150, DOI: 10.7519/j.issn.1000-0526.2010.7.021
Abstract:
Mesoscale severe weather forecasting ability is limited, in some sense for a lack of valid analysis on mesoscale convective systems and its favorable environments. This paper introduces the mesoscale weather chart analysis techniq ue which was tested in the National Meteorological Center (NMC). Mesoscale weath er chart analyzes the favorable environmental conditions of mesoscale convective systems based on observational data and numerical weather forecast outputs. It includes upper air composite chart and surface chart. In the upper air composite ch art, by analyzing wind, temperature, moisture, temperature change and height change, the diagnostic systems and features in all the lower, middle and upper t roposphere isobaric layers are combined into one plot, which can clearly displa y the available environments and synoptic pattern of severe convective weather. In the surface chart, the analysis contents are pressure, wind, temperature, moi sture, convective weather phenomena and all kinds of boundaries (fronts). The te st in NMC shows that mesoscale weather chart analysis is a dependable means for severe convective weather outlook forecasting.
Mesoscale severe weather forecasting ability is limited, in some sense for a lack of valid analysis on mesoscale convective systems and its favorable environments. This paper introduces the mesoscale weather chart analysis techniq ue which was tested in the National Meteorological Center (NMC). Mesoscale weath er chart analyzes the favorable environmental conditions of mesoscale convective systems based on observational data and numerical weather forecast outputs. It includes upper air composite chart and surface chart. In the upper air composite ch art, by analyzing wind, temperature, moisture, temperature change and height change, the diagnostic systems and features in all the lower, middle and upper t roposphere isobaric layers are combined into one plot, which can clearly displa y the available environments and synoptic pattern of severe convective weather. In the surface chart, the analysis contents are pressure, wind, temperature, moi sture, convective weather phenomena and all kinds of boundaries (fronts). The te st in NMC shows that mesoscale weather chart analysis is a dependable means for severe convective weather outlook forecasting.
Zhou Yuquan, Chen Yingying, Li Juan, Huang Yimei, He Xiaodong, Zhou Feifei, Wu Menxin, Hu Bo, Mao Jietai
2008,34(12):27-35, DOI: 10.7519/j.issn.1000-0526.2008.12.004
Abstract:
Cloud macro and micro physical characteristic parameters play an important role not only in the field of the analysis and forecast of the weather and climate, but also in the field of weather modification to identify the seeding c ondition. Based on the data from FY-2C/D stationary satellite and SBDART radiati on transfer model, associated with the sounding data and surface information, a method retrieving cloud macro and micro physical parameters is established in th is research. These parameters include cloud top height, cloud top temperature, d epth of super-cooled layer, depth of warm layer, cloud bottom height, depth of c loud, cloud optical thickness, cloud effective particle radius and cloud liquid water content. It has been run operationally. In this paper, the correlated info rmation such as physical meaning, retrieving method and technology, retrieving p rocess and data format are simply introduced. Furthermore, comparing with the ob servation of Cloudsat up to the minute, the retrieving results of main cloud par ameters are proved to be reasonable and usable. By contrast with same kind produ cts of MODIS, it also shows good corresponding relationship.
Cloud macro and micro physical characteristic parameters play an important role not only in the field of the analysis and forecast of the weather and climate, but also in the field of weather modification to identify the seeding c ondition. Based on the data from FY-2C/D stationary satellite and SBDART radiati on transfer model, associated with the sounding data and surface information, a method retrieving cloud macro and micro physical parameters is established in th is research. These parameters include cloud top height, cloud top temperature, d epth of super-cooled layer, depth of warm layer, cloud bottom height, depth of c loud, cloud optical thickness, cloud effective particle radius and cloud liquid water content. It has been run operationally. In this paper, the correlated info rmation such as physical meaning, retrieving method and technology, retrieving p rocess and data format are simply introduced. Furthermore, comparing with the ob servation of Cloudsat up to the minute, the retrieving results of main cloud par ameters are proved to be reasonable and usable. By contrast with same kind produ cts of MODIS, it also shows good corresponding relationship.
2014,40(4):400-411, DOI: 10.7519/j.issn.1000-0526.2014.04.002
Abstract:
Based on the synoptic environment analysis of about 100 severe convection cases in China since 2000 and the reference of related literatures, from the perspectives of the three essential conditions for the development of severe convection, namely the thermal instability, lift and moisture, five basic synoptic situation configurations of severe convection in China are proposed and expounded. They are cold advection forcing category, warm advection forcing category, baroclinic frontogenesis category, quasi barotropic category and elevated thunderstorm category. The typical characteristics of the upper cold advection forcing category is that the mid upper strong cold advection above 500 hPa strengthens and reaches the boundary warm convergence zone. The warm advection forcing category is characterized by trough with special structure moving over low level strong warm and moist advection. The deep convection produced by the mid lower layer convergence of cold and warm air features the baroclinic frontogenesis category. The quasi barotropic category mostly occurs at the northern and the southern edges or the interior of summer subtropical high and the area with weak baroclinicity, where the dynamic forcing and the surface inhomogeneous local heating play major roles. The features of elevated thunderstorms are the southwest jet in 700-500 hPa lifted by boundary cold wedge and the instable energy is from above 700 hPa. The classification based on the difference of the formation mechanisms can grasp accurately the synoptic characteristics, the situation configurations, the dynamic and thermal properties and the key points in analyzing short term potential forecast, providing more technical support to further enhance the level of weather prediction.
Based on the synoptic environment analysis of about 100 severe convection cases in China since 2000 and the reference of related literatures, from the perspectives of the three essential conditions for the development of severe convection, namely the thermal instability, lift and moisture, five basic synoptic situation configurations of severe convection in China are proposed and expounded. They are cold advection forcing category, warm advection forcing category, baroclinic frontogenesis category, quasi barotropic category and elevated thunderstorm category. The typical characteristics of the upper cold advection forcing category is that the mid upper strong cold advection above 500 hPa strengthens and reaches the boundary warm convergence zone. The warm advection forcing category is characterized by trough with special structure moving over low level strong warm and moist advection. The deep convection produced by the mid lower layer convergence of cold and warm air features the baroclinic frontogenesis category. The quasi barotropic category mostly occurs at the northern and the southern edges or the interior of summer subtropical high and the area with weak baroclinicity, where the dynamic forcing and the surface inhomogeneous local heating play major roles. The features of elevated thunderstorms are the southwest jet in 700-500 hPa lifted by boundary cold wedge and the instable energy is from above 700 hPa. The classification based on the difference of the formation mechanisms can grasp accurately the synoptic characteristics, the situation configurations, the dynamic and thermal properties and the key points in analyzing short term potential forecast, providing more technical support to further enhance the level of weather prediction.
2013,39(10):1284-1292, DOI: 10.7519/j.issn.1000-0526.2013.10.006
Abstract:
Based on the fog observation data during 24-27 December 2006 (advection radiation fog), NCEP NC reanalysis data (2.5°×2.5°) and GDAS global meteorological data (1°×1°), detailed trajectory analysis of the boundary layer characteristics and water vapor transport of the fog is investigated, combined with the weather condition, meteorological elements and physical quantity field. The results show that: (1) there is thick inversion layer, even multi layer inversion throughout the dense fog event. Temperatures of different inversion tops in the middle and high levels are 2-5℃ higher than the surface temperature. The thickness of inversion layer is more than 200 m, and it gets to 500 m at 08:00 BT 26 December, indicating the atmosphere is very stable and conducive to the convergence of water vapor before the fog forms. However, it is not favorable for the divergence of water vapor after the formation of fog, which helps the development and maintenance of the fog, causing the fog to last about 64 hours with dense fog (visibility <50 m) about 37 hours; (2) The divergence of water vapor flux in low level is negative in the advection fog event. The upper air has persistent moisture convergence and the strongest moisture convergence appears at 02:00 BT 25 December, being -30×10-7 g·s-1·cm-2·hPa-1. The accumulation of low level water vapor makes fog form and develop while the divergence of water vapor flux speeds up its dissipation. 〖JP2〗The long lasting advection radiation fog is mainly caused by the continuous water vapor convergence; (3) The water vapor path is from the coastal area in easten China to Nanjing. The water vapor is continuously supplied from sea during the fog event, with the water vapor flux maximum getting to 2 g·s-1·hPa-1·cm-1. The sufficient supply and supplementary of water vapor determines the duration of the fog.
Based on the fog observation data during 24-27 December 2006 (advection radiation fog), NCEP NC reanalysis data (2.5°×2.5°) and GDAS global meteorological data (1°×1°), detailed trajectory analysis of the boundary layer characteristics and water vapor transport of the fog is investigated, combined with the weather condition, meteorological elements and physical quantity field. The results show that: (1) there is thick inversion layer, even multi layer inversion throughout the dense fog event. Temperatures of different inversion tops in the middle and high levels are 2-5℃ higher than the surface temperature. The thickness of inversion layer is more than 200 m, and it gets to 500 m at 08:00 BT 26 December, indicating the atmosphere is very stable and conducive to the convergence of water vapor before the fog forms. However, it is not favorable for the divergence of water vapor after the formation of fog, which helps the development and maintenance of the fog, causing the fog to last about 64 hours with dense fog (visibility <50 m) about 37 hours; (2) The divergence of water vapor flux in low level is negative in the advection fog event. The upper air has persistent moisture convergence and the strongest moisture convergence appears at 02:00 BT 25 December, being -30×10-7 g·s-1·cm-2·hPa-1. The accumulation of low level water vapor makes fog form and develop while the divergence of water vapor flux speeds up its dissipation. 〖JP2〗The long lasting advection radiation fog is mainly caused by the continuous water vapor convergence; (3) The water vapor path is from the coastal area in easten China to Nanjing. The water vapor is continuously supplied from sea during the fog event, with the water vapor flux maximum getting to 2 g·s-1·hPa-1·cm-1. The sufficient supply and supplementary of water vapor determines the duration of the fog.
2015,41(2):212-218, DOI: 10.7519/j.issn.1000-0526.2015.02.009
Abstract:
From 1 May to 8 June 2013 CMA Meteorological Observation Centre conducted an experiment of cloud height observations by using cloud radar (35 GHz), whose observation data are the echo power value and temporal resolution is 1 min and a ceilometer whose observation data are the back scattering intens data with 1 min temporal resolution. The result of analyzing the data observed from the 39 d experiment indicates that: (1) the data acquisition ratio of cloud radar is 26% larger than that of ceilometer; (2) the ratio is 51% in fog haze weather; (3) relatively, precipitation has more significant effect on cloud base height measured by laser ceilometer than that by cloud radar; (4) height of cloud base measured by cloud radar is almost consistent with the height by ceilometer because their average deviation is less than 300 m.
From 1 May to 8 June 2013 CMA Meteorological Observation Centre conducted an experiment of cloud height observations by using cloud radar (35 GHz), whose observation data are the echo power value and temporal resolution is 1 min and a ceilometer whose observation data are the back scattering intens data with 1 min temporal resolution. The result of analyzing the data observed from the 39 d experiment indicates that: (1) the data acquisition ratio of cloud radar is 26% larger than that of ceilometer; (2) the ratio is 51% in fog haze weather; (3) relatively, precipitation has more significant effect on cloud base height measured by laser ceilometer than that by cloud radar; (4) height of cloud base measured by cloud radar is almost consistent with the height by ceilometer because their average deviation is less than 300 m.
2012,38(2):164-173, DOI: 10.7519/j.issn.1000-0526.2012.02.004
Abstract:
Many weather forecasters seem to have acquaintance with most of basic concepts or fundamental theories which are connected with severe convection, but some of them are misapplied frequently by some forecasters when they are engaged in severe convective weather analysis or forecasting argumentation. Due to the above problem, some basic concepts and fundamental theories should be explained from the view of forecasting application. The following issues are discussed in this paper. They are the relationship between humidity and water vapor content, the role of clod air during the precipitation process, the fundamental theories connected with thermal and dynamic instability, the sounding analysis related to instability parameters, the relationship between helicity or moist potential vorticity and instability, the relationship among the convergence line, lifting velocity and convective vertical movement, and the essential connection between the synoptic patterns and severe convective phenomena.
Many weather forecasters seem to have acquaintance with most of basic concepts or fundamental theories which are connected with severe convection, but some of them are misapplied frequently by some forecasters when they are engaged in severe convective weather analysis or forecasting argumentation. Due to the above problem, some basic concepts and fundamental theories should be explained from the view of forecasting application. The following issues are discussed in this paper. They are the relationship between humidity and water vapor content, the role of clod air during the precipitation process, the fundamental theories connected with thermal and dynamic instability, the sounding analysis related to instability parameters, the relationship between helicity or moist potential vorticity and instability, the relationship among the convergence line, lifting velocity and convective vertical movement, and the essential connection between the synoptic patterns and severe convective phenomena.
2014,40(2):133-145, DOI: 10.7519/j.issn.1000-0526.2014.02.001
Abstract:
By using the NCEP reanalysis data, the vapor budget of the area covered by the severe torrential rain over the northeast of North China on 21 July, 2012 is calculated according to the vapor budget equation. The results show that meridional water vapor transportation is dominant while the extremely heavy rain hits Beijing Region, where most moist vapor comes from the southern boundary below 500 hPa. The low level regional moisture convergence is consistent with the time and space when the torrential rain breaks out and develops. Above the middle level the vertical vapor transport is more prominent. Then the variation features of the vapor transport corridors and their moisture contributions are got through the HYSPLIT mode. The backward trajectory analyses illustrate two major vapor transport corridors. The moistest vapor derived from Yellow Sea and East China Sea along the low level make the main moisture contribution during the heavy precipitation. Moisture from the South China Sea and the Bay of Bengal strengthens the water vapor in the region when the heavy rain starts and develops. Also the drier vapor corridor along the high level from the northwest of China plays an important role in this case.
By using the NCEP reanalysis data, the vapor budget of the area covered by the severe torrential rain over the northeast of North China on 21 July, 2012 is calculated according to the vapor budget equation. The results show that meridional water vapor transportation is dominant while the extremely heavy rain hits Beijing Region, where most moist vapor comes from the southern boundary below 500 hPa. The low level regional moisture convergence is consistent with the time and space when the torrential rain breaks out and develops. Above the middle level the vertical vapor transport is more prominent. Then the variation features of the vapor transport corridors and their moisture contributions are got through the HYSPLIT mode. The backward trajectory analyses illustrate two major vapor transport corridors. The moistest vapor derived from Yellow Sea and East China Sea along the low level make the main moisture contribution during the heavy precipitation. Moisture from the South China Sea and the Bay of Bengal strengthens the water vapor in the region when the heavy rain starts and develops. Also the drier vapor corridor along the high level from the northwest of China plays an important role in this case.
2014,40(4):389-399, DOI: 10.7519/j.issn.1000-0526.2014.04.001
Abstract:
Thunderstorm potential forecasting based on three ingredients has been widely accepted. This article aims to discuss some basical questions in operational forecast applications, and clarify some easily confused concepts. The content includes atmospheric instablility and convection, thunderstorms trigger mechanism and lifting and its relationship with snoptical weather system, how to deal with the three elements of the thunderstorm “enough”, the combination of pattern recognition and ingredients based forecasting methodology. Atmospheric instablility is one of the three ingredients of convection initiation, and it is also very important to thunderstorm short time forecasting and analysis. This paper discusses various mesoscale instability related to the thunderstorm, and inicates how to estimate the spatial and temporal evolution of CAPE. In addition, the definition and criterion for potential instability and symmetric instability are discussed profoundly.
Thunderstorm potential forecasting based on three ingredients has been widely accepted. This article aims to discuss some basical questions in operational forecast applications, and clarify some easily confused concepts. The content includes atmospheric instablility and convection, thunderstorms trigger mechanism and lifting and its relationship with snoptical weather system, how to deal with the three elements of the thunderstorm “enough”, the combination of pattern recognition and ingredients based forecasting methodology. Atmospheric instablility is one of the three ingredients of convection initiation, and it is also very important to thunderstorm short time forecasting and analysis. This paper discusses various mesoscale instability related to the thunderstorm, and inicates how to estimate the spatial and temporal evolution of CAPE. In addition, the definition and criterion for potential instability and symmetric instability are discussed profoundly.
2009,35(1):55-64, DOI: 10.7519/j.issn.1000-0526.2009.1.007
Abstract:
A strong rainstorm is analysis which occurred in Xinghua located the north of Ji angsu province on 25 July 2007. Results show that wind disaster originated from two kinds of rainstorm. One kind was the gust front which occurred at the front of the storm. Strong wind of grade 7-9 was attained when it happened. Another ki nd was the downburst arose in the multi cell storm. The original height of refl ectivity core was higher than -20℃ isotherm. It had the characteristics of conv ergence on the mid level and descending of reflectivity core. The strong wind ab ove grade 10 was attained, when the descending airflow diverged strongly on the ground. A new cell was combined with the former storm above the gust front, thus the storm enhanced. When the downburst happened, the storm weakened, and another new cell was combin ed with the former storm. The downburst happened continuously, and the impact of gust front persisted.
A strong rainstorm is analysis which occurred in Xinghua located the north of Ji angsu province on 25 July 2007. Results show that wind disaster originated from two kinds of rainstorm. One kind was the gust front which occurred at the front of the storm. Strong wind of grade 7-9 was attained when it happened. Another ki nd was the downburst arose in the multi cell storm. The original height of refl ectivity core was higher than -20℃ isotherm. It had the characteristics of conv ergence on the mid level and descending of reflectivity core. The strong wind ab ove grade 10 was attained, when the descending airflow diverged strongly on the ground. A new cell was combined with the former storm above the gust front, thus the storm enhanced. When the downburst happened, the storm weakened, and another new cell was combin ed with the former storm. The downburst happened continuously, and the impact of gust front persisted.
2012,38(1):1-16, DOI: 10.7519/j.issn.1000-0526.2012.01.001
Abstract:
In this paper, the modulation of atmospheric MJO on typhoon generation over the northwestern Pacific and its mechanism are first studied by using the MJO index. The results show that the MJO plays an important modulation role in typhoon generation over the northwestern Pacific: The proportion of typhoon number is 21 between active period and inactive period; During the MJO active period, the proportion of typhoon number is also 2:1 between phases 5-6 and phases 2-3 of MJO. The composite analyses of atmospheric circulation show that there are different circulation patterns over the northwestern Pacific in different phases of the MJO, which will affect the typhoon generation. In phases 5-6 (2-3), the dynamic factor and convective heating patterns over western Pacific are favorable (unfavorable) for typhoon generation. Then， the comparing analyses of the 30-60 day low frequency kinetic energy in lower and higher levels of the troposphere show that the atmospheric intraseasonal oscillation over the northwestern Pacific has a clear impact on the typhoon generation. There is an evident positive (negative) anomaly area of 30-60 day low frequency kinetic energy in the more (less) typhoon years over the northwestern Pacific east of the Philippines, which means that strong (weak) atmospheric intraseasonal oscillation (ISO) over the northwestern Pacific is favorable (unfavorable) for typhoon generation. The analyses of 200 hPa velocity potential show that there is a clear divergence (convergence) pattern over the northwestern Pacific in the more (less) typhoon years, which is favorable (unfavorable) for typhoon generation. The modulation of the intraseasonal oscillation on the typhoon tracks over the northwestern Pacific is studied by observational data analyses. We classified the main classes of typhoon tracks into 5 types as straight west moving typhoons (I), northwest moving typhoons (II), recurving to Korea/west of Japan typhoons (III), landing on Japan typhoons (IV) and recurving to the east of Japan typhoons (V). Then the composite analyses of atmospheric low-frequency wind fields at 850, 500 and 200 hPa, corresponding to the typhoon forming date, for every typhoon track are completed. The analysis results of relationships between the low-frequency (ISO) wind fields and typhoon tracks have indicated that the typhoon tracks will be affected by wind pattern of the ISO. The low frequency positive vorticity belt (the maximum value line of cyclonic vorticity) associated with low-frequency cyclone (LFC) at 850 hPa is so closely related to the typhoon track, that the maximum value line (belt) of low frequency cyclonic vorticity can be an important factor to predicate the typhoon tracks over the northwestern Pacific. And the typhoon tracks will be also affected by the ISO circulation pattern at 200 hPa, particularly the strong low frequency wind associated with low frequency anticyclone (LFAC).
In this paper, the modulation of atmospheric MJO on typhoon generation over the northwestern Pacific and its mechanism are first studied by using the MJO index. The results show that the MJO plays an important modulation role in typhoon generation over the northwestern Pacific: The proportion of typhoon number is 21 between active period and inactive period; During the MJO active period, the proportion of typhoon number is also 2:1 between phases 5-6 and phases 2-3 of MJO. The composite analyses of atmospheric circulation show that there are different circulation patterns over the northwestern Pacific in different phases of the MJO, which will affect the typhoon generation. In phases 5-6 (2-3), the dynamic factor and convective heating patterns over western Pacific are favorable (unfavorable) for typhoon generation. Then， the comparing analyses of the 30-60 day low frequency kinetic energy in lower and higher levels of the troposphere show that the atmospheric intraseasonal oscillation over the northwestern Pacific has a clear impact on the typhoon generation. There is an evident positive (negative) anomaly area of 30-60 day low frequency kinetic energy in the more (less) typhoon years over the northwestern Pacific east of the Philippines, which means that strong (weak) atmospheric intraseasonal oscillation (ISO) over the northwestern Pacific is favorable (unfavorable) for typhoon generation. The analyses of 200 hPa velocity potential show that there is a clear divergence (convergence) pattern over the northwestern Pacific in the more (less) typhoon years, which is favorable (unfavorable) for typhoon generation. The modulation of the intraseasonal oscillation on the typhoon tracks over the northwestern Pacific is studied by observational data analyses. We classified the main classes of typhoon tracks into 5 types as straight west moving typhoons (I), northwest moving typhoons (II), recurving to Korea/west of Japan typhoons (III), landing on Japan typhoons (IV) and recurving to the east of Japan typhoons (V). Then the composite analyses of atmospheric low-frequency wind fields at 850, 500 and 200 hPa, corresponding to the typhoon forming date, for every typhoon track are completed. The analysis results of relationships between the low-frequency (ISO) wind fields and typhoon tracks have indicated that the typhoon tracks will be affected by wind pattern of the ISO. The low frequency positive vorticity belt (the maximum value line of cyclonic vorticity) associated with low-frequency cyclone (LFC) at 850 hPa is so closely related to the typhoon track, that the maximum value line (belt) of low frequency cyclonic vorticity can be an important factor to predicate the typhoon tracks over the northwestern Pacific. And the typhoon tracks will be also affected by the ISO circulation pattern at 200 hPa, particularly the strong low frequency wind associated with low frequency anticyclone (LFAC).
2014,40(11):1372-1379, DOI: 10.7519/j.issn.1000-0526.2014.11.010
Abstract:
The hourly precipitation observation data from 2447 rain gauge records are used to verify and evaluate the satellite precipitation estimation products of Climate Prediction Center Morphing Technique (CMORPH) and Tropical Rainfall Measuring Mission (TRMM) 3B42 during 2007-2010 in China. The results show that the two satellite precipitation data are similar to ground gain gauge data in revealing the spatial patterns of daily mean precipitation amount. The pattern correlation coefficients of 3 h rainfall amount are over 0.5 and 0.4 in most areas, respectively. Bias of two satellite precipitation products are both between the positive and negative 0.25 mm, but there are significant difference between the north and the south. And the mean absolute error, relative error and root mean square error all have a significant seasonal periodic variations. The two satellite precipitation products can reflect the summer rainfall diurnal variation well in most parts of China, but there are also clear distinctions in some areas. The overall vacancy retrieval rate of CMORPH and TRMM 3B42 products are 7.23% and 2.63%, overall missing retrieval rate are 3.25% and 5.5%, respectively.
The hourly precipitation observation data from 2447 rain gauge records are used to verify and evaluate the satellite precipitation estimation products of Climate Prediction Center Morphing Technique (CMORPH) and Tropical Rainfall Measuring Mission (TRMM) 3B42 during 2007-2010 in China. The results show that the two satellite precipitation data are similar to ground gain gauge data in revealing the spatial patterns of daily mean precipitation amount. The pattern correlation coefficients of 3 h rainfall amount are over 0.5 and 0.4 in most areas, respectively. Bias of two satellite precipitation products are both between the positive and negative 0.25 mm, but there are significant difference between the north and the south. And the mean absolute error, relative error and root mean square error all have a significant seasonal periodic variations. The two satellite precipitation products can reflect the summer rainfall diurnal variation well in most parts of China, but there are also clear distinctions in some areas. The overall vacancy retrieval rate of CMORPH and TRMM 3B42 products are 7.23% and 2.63%, overall missing retrieval rate are 3.25% and 5.5%, respectively.
2014,40(7):816-826, DOI: 10.7519/j.issn.1000-0526.2014.07.005
Abstract:
In term of precipitation data of 2400 stations from 1981 to 2010, annual, seasonal and monthly distribution and evolution characteristics of rainstorm were analyzed. The results show that the processes of rainstorm have been increased evidently since 21 century especially in the south of China, but the duration is relatively short. Rainstorm days have been increased, but the amount of precipitation is not as much as in 1990s. Variation trend of the annual (monthly) precipitation amount is in accordance with that of rainstorm days, but rainfall is averagely more while the rainstorm days are less during spring rainfall phase over the south of Yangtze River. Distribution of the maximum annual rainstorm days is very similar with that of the annual mean rainstorm days, revealing the feature of more in south and east but less in north and west. Maximum annual rainstorm days are more than double of annual average rainstorm days with multi centers due to the effect of topography. The months of maximum monthly rainstorm days over different regions of the same province are incompletely same as the result of the impact of different weather systems. Generally, rainstorm days have been increased since 2000, rainstorm begins earlier, ends latter and lasts longer than before. Nowadays, as the extreme rainfall events and secondary disasters happen frequently, it is conducive for the forecast of quantitative precipitation forecast (QPF) to learn the spatio temporal distribution and evolution features of rainstorm.
In term of precipitation data of 2400 stations from 1981 to 2010, annual, seasonal and monthly distribution and evolution characteristics of rainstorm were analyzed. The results show that the processes of rainstorm have been increased evidently since 21 century especially in the south of China, but the duration is relatively short. Rainstorm days have been increased, but the amount of precipitation is not as much as in 1990s. Variation trend of the annual (monthly) precipitation amount is in accordance with that of rainstorm days, but rainfall is averagely more while the rainstorm days are less during spring rainfall phase over the south of Yangtze River. Distribution of the maximum annual rainstorm days is very similar with that of the annual mean rainstorm days, revealing the feature of more in south and east but less in north and west. Maximum annual rainstorm days are more than double of annual average rainstorm days with multi centers due to the effect of topography. The months of maximum monthly rainstorm days over different regions of the same province are incompletely same as the result of the impact of different weather systems. Generally, rainstorm days have been increased since 2000, rainstorm begins earlier, ends latter and lasts longer than before. Nowadays, as the extreme rainfall events and secondary disasters happen frequently, it is conducive for the forecast of quantitative precipitation forecast (QPF) to learn the spatio temporal distribution and evolution features of rainstorm.
2011,37(10):1262-1269, DOI: 10.7519/j.issn.1000-0526.2011.10.009
Abstract:
Based on the daily precipitation data at 110 observational stations during 1961-2008 in South China， the climatic characteristics and variation of torrential rain days， rainstorm intensity and contribution which is in annual， the first and second flood seasons in South China were studied by using statistical and diagnostic methods， such as linear regression analysis， Mann Kendall test， wavelet analysis and the computation of trend coefficients. The results have shown that the annual mean torrential rain days have a decreasing trend from coastal regions to inland in South China in recent 48 years， the highest center is in Dongxing of Guangxi （14.9 d）， and the lowest center is in Longlin of Guangxi （3.2 d）. About 72% of the total torrential rain days occurred in the flood seasons with about 45% in the first season and 27% in the second season. The mean torrential rain days have increased faintly in annual， the first and second flood seasons in South China， but it is not obvious. There are the characteristics of interannual and interdecadal changes. The mean rainstorm intensity has increased faintly in annual and in the first flood season in South China. However, since 2005 it has become obviously. The mean rainstorm intensity has declined in the second flood season， but it is not obvious. The annual mean rainstorm contribution to the total rainfall has increased obviously， but the mean contribution is not obvious in the first and second flood seasons. The wavelet analysis has shown that the changes of torrential rain days， intensity and contribution which is in annual， the first and second flood seasons in South China have two significant periods of 2-3 a and 3-4 a.
Based on the daily precipitation data at 110 observational stations during 1961-2008 in South China， the climatic characteristics and variation of torrential rain days， rainstorm intensity and contribution which is in annual， the first and second flood seasons in South China were studied by using statistical and diagnostic methods， such as linear regression analysis， Mann Kendall test， wavelet analysis and the computation of trend coefficients. The results have shown that the annual mean torrential rain days have a decreasing trend from coastal regions to inland in South China in recent 48 years， the highest center is in Dongxing of Guangxi （14.9 d）， and the lowest center is in Longlin of Guangxi （3.2 d）. About 72% of the total torrential rain days occurred in the flood seasons with about 45% in the first season and 27% in the second season. The mean torrential rain days have increased faintly in annual， the first and second flood seasons in South China， but it is not obvious. There are the characteristics of interannual and interdecadal changes. The mean rainstorm intensity has increased faintly in annual and in the first flood season in South China. However, since 2005 it has become obviously. The mean rainstorm intensity has declined in the second flood season， but it is not obvious. The annual mean rainstorm contribution to the total rainfall has increased obviously， but the mean contribution is not obvious in the first and second flood seasons. The wavelet analysis has shown that the changes of torrential rain days， intensity and contribution which is in annual， the first and second flood seasons in South China have two significant periods of 2-3 a and 3-4 a.
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