ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 52,Issue 4,2026 Table of Contents
2026, 52(4):385-400.
DOI: 10.7519/j.issn.1000-0526.2026.020601
Abstract:
Aviation turbulence is one of the most prevalent phenomena affecting aircraft flight safety, characterized by complex causes and significant difficulties in detecting and warning. Based on a systematic review of the research progress at home and abroad, we conduct a comprehensive commentary on the following four key aspects: the definition and causes of aircraft turbulence, the turbulence indices, the impact of turbulence on flight, and the turbulence detection and warning technology. It is found that there have been great research achievements made, which are helpful for understanding the formation of aircraft turbulence and enhancing the capability of detecting and forecasting the turbulence, but many scientific challenges still remain. The challenges include the in-depth exploration of multi-domain interaction mechanisms of aircraft turbulence formation, the application and development of new technologies such as aircraft turbulence detection, perception and multi-source data fusion, artificial intelligence, etc. This is expected to provide some useful and essential references for scholars and technical professionals in aviation operation, aircraft design, aviation meteorology, and other related fields.
Aviation turbulence is one of the most prevalent phenomena affecting aircraft flight safety, characterized by complex causes and significant difficulties in detecting and warning. Based on a systematic review of the research progress at home and abroad, we conduct a comprehensive commentary on the following four key aspects: the definition and causes of aircraft turbulence, the turbulence indices, the impact of turbulence on flight, and the turbulence detection and warning technology. It is found that there have been great research achievements made, which are helpful for understanding the formation of aircraft turbulence and enhancing the capability of detecting and forecasting the turbulence, but many scientific challenges still remain. The challenges include the in-depth exploration of multi-domain interaction mechanisms of aircraft turbulence formation, the application and development of new technologies such as aircraft turbulence detection, perception and multi-source data fusion, artificial intelligence, etc. This is expected to provide some useful and essential references for scholars and technical professionals in aviation operation, aircraft design, aviation meteorology, and other related fields.
2026, 52(4):401-414.
DOI: 10.7519/j.issn.1000-0526.2025.083001
Abstract:
Using dynamic synthesis method, this paper comparatively analyzes the reasons for heavy rainfall induced by tropical cyclones passing through Jiangsu Province with and without the influence of cold air. A meteorological model of heavy rainfall associated with tropical cyclones in Jiangsu is established. The results are that, regardless of the influence of cold air, tropical cyclones having moved into Jiangsu Province can induce heavy rainfall. The key reasons include the evolution of tropical cyclone’s asymmetric structure, the continuous water vapor transport by low-level jet streams, and the sustained strong upward motion near the tropical cyclone. The differences between the situations with and without the influence of cold air mainly lie in the maintenance mechanisms of dynamic uplift and the developing means of atmospheric instability. When cold air is present, baroclinicity and conditional instability within the peripheral circulation of tropical cyclone intensify rapidly. Slantwise ascending motion in the baroclinic zone promotes the sustained lifting of warm-moist air and enhances convective development, with heavy rainfall primarily located on the northern to northeastern side of tropical cyclone. When cold air is absent, continuous transport of warm-moist air by low-level and ultra-low-level jets significantly enhances the conditional instability near and around the center of tropical cyclone. The deep vertical updraft, which is maintained by convergence within the warm sector and further enhanced by latent heat release, favors the development of convective precipitation, with heavy rainfall mainly existing on the eastern to northeastern side of tropical cyclone.
Using dynamic synthesis method, this paper comparatively analyzes the reasons for heavy rainfall induced by tropical cyclones passing through Jiangsu Province with and without the influence of cold air. A meteorological model of heavy rainfall associated with tropical cyclones in Jiangsu is established. The results are that, regardless of the influence of cold air, tropical cyclones having moved into Jiangsu Province can induce heavy rainfall. The key reasons include the evolution of tropical cyclone’s asymmetric structure, the continuous water vapor transport by low-level jet streams, and the sustained strong upward motion near the tropical cyclone. The differences between the situations with and without the influence of cold air mainly lie in the maintenance mechanisms of dynamic uplift and the developing means of atmospheric instability. When cold air is present, baroclinicity and conditional instability within the peripheral circulation of tropical cyclone intensify rapidly. Slantwise ascending motion in the baroclinic zone promotes the sustained lifting of warm-moist air and enhances convective development, with heavy rainfall primarily located on the northern to northeastern side of tropical cyclone. When cold air is absent, continuous transport of warm-moist air by low-level and ultra-low-level jets significantly enhances the conditional instability near and around the center of tropical cyclone. The deep vertical updraft, which is maintained by convergence within the warm sector and further enhanced by latent heat release, favors the development of convective precipitation, with heavy rainfall mainly existing on the eastern to northeastern side of tropical cyclone.
3 A Monitoring and Early Warning Method Based on Regional Radar Network for Severe Convective Weather
2026, 52(4):415-431.
DOI: 10.7519/j.issn.1000-0526.2025.071001
Abstract:
Based on 7 S-band dual-polarization weather radars in Ningbo and its surrounding areas, the paper develops a monitoring and early warning method of regional radar network for severe convective weather. Then, the novel method is quantitatively evaluated through 43 cases of severe convective weather. The result shows that the amount of data provided per minute by the regional radar network is about 2.66 times that of the single-site radar in Ningbo. And the radar networt can provide more information for the observation of boundary layers below 1 km. The novel method can respond promptly to severe convective weather in the monitored area. Hail warning and downburst warning can be advanced about 79.0 min and 42.6 min ahead of the observation, respectively. Compared to the single-site radar station in Ningbo, the monitoring and early warning method of regional radar network can identify the convective initiation about 4.0 min in advance, and warn hail and downburst processes about 12.2 min and 13.3 min in advance, respectively. At the same time, the early warning hit rate of the regional radar network for downburst triggered by γ-MCS reaches 100%. The method can be applied to all the central-east part of China and a small portion of the western and northeastern regions, covering approximately 44 000 km2 area. Especially, the method could have a good application potential in the Yangtze River Delta, the area from Hubei to Guangzhou and the area from Anhui to Shandong.
Based on 7 S-band dual-polarization weather radars in Ningbo and its surrounding areas, the paper develops a monitoring and early warning method of regional radar network for severe convective weather. Then, the novel method is quantitatively evaluated through 43 cases of severe convective weather. The result shows that the amount of data provided per minute by the regional radar network is about 2.66 times that of the single-site radar in Ningbo. And the radar networt can provide more information for the observation of boundary layers below 1 km. The novel method can respond promptly to severe convective weather in the monitored area. Hail warning and downburst warning can be advanced about 79.0 min and 42.6 min ahead of the observation, respectively. Compared to the single-site radar station in Ningbo, the monitoring and early warning method of regional radar network can identify the convective initiation about 4.0 min in advance, and warn hail and downburst processes about 12.2 min and 13.3 min in advance, respectively. At the same time, the early warning hit rate of the regional radar network for downburst triggered by γ-MCS reaches 100%. The method can be applied to all the central-east part of China and a small portion of the western and northeastern regions, covering approximately 44 000 km2 area. Especially, the method could have a good application potential in the Yangtze River Delta, the area from Hubei to Guangzhou and the area from Anhui to Shandong.
2026, 52(4):432-442.
DOI: 10.7519/j.issn.1000-0526.2026.010601
Abstract:
For basins characterized by long-sequence precipitation data but complex runoff generation mechanisms and a lack of flow process data, this study employed the rational formula method to estimate the early warning time duration for flood risk in small- and medium-sized rivers. Moreover, the Pearson Type Ⅲ frequency curve method, which is commonly used in hydrological frequency analysis, was applied to construct flood-triggering critical areal rainfall thresholds. Then, these thresholds were corrected based on nearly 10 years of flood peak discharge data from small- and medium-sized rivers in the middle reaches of Yellow River. Finally, this methodology was applied and tested in 15 flood events occurring from 2014 to 2024 within the upstream basin of the Dongwan Hydrological Station of the Yihe River, which lacked the measured hydrological data. The results demonstrated that correcting the critical thresholds based on the historical flood data and the same frequency method can significantly improve the hit rate of risk warnings to 71.8%, reducing the missing rate and false alarm rate to 20.0% and 29.4%, respectively. Its forecast accuracy is comparable to the current flood forecast standards in northern China. This method also performs well when applied in basins lacking hydrological characteristic values. Overall, the flood risk early warning method for small- and medium-sized rivers, corrected by the same frequency method, can effectively address the challenge of obtaining long-sequence hydrological data across different regions. It also fully leverages the advantage of meteorological departments that possess long-term precipitation records. This methodology can be further extended to small- and medium-sized watersheds without hydrological stations, and provide valuable technical references for meteorological flood disaster warning efforts in similar basins. Future work could involve classifying small- and medium-sized basins based on underlying surface conditions or establishing distinct correction models for basins dominated by saturation-excess runoff by categorizing soil moisture levels, so as to further enhance risk warning accuracy.
For basins characterized by long-sequence precipitation data but complex runoff generation mechanisms and a lack of flow process data, this study employed the rational formula method to estimate the early warning time duration for flood risk in small- and medium-sized rivers. Moreover, the Pearson Type Ⅲ frequency curve method, which is commonly used in hydrological frequency analysis, was applied to construct flood-triggering critical areal rainfall thresholds. Then, these thresholds were corrected based on nearly 10 years of flood peak discharge data from small- and medium-sized rivers in the middle reaches of Yellow River. Finally, this methodology was applied and tested in 15 flood events occurring from 2014 to 2024 within the upstream basin of the Dongwan Hydrological Station of the Yihe River, which lacked the measured hydrological data. The results demonstrated that correcting the critical thresholds based on the historical flood data and the same frequency method can significantly improve the hit rate of risk warnings to 71.8%, reducing the missing rate and false alarm rate to 20.0% and 29.4%, respectively. Its forecast accuracy is comparable to the current flood forecast standards in northern China. This method also performs well when applied in basins lacking hydrological characteristic values. Overall, the flood risk early warning method for small- and medium-sized rivers, corrected by the same frequency method, can effectively address the challenge of obtaining long-sequence hydrological data across different regions. It also fully leverages the advantage of meteorological departments that possess long-term precipitation records. This methodology can be further extended to small- and medium-sized watersheds without hydrological stations, and provide valuable technical references for meteorological flood disaster warning efforts in similar basins. Future work could involve classifying small- and medium-sized basins based on underlying surface conditions or establishing distinct correction models for basins dominated by saturation-excess runoff by categorizing soil moisture levels, so as to further enhance risk warning accuracy.
2026, 52(4):443-453.
DOI: 10.7519/j.issn.1000-0526.2026.022701
Abstract:
To enhance the forecast accuracy of average wind speed and direction, based on the 2 min average wind speed and direction data from surface observations provided by China Meteorological Administration from January 2021 to December 2022, as well as the 10 m wind forecasts from the ECMWF deterministic model at 24 h lead time, a two-step bias correction method tailored for multi-site wind speed and direction forecasts is developed for 662 national observation stations that are located within the range of 30°-40°N and 110°-120°E. Then this method is validated by using data from January to December 2023. The results show that proper correction of the u and v winds forecasted by model can effectively improve wind speed and direction forecast performance at individual stations. However, significant discrepancies exist in the u and v wind forecast errors among stations, and joint modeling of u and v winds tends to compound errors in wind speed and direction forecasts. Constructing simple linear regression correction models separately for u and v winds at each station can improve wind direction forecasts at most stations, but the correction capability for strong wind speeds (≥10 m·s-1) remains limited. To overcome this shortcoming, a quantile matching approach, i.e., the two step bias correction method, is applied to further correct the wind speed derived from the regression adjusted u and v winds. The validation results show that the two step bias correction method performs well in both wind speed and direction forecasting. Compared with the ECMWF model, the corrected root mean square error by this method is reduced by 18.8% for all wind speeds and by 29.6% for strong wind speeds. Moreover, this method also exhibits distinct advantages in forecasting strong winds associated with cold air and typhoons.
To enhance the forecast accuracy of average wind speed and direction, based on the 2 min average wind speed and direction data from surface observations provided by China Meteorological Administration from January 2021 to December 2022, as well as the 10 m wind forecasts from the ECMWF deterministic model at 24 h lead time, a two-step bias correction method tailored for multi-site wind speed and direction forecasts is developed for 662 national observation stations that are located within the range of 30°-40°N and 110°-120°E. Then this method is validated by using data from January to December 2023. The results show that proper correction of the u and v winds forecasted by model can effectively improve wind speed and direction forecast performance at individual stations. However, significant discrepancies exist in the u and v wind forecast errors among stations, and joint modeling of u and v winds tends to compound errors in wind speed and direction forecasts. Constructing simple linear regression correction models separately for u and v winds at each station can improve wind direction forecasts at most stations, but the correction capability for strong wind speeds (≥10 m·s-1) remains limited. To overcome this shortcoming, a quantile matching approach, i.e., the two step bias correction method, is applied to further correct the wind speed derived from the regression adjusted u and v winds. The validation results show that the two step bias correction method performs well in both wind speed and direction forecasting. Compared with the ECMWF model, the corrected root mean square error by this method is reduced by 18.8% for all wind speeds and by 29.6% for strong wind speeds. Moreover, this method also exhibits distinct advantages in forecasting strong winds associated with cold air and typhoons.
LUO Ling
,
ZHANG Zhicha
,
ZHAO Junping
,
CHEN Lie
,
HUANG Xuanxuan
,
WANG Liying
,
LI Wenjuan
,
LUO Ran
,
PENG Xiayun
,
HUANG Juan
2026, 52(4):454-464.
DOI: 10.7519/j.issn.1000-0526.2025.061101
Abstract:
The serious imbalance in sample distribution, characterized by a sharp drop in the frequency of severe convective winds with increasing wind speeds, is identified as the predominant factor hindering the accurate intensity-based classification of severe convective winds by various existing algorithms.To address this problem, in this study the non-differentiable probability of detection (POD) is proposed to be the loss function for a convolutional neural network (CNN) and Bias to be its constraint condition. Subsequently, the multi-objective optimization immune evolution algorithm (MOIEA) is employed to optimize all the model parameters of the CNN. This contributes to the development of a novel identification algorithm, which is named severe convective wind identification network (SCWINet), for identifying severe convective winds at the speeds of 17.2, 20.8, 24.5 m·s-1 and above. SCWINet leverages the radar vertical liquid water content, three-dimensional radar reflectivity, lightning location data and minutely surface automatic observation station data in Zhejiang Province during 2022-2024, achieving different levels of severe convective wind identification with temporal resolution of 6 minutes and spatial resolution of 0.01°. Then, the performance of SCWINet is compared to weighted mean-square error (WMSE) and balanced mean-square error (BMSE), which use the same CNN structure but have differentiable loss functions. The applicability of SCWINet is then assessed based on the threat score, Bias, POD, false alarm ratio that uses the neighborhood method (with a scanning radius of 5 km), and the planar distribution characteristics of severe convective winds. The main results are as follows. SCWINet can effectively identify severe convective winds of 17.2, 20.8, 24.5 m·s-1 and above corresponding to systematic and scattered severe convective systems, with better performance observed in identifying severe convective winds triggered by systematic convection than those triggered by scattered convection. However, the identification effectiveness of SCWINet generally decreases as wind speed increases, with increased false alarms and missed detections being the primary causes of this phenomenon. By contrast, the commonly used WMSE and BMSE approach-es fail to identify severe convective winds, and all severe convective winds they identify are below 17.2 m·s-1.Nevertheless, the data used in this study are somewhat limited in terms of the feature completeness and volume. Future enhancements in identification accuracy of severe convective winds could be achieved by incorporating additional features and data, such as radar radial velocity, specific differential phase, differential reflectivity, and satellite data. This could be also applied to identify even higher wind speeds.
The serious imbalance in sample distribution, characterized by a sharp drop in the frequency of severe convective winds with increasing wind speeds, is identified as the predominant factor hindering the accurate intensity-based classification of severe convective winds by various existing algorithms.To address this problem, in this study the non-differentiable probability of detection (POD) is proposed to be the loss function for a convolutional neural network (CNN) and Bias to be its constraint condition. Subsequently, the multi-objective optimization immune evolution algorithm (MOIEA) is employed to optimize all the model parameters of the CNN. This contributes to the development of a novel identification algorithm, which is named severe convective wind identification network (SCWINet), for identifying severe convective winds at the speeds of 17.2, 20.8, 24.5 m·s-1 and above. SCWINet leverages the radar vertical liquid water content, three-dimensional radar reflectivity, lightning location data and minutely surface automatic observation station data in Zhejiang Province during 2022-2024, achieving different levels of severe convective wind identification with temporal resolution of 6 minutes and spatial resolution of 0.01°. Then, the performance of SCWINet is compared to weighted mean-square error (WMSE) and balanced mean-square error (BMSE), which use the same CNN structure but have differentiable loss functions. The applicability of SCWINet is then assessed based on the threat score, Bias, POD, false alarm ratio that uses the neighborhood method (with a scanning radius of 5 km), and the planar distribution characteristics of severe convective winds. The main results are as follows. SCWINet can effectively identify severe convective winds of 17.2, 20.8, 24.5 m·s-1 and above corresponding to systematic and scattered severe convective systems, with better performance observed in identifying severe convective winds triggered by systematic convection than those triggered by scattered convection. However, the identification effectiveness of SCWINet generally decreases as wind speed increases, with increased false alarms and missed detections being the primary causes of this phenomenon. By contrast, the commonly used WMSE and BMSE approach-es fail to identify severe convective winds, and all severe convective winds they identify are below 17.2 m·s-1.Nevertheless, the data used in this study are somewhat limited in terms of the feature completeness and volume. Future enhancements in identification accuracy of severe convective winds could be achieved by incorporating additional features and data, such as radar radial velocity, specific differential phase, differential reflectivity, and satellite data. This could be also applied to identify even higher wind speeds.
2026, 52(4):465-477.
DOI: 10.7519/j.issn.1000-0526.2025.082002
Abstract:
The surface wind speed forecasts are typically diagnosed from the lowest model level to 10 m height, thus the accuracy of model terrain has a significant impact on the performance of surface wind speed forecasts. In this study, high-resolution terrain data with 90 m resolution from the Shuttle Radar Topography Mission (SRTM) are used to construct a more accurate representation of terrain. Combined with near-surface similarity theory, the original surface wind speed diagnostic scheme in the numerical model is optimized. Using hourly observations from more than 10 000 automatic weather stations that are included in the assessment of the China Meteorological Administration, the 24 h surface wind speed forecasts initialized at 00:00 UTC 20 July and 20 November 2023 are verified. The results show that, compared with the original scheme, the optimized scheme reduces the deviation of regional surface wind speed forecasts by 10.7% on 20 July and 9.1% on 20 November, with a decrease about 1% in root mean square error as well. Furthermore, the two-month continuous tests indicate that the optimized scheme reduces the regional deviation of the 24 h surface wind speed forecasts by 50.0% and 52.6% for the whole months of July and November 2023, respectively. This denotes a significant positive effect of the optimized scheme on the performance of the surface wind speed forecasts by the numerical model.
The surface wind speed forecasts are typically diagnosed from the lowest model level to 10 m height, thus the accuracy of model terrain has a significant impact on the performance of surface wind speed forecasts. In this study, high-resolution terrain data with 90 m resolution from the Shuttle Radar Topography Mission (SRTM) are used to construct a more accurate representation of terrain. Combined with near-surface similarity theory, the original surface wind speed diagnostic scheme in the numerical model is optimized. Using hourly observations from more than 10 000 automatic weather stations that are included in the assessment of the China Meteorological Administration, the 24 h surface wind speed forecasts initialized at 00:00 UTC 20 July and 20 November 2023 are verified. The results show that, compared with the original scheme, the optimized scheme reduces the deviation of regional surface wind speed forecasts by 10.7% on 20 July and 9.1% on 20 November, with a decrease about 1% in root mean square error as well. Furthermore, the two-month continuous tests indicate that the optimized scheme reduces the regional deviation of the 24 h surface wind speed forecasts by 50.0% and 52.6% for the whole months of July and November 2023, respectively. This denotes a significant positive effect of the optimized scheme on the performance of the surface wind speed forecasts by the numerical model.
2026, 52(4):478-491.
DOI: 10.7519/j.issn.1000-0526.2025.072902
Abstract:
A multi-model integrated forecast correction process and scheme are constructed for 2 m temperature forecasts at Xi’an Station based on the 2021-2024 European Centre for Medium-Range Weather Forecasts (ECMWF) model forecasts and the 2 m temperature observations from Xi’an Station. The data from 1 September 2021 to 31 December 2023 are used as the training set for factor screening, parameter tuning, and model ensemble, while the data from 1 January to 30 April 2024 are taken as the test set to assess the forecast performance of numerical models and models trained under different schemes. Through subjective experience screening and time-lag correlation analysis, seven model forecasting physical variables closely related to temperature changes, as well as different lead time high level key zone variables and other characteristic factors get optimized. XGBoost, LightGBM and CatBoost are used for single model bias correction, and finally model fusion optimization is achieved through Stacking ensemble. The results show that ECMWF model exhibits a systematic cold bias in temperature forecasts at Xi’an Station, with the error being significantly greater at night than during the day and the cold bias intensifying during cooling and precipitation processes. After Bayesian optimization and parameter tuning, all the three machine learning models are able to effectively correct mode bias with root mean square errors (RMSE) reduced by 0.039℃, 0.030℃, and 0.027℃, respectively. Subsequent feature factor optimization further improves the single model forecast accuracy by approximately 0.257℃. The Stacking ensemble surpasses the traditional weighted ensemble. After ensemble, the RMSE of temperature forecasts is reduced by 0.023℃, and the forecast accuracy within 2℃ is improved by 2.589%. During the significant cooling and precipitation process, the forecast RMSE has a maximum reduction of 0.481℃ compared to that by the single model.
A multi-model integrated forecast correction process and scheme are constructed for 2 m temperature forecasts at Xi’an Station based on the 2021-2024 European Centre for Medium-Range Weather Forecasts (ECMWF) model forecasts and the 2 m temperature observations from Xi’an Station. The data from 1 September 2021 to 31 December 2023 are used as the training set for factor screening, parameter tuning, and model ensemble, while the data from 1 January to 30 April 2024 are taken as the test set to assess the forecast performance of numerical models and models trained under different schemes. Through subjective experience screening and time-lag correlation analysis, seven model forecasting physical variables closely related to temperature changes, as well as different lead time high level key zone variables and other characteristic factors get optimized. XGBoost, LightGBM and CatBoost are used for single model bias correction, and finally model fusion optimization is achieved through Stacking ensemble. The results show that ECMWF model exhibits a systematic cold bias in temperature forecasts at Xi’an Station, with the error being significantly greater at night than during the day and the cold bias intensifying during cooling and precipitation processes. After Bayesian optimization and parameter tuning, all the three machine learning models are able to effectively correct mode bias with root mean square errors (RMSE) reduced by 0.039℃, 0.030℃, and 0.027℃, respectively. Subsequent feature factor optimization further improves the single model forecast accuracy by approximately 0.257℃. The Stacking ensemble surpasses the traditional weighted ensemble. After ensemble, the RMSE of temperature forecasts is reduced by 0.023℃, and the forecast accuracy within 2℃ is improved by 2.589%. During the significant cooling and precipitation process, the forecast RMSE has a maximum reduction of 0.481℃ compared to that by the single model.
2026, 52(4):492-502.
DOI: 10.7519/j.issn.1000-0526.2026.030901
Abstract:
Based on observations from 2374 meteorological stations in China and the NCEP/NCAR reanalysis dataset, the spatio-temporal characteristics and causes of climate anomalies over China during autumn 2025 are analyzed. It was generally warmer than normal across China in autumn 2025, with the northern region of China exhibiting an intraseasonal “warm-cold-warm” temperature variation and the southern region showing a “warm in the early stage and cold in the late stage” pattern. The national average precipitation was the most since 1961 for the same period, with an uneven spatio and temporal distribution. In the early autumn, large-scale excessive precipitation with prominent extremeness occurred in southern North China, northern East China, northern Central China, and eastern Northwest China, finally leading to serious autumn flood. The autumn rain in West China started early, ended late and had a long duration, with the precipitation amount ranking the top in history. The formation of autumn flood in northern China was closely correlated to East Asian atmospheric circulation anomalies. The abnormally strong western Pacific subtropical high (WPSH) with an extremely northward ridge line, coupled with a robust low-level anticyclone over the Yellow Sea, provided favorable water vapor transport conditions, serving as the basic circulation background for the autumn flood in northern China. Additionally, the extreme meridional stability of the WPSH (i.e., little north-south movement) was another key circulation feature contributing to the flood. Furthermore, during the autumn, the equatorial central-eastern Pacific was in a developmental stage from cold water conditions to La Nia, accompanied by an extreme negative phase of the tropical Indian Ocean dipole (TIOD). The two factors, through pathways such as atmospheric teleconnections and local meridional-zonal circulation coupling, worked synergistically in influencing the anomalous configuration of the East Asian circulation, forming as an important oceanic external forcing background for the autumn flood in northern China in 2025.
Based on observations from 2374 meteorological stations in China and the NCEP/NCAR reanalysis dataset, the spatio-temporal characteristics and causes of climate anomalies over China during autumn 2025 are analyzed. It was generally warmer than normal across China in autumn 2025, with the northern region of China exhibiting an intraseasonal “warm-cold-warm” temperature variation and the southern region showing a “warm in the early stage and cold in the late stage” pattern. The national average precipitation was the most since 1961 for the same period, with an uneven spatio and temporal distribution. In the early autumn, large-scale excessive precipitation with prominent extremeness occurred in southern North China, northern East China, northern Central China, and eastern Northwest China, finally leading to serious autumn flood. The autumn rain in West China started early, ended late and had a long duration, with the precipitation amount ranking the top in history. The formation of autumn flood in northern China was closely correlated to East Asian atmospheric circulation anomalies. The abnormally strong western Pacific subtropical high (WPSH) with an extremely northward ridge line, coupled with a robust low-level anticyclone over the Yellow Sea, provided favorable water vapor transport conditions, serving as the basic circulation background for the autumn flood in northern China. Additionally, the extreme meridional stability of the WPSH (i.e., little north-south movement) was another key circulation feature contributing to the flood. Furthermore, during the autumn, the equatorial central-eastern Pacific was in a developmental stage from cold water conditions to La Nia, accompanied by an extreme negative phase of the tropical Indian Ocean dipole (TIOD). The two factors, through pathways such as atmospheric teleconnections and local meridional-zonal circulation coupling, worked synergistically in influencing the anomalous configuration of the East Asian circulation, forming as an important oceanic external forcing background for the autumn flood in northern China in 2025.
2026, 52(4):503-512.
DOI: 10.7519/j.issn.10000526.2026.033001
Abstract:
In January 2026, the main characteristics of the atmospheric circulation in the Northern Hemisphere are that the polar vortex was distributed in a dipole pattern, and the center of polar vortex in the Eastern Hemisphere was located in the Central Siberian Plateau, which was weaker than that in the same period of normal years. The circulation in the middle and high latitudes of Eurasia presented a “two troughs and one ridge” distribution. The East Asian trough was stronger than usual, and the cold air path affecting China was by east. The Northwest Pacific subtropical high (WPSH) was slightly weaker than normal, while the southern branch trough was weaker than normal. Under such circulation background, the national average precipitation in January (6.3 mm) was 56% less than that in the same period of normal years (14.3 mm), with an uneven spatial distribution, i.e., more in the north and less in the south. The national average temperature was -4.0℃, which is 0.8℃ higher than normal (-4.8℃). During this month, five cold air activities occurred, of which there was a cold wave weather process during 17-21 when the temperatures in the central and eastern regions of China dropped sharply. The minimum temperatures at six national stations broke through the historical extreme values, accompanied by a wide range of rain and snow weather, and the phasetransition of precipitation was complex. Freezing rain or ice particles appeared in Guizhou, Hunan and Hubei, posing a serious impact on road traffic and power. In addition, there were three sanddust weather processes and three foghaze weather processes in the month.
In January 2026, the main characteristics of the atmospheric circulation in the Northern Hemisphere are that the polar vortex was distributed in a dipole pattern, and the center of polar vortex in the Eastern Hemisphere was located in the Central Siberian Plateau, which was weaker than that in the same period of normal years. The circulation in the middle and high latitudes of Eurasia presented a “two troughs and one ridge” distribution. The East Asian trough was stronger than usual, and the cold air path affecting China was by east. The Northwest Pacific subtropical high (WPSH) was slightly weaker than normal, while the southern branch trough was weaker than normal. Under such circulation background, the national average precipitation in January (6.3 mm) was 56% less than that in the same period of normal years (14.3 mm), with an uneven spatial distribution, i.e., more in the north and less in the south. The national average temperature was -4.0℃, which is 0.8℃ higher than normal (-4.8℃). During this month, five cold air activities occurred, of which there was a cold wave weather process during 17-21 when the temperatures in the central and eastern regions of China dropped sharply. The minimum temperatures at six national stations broke through the historical extreme values, accompanied by a wide range of rain and snow weather, and the phasetransition of precipitation was complex. Freezing rain or ice particles appeared in Guizhou, Hunan and Hubei, posing a serious impact on road traffic and power. In addition, there were three sanddust weather processes and three foghaze weather processes in the month.
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