ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 52,Issue 5,2026 Table of Contents
LI Nina
,
JIN Ronghua
,
GONG Yu
,
DAI Kan
,
CAO Yong
,
NIE Gaozhen
,
LIN Jian
,
WEI Qing
,
WU Haixu
,
LUO Huakun
,
LONG Mingsheng
,
WANG Jianmin
2026, 52(5):513-526.
DOI: 10.7519/j.issn.1000-0526.2026.030501
Abstract:
In 2024, China Meteorological Administration (CMA), in collaboration with Tsinghua University, developed the forecasting model following an innovative “AI-Physics” hybrid approach—Fengqing Model. With the designs such as a multi-scale latent space projection architecture and an energy-conservation loss function, the model has been equipped with global short- and medium-range weather forecasting capabilities and has been applied in forecasting operations. This paper comprehensively evaluates the forecasting ability of Fengqing Model in China and the surrounding areas in 2024 from several metrics such as forecasting accuracy and bias distribution. Two kinds of typical synoptic processes, i.e., typhoon and rainstorm, are also focused on deeply exploring the model’s performance in the forecasts of disastrous weather. The results show that the 500 hPa geopotential height forecasts by Fengqing Model maintain a predictive skill beyond 10 days. The root mean square errors of 2 m temperature and 850 hPa temperature are significantly lower than those from the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS), having a maximum improvement of 37.66%. In terms of typical weather processes, Fengqing Model demonstrates its superior performance in typhoon track forecasting to ECMWF-IFS, but it underestesmates the typhoon intensity. In addition, Fengqing Model has a good forecasting ability for rainstorm, with smaller forecast errors for the locations of typhoon rain and Meiyu front rainfall belt. The TS score of rainstorm forecasts in the medium-range (73-168 h lead time) is improved by 43.53% compared to that of ECMWF-IFS forecasts. Overall, the Fengqing Model presents considerable potential in operational forecasting, although further improvements are needed in activity level and typhoon intensity prediction at medium- and long-range lead times.
In 2024, China Meteorological Administration (CMA), in collaboration with Tsinghua University, developed the forecasting model following an innovative “AI-Physics” hybrid approach—Fengqing Model. With the designs such as a multi-scale latent space projection architecture and an energy-conservation loss function, the model has been equipped with global short- and medium-range weather forecasting capabilities and has been applied in forecasting operations. This paper comprehensively evaluates the forecasting ability of Fengqing Model in China and the surrounding areas in 2024 from several metrics such as forecasting accuracy and bias distribution. Two kinds of typical synoptic processes, i.e., typhoon and rainstorm, are also focused on deeply exploring the model’s performance in the forecasts of disastrous weather. The results show that the 500 hPa geopotential height forecasts by Fengqing Model maintain a predictive skill beyond 10 days. The root mean square errors of 2 m temperature and 850 hPa temperature are significantly lower than those from the European Centre for Medium-Range Weather Forecasts (ECMWF-IFS), having a maximum improvement of 37.66%. In terms of typical weather processes, Fengqing Model demonstrates its superior performance in typhoon track forecasting to ECMWF-IFS, but it underestesmates the typhoon intensity. In addition, Fengqing Model has a good forecasting ability for rainstorm, with smaller forecast errors for the locations of typhoon rain and Meiyu front rainfall belt. The TS score of rainstorm forecasts in the medium-range (73-168 h lead time) is improved by 43.53% compared to that of ECMWF-IFS forecasts. Overall, the Fengqing Model presents considerable potential in operational forecasting, although further improvements are needed in activity level and typhoon intensity prediction at medium- and long-range lead times.
LIU Junjie
,
LU Bo
,
LI Hao
,
CHEN Lei
,
ZHONG Xiaohui
,
ZHOU Chenguang
,
HU Jiahui
,
WU Jie
,
ZHAO Chunyan
,
XIN Yuhang
,
ZHAO Yang
,
QIAN Qifeng
2026, 52(5):527-537.
DOI: 10.7519/j.issn.1000-0526.2026.032901
Abstract:
Based on the hindcast datasets from the Fengshun Model and the S2S-ECMWF Model, together with CMA-RA1.0 and NCEP reanalysis data and station observations during 2017-2021, the pentad-scale prediction skills of the Fengshun Model and the S2S-ECMWF Model for midsummer (July-August) temperature over China are comparatively verified. Three metrics of temporal correlation coefficient (TCC), anomaly correlation coefficient (ACC), and integrated prediction score (IPS) are adopted. The results are as follows. The verification results based on different reference datasets are generally consistent, demonstrating their good robustness. Overall, the Fengshun Model shows superior prediction skill to the S2S-ECMWF Model, with TCC, ACC and IPS values improved by 7.9%, 18.4%, and 1.5%, respectively. Spatially, the Fengshun Model exhibits higher TCC skill in the Huang-Huai (Yellow River-Huaihe River), Jiang-Huai (Yangtze River-Huaihe River), Central China, South China, East China, and Xinjiang regions, but showing relatively lower skill in Northeast China, Inner Mongolia, the Qinghai-Xizang Plateau, and Southwest China. Temporally, the Fengshun Model demonstrates superior prediction skill with 1 pentad and 4-8 pentad lead times, with the highest skill appearing at a 6 pentad lead time, and improvements in TCC, ACC and IPS can reach 42%, 260%, and 4.5%, respectively, which indicates an extended predictability window. This advantage is primarily attributed to the Fengshun Model’s better characterization of 500 hPa geopotential height anomalies over key circulation regions in mid-latitudes of Asia. However, the Fengshun Model shows a relatively weak prediction skill with 2 pentad to 3 pentad lead times, which is likely due to the decay of initial atmospheric signals and insufficient influence from underlying surface information. Future improvement focus will be put on incorporating multi-layer surface information to further enhance the Model’s prediction performance.
Based on the hindcast datasets from the Fengshun Model and the S2S-ECMWF Model, together with CMA-RA1.0 and NCEP reanalysis data and station observations during 2017-2021, the pentad-scale prediction skills of the Fengshun Model and the S2S-ECMWF Model for midsummer (July-August) temperature over China are comparatively verified. Three metrics of temporal correlation coefficient (TCC), anomaly correlation coefficient (ACC), and integrated prediction score (IPS) are adopted. The results are as follows. The verification results based on different reference datasets are generally consistent, demonstrating their good robustness. Overall, the Fengshun Model shows superior prediction skill to the S2S-ECMWF Model, with TCC, ACC and IPS values improved by 7.9%, 18.4%, and 1.5%, respectively. Spatially, the Fengshun Model exhibits higher TCC skill in the Huang-Huai (Yellow River-Huaihe River), Jiang-Huai (Yangtze River-Huaihe River), Central China, South China, East China, and Xinjiang regions, but showing relatively lower skill in Northeast China, Inner Mongolia, the Qinghai-Xizang Plateau, and Southwest China. Temporally, the Fengshun Model demonstrates superior prediction skill with 1 pentad and 4-8 pentad lead times, with the highest skill appearing at a 6 pentad lead time, and improvements in TCC, ACC and IPS can reach 42%, 260%, and 4.5%, respectively, which indicates an extended predictability window. This advantage is primarily attributed to the Fengshun Model’s better characterization of 500 hPa geopotential height anomalies over key circulation regions in mid-latitudes of Asia. However, the Fengshun Model shows a relatively weak prediction skill with 2 pentad to 3 pentad lead times, which is likely due to the decay of initial atmospheric signals and insufficient influence from underlying surface information. Future improvement focus will be put on incorporating multi-layer surface information to further enhance the Model’s prediction performance.
2026, 52(5):538-551.
DOI: 10.7519/j.issn.1000-0526.2026.030903
Abstract:
Aiming at the fine-scale forecasting challenge of 2 m temperature (T2m) in complex terrain areas, this paper selects the Guangxi Region, a typical area with complex terrain, as the research object and proposes a physics-constrained deep learning forecasting model named PSD-Net, which integrates the forecast products of the Pangu-Weather Model. The forecast products of Pangu-Weather (PANGU) are used as feature variables input. The generator based on the super-resolution generative adversarial network is employed to extract multi-scale features. Power-spectral-density and Kullback-Leibler divergence are explicitly injected into the loss function as constraint terms so as to improve the consistency of forecast products with observations in spectral fidelity and probability distribution. Compared to the T2m forecast performance of the ECMWF, SCMOC and PANGU products in the Guangxi Region in 2024, both the grided forecasts and station-based forecasts of PSD-Net outperform the compared forecast products. In particular, the mean absolute error (MAE) of the gridded forecasts is reduced by 37.6% relative to that of PANGU and the accuracy is improved by 17 percentage points. The growths of both MAE and root mean square error (RMSE) for the 1-72 h T2m forecast products from PSD-Net are less than those of the compared forecast products, and there is a gentle error growth at lead time 25-72 h. In a word, this study has verified the effectiveness of the physics-constrained deep learning framework in fine-scale T2m forecasting which could provide a new approach for the combination of meteorological and AI models.
Aiming at the fine-scale forecasting challenge of 2 m temperature (T2m) in complex terrain areas, this paper selects the Guangxi Region, a typical area with complex terrain, as the research object and proposes a physics-constrained deep learning forecasting model named PSD-Net, which integrates the forecast products of the Pangu-Weather Model. The forecast products of Pangu-Weather (PANGU) are used as feature variables input. The generator based on the super-resolution generative adversarial network is employed to extract multi-scale features. Power-spectral-density and Kullback-Leibler divergence are explicitly injected into the loss function as constraint terms so as to improve the consistency of forecast products with observations in spectral fidelity and probability distribution. Compared to the T2m forecast performance of the ECMWF, SCMOC and PANGU products in the Guangxi Region in 2024, both the grided forecasts and station-based forecasts of PSD-Net outperform the compared forecast products. In particular, the mean absolute error (MAE) of the gridded forecasts is reduced by 37.6% relative to that of PANGU and the accuracy is improved by 17 percentage points. The growths of both MAE and root mean square error (RMSE) for the 1-72 h T2m forecast products from PSD-Net are less than those of the compared forecast products, and there is a gentle error growth at lead time 25-72 h. In a word, this study has verified the effectiveness of the physics-constrained deep learning framework in fine-scale T2m forecasting which could provide a new approach for the combination of meteorological and AI models.
4 Using Generative Adversarial Network to Improve Heavy Precipitation Nowcasting in the Jianghuai Area
2026, 52(5):552-565.
DOI: 10.7519/j.issn.1000-0526.2025.080801
Abstract:
This paper proposes two deep learning-based short-term heavy precipitation nowcasting methods for the Jianghuai Area by optimizing generative adversarial network (GAN), namely PhySGAN (combining PhyDNet and SGAN) and PhyMGAN (combining PhyDNet and MGAN), so as to provide precipitation forecasts in the next 3 hours for the Jianghuai Area. Based on the forecast skill score, the performance assessment in “complex scenarios” and the analysis of typical application examples, this paper analyzes the forecast performance of the two methods in the short-time heavy precipitation (≥20 mm·h-1) forecasts in Jiangsu Province during the flood season of 2024. The results show that the TS scores of short-term heavy precipitation within 3 hours in different verification periods of PhySGAN and PhyMGAN are significantly improved compared with those of the basic experiment PhyDNet and the general GAN experiment PhyGAN. The two new methods can correct the low frequency problem of short-term heavy precipitation forecasts by PhyDNet and PhyGAN, so that the TS score increases with the increase of the forecast lead time, thereby effectively extending the nowcasting lead time of short-term heavy precipitation. Judged from the forecast performance shown by each method in “complex scenarios”, deep learning can reflect the evolution of the generation and dissipation of heavy precipitation relative to the traditional extrapolation methods. PhySGAN and PhyMGAN show better forecast performance than PhyDNet and PhyGAN. The former has a better ability to depict local details such as the shape and intensity of heavy precipitation, while the latter has a better representation of the overall contour and position of the heavy precipitation rain band. Combined with the application of typical heavy precipitation cases during the flood season, both PhySGAN and PhyMGAN can forecast the precipitation enhancement process in advance in both systematic heavy precipitation and local heavy precipitation cases, effectively guiding the early warning of disasters. In addition, PhyMGAN has a certain indicative effect on extreme rainfall intensities above 50 mm·h-1, while PhySGAN can better reflect the changes in the shape and position of the rain band.
This paper proposes two deep learning-based short-term heavy precipitation nowcasting methods for the Jianghuai Area by optimizing generative adversarial network (GAN), namely PhySGAN (combining PhyDNet and SGAN) and PhyMGAN (combining PhyDNet and MGAN), so as to provide precipitation forecasts in the next 3 hours for the Jianghuai Area. Based on the forecast skill score, the performance assessment in “complex scenarios” and the analysis of typical application examples, this paper analyzes the forecast performance of the two methods in the short-time heavy precipitation (≥20 mm·h-1) forecasts in Jiangsu Province during the flood season of 2024. The results show that the TS scores of short-term heavy precipitation within 3 hours in different verification periods of PhySGAN and PhyMGAN are significantly improved compared with those of the basic experiment PhyDNet and the general GAN experiment PhyGAN. The two new methods can correct the low frequency problem of short-term heavy precipitation forecasts by PhyDNet and PhyGAN, so that the TS score increases with the increase of the forecast lead time, thereby effectively extending the nowcasting lead time of short-term heavy precipitation. Judged from the forecast performance shown by each method in “complex scenarios”, deep learning can reflect the evolution of the generation and dissipation of heavy precipitation relative to the traditional extrapolation methods. PhySGAN and PhyMGAN show better forecast performance than PhyDNet and PhyGAN. The former has a better ability to depict local details such as the shape and intensity of heavy precipitation, while the latter has a better representation of the overall contour and position of the heavy precipitation rain band. Combined with the application of typical heavy precipitation cases during the flood season, both PhySGAN and PhyMGAN can forecast the precipitation enhancement process in advance in both systematic heavy precipitation and local heavy precipitation cases, effectively guiding the early warning of disasters. In addition, PhyMGAN has a certain indicative effect on extreme rainfall intensities above 50 mm·h-1, while PhySGAN can better reflect the changes in the shape and position of the rain band.
JI Zhongping
,
GU Dejun
,
GAO Xiaorong
,
XU Yanhong
,
LI Shanshan
,
LIANG Qiaoqian
,
TU Jing
,
LIANG Weijie
2026, 52(5):566-579.
DOI: 10.7519/j.issn.1000-0526.2026.022801
Abstract:
The low-frequency oscillation characteristics of continuous rainstorm processes in Guangdong during the pre-flood season in 2023 are analyzed by means of wavelet analysis and Lanczos temporal filter. The focus of this study is on revealing the mean atmospheric circulation field and its evolution characteristics in different phases of quasi-biweekly oscillations and the source of the low-frequency signals of the continuous warm-sector rainstorms with strong southwesterly winds in the northwest of Guangdong Province. The results show that the three continuous rainstorms exhibited a quasi-7-18 d periodic oscillation, located in the positive phase of two quasi-30-60 d intraseasonal oscillations. The continuous warm-sector rainstorms with strong southwesterly winds mainly occurred in the northwest of Guangdong in 22-26 June when there were stable “west blocking” and “east blocking” at the middle and high latitudes. During this period, the northwest of Guangdong was located at the bottom of the plateau trough and the edge of the western Pacific subtropical high at 500 hPa, and at the left side of southwest monsoon axis and the right side of the cyclonic circulations at 850 hPa. During the intermittent-start-peak period of the continuous warm-sector rainstorm processes with strong southwesterly winds, the low-frequency signals in the mid-to-upper levels originated from the southern branch wave train with the eastward propagation and southward extension of baroclinic low-frequency anticyclones and cyclones within or to the south of the Iranian Plateau and Qinghai-Xizang Plateau. The low-frequency signals in the lower levels arose from the eastward movement and southward extension of the eastern Mongolian Plateau low-frequency anticyclone and the progressively intensifying low-frequency cyclone east of the Sichuan Basin and Yunnan-Guizhou Plateau. They induced the center of South Asian high to move eastward gradually and the western Pacific subtropical high to weaken and retreat eastward. At the same time, the Qinghai-Xizang Plateau was controlled by low-frequency cyclonic circulation in intermittent periods and gradually became dominated by the low-frequency anticyclone circulation in the peak period, while North China and South China were controlled first by low-frequency anticyclone circulation in intermittent periods and then gradually by the low-frequency cyclonic circulation in the peak period. These findings could provide some references for medium-to-extended range forecasting of continuous warm-sector rainstorm.
The low-frequency oscillation characteristics of continuous rainstorm processes in Guangdong during the pre-flood season in 2023 are analyzed by means of wavelet analysis and Lanczos temporal filter. The focus of this study is on revealing the mean atmospheric circulation field and its evolution characteristics in different phases of quasi-biweekly oscillations and the source of the low-frequency signals of the continuous warm-sector rainstorms with strong southwesterly winds in the northwest of Guangdong Province. The results show that the three continuous rainstorms exhibited a quasi-7-18 d periodic oscillation, located in the positive phase of two quasi-30-60 d intraseasonal oscillations. The continuous warm-sector rainstorms with strong southwesterly winds mainly occurred in the northwest of Guangdong in 22-26 June when there were stable “west blocking” and “east blocking” at the middle and high latitudes. During this period, the northwest of Guangdong was located at the bottom of the plateau trough and the edge of the western Pacific subtropical high at 500 hPa, and at the left side of southwest monsoon axis and the right side of the cyclonic circulations at 850 hPa. During the intermittent-start-peak period of the continuous warm-sector rainstorm processes with strong southwesterly winds, the low-frequency signals in the mid-to-upper levels originated from the southern branch wave train with the eastward propagation and southward extension of baroclinic low-frequency anticyclones and cyclones within or to the south of the Iranian Plateau and Qinghai-Xizang Plateau. The low-frequency signals in the lower levels arose from the eastward movement and southward extension of the eastern Mongolian Plateau low-frequency anticyclone and the progressively intensifying low-frequency cyclone east of the Sichuan Basin and Yunnan-Guizhou Plateau. They induced the center of South Asian high to move eastward gradually and the western Pacific subtropical high to weaken and retreat eastward. At the same time, the Qinghai-Xizang Plateau was controlled by low-frequency cyclonic circulation in intermittent periods and gradually became dominated by the low-frequency anticyclone circulation in the peak period, while North China and South China were controlled first by low-frequency anticyclone circulation in intermittent periods and then gradually by the low-frequency cyclonic circulation in the peak period. These findings could provide some references for medium-to-extended range forecasting of continuous warm-sector rainstorm.
2026, 52(5):580-594.
DOI: 10.7519/j.issn.1000-0526.2026.040202
Abstract:
This study classified the weather situations of heavy precipitation events in the Yangtze River Delta Region from September 2022 to September 2024 and evaluated the forecast performance of the numerical models CMA-MESO, CMA-GFS, CMA-TYM, CMA-SH9 and the ECMWF model under four main weather types. The research results show that in the 24 h precipitation forecasts, there is a high false alarm rate of forecast for light rain, while torrential rain and above are difficult to accurately forecast, thus the TS score is low. In the 3 h forecasts, the CMA-MESO model performs best for light precipitation. However, under cold shear and low-vortex shear types, heavy precipitation becomes harder to capture, and the model forecast performance weak. In terms of spatial feature evaluation, except the CMA-SH9 model, most models have northern systematic errors in the low-vortex shear and subtropical high with low trough types in the north-south direction, but the situation is the opposite for the typhoon body and periphery type. In the east-west direction, these models generally exhibit an eastern systematic bias in the typhoon body and periphery types, while in other weather types, most models have western systematic errors. For the evaluation of temporal characteristics, all the models have the highest accuracy in forecasting the start time of precipitation, followed by the forecast of end time, and the accuracy in forecasting the peak time is relatively low.
This study classified the weather situations of heavy precipitation events in the Yangtze River Delta Region from September 2022 to September 2024 and evaluated the forecast performance of the numerical models CMA-MESO, CMA-GFS, CMA-TYM, CMA-SH9 and the ECMWF model under four main weather types. The research results show that in the 24 h precipitation forecasts, there is a high false alarm rate of forecast for light rain, while torrential rain and above are difficult to accurately forecast, thus the TS score is low. In the 3 h forecasts, the CMA-MESO model performs best for light precipitation. However, under cold shear and low-vortex shear types, heavy precipitation becomes harder to capture, and the model forecast performance weak. In terms of spatial feature evaluation, except the CMA-SH9 model, most models have northern systematic errors in the low-vortex shear and subtropical high with low trough types in the north-south direction, but the situation is the opposite for the typhoon body and periphery type. In the east-west direction, these models generally exhibit an eastern systematic bias in the typhoon body and periphery types, while in other weather types, most models have western systematic errors. For the evaluation of temporal characteristics, all the models have the highest accuracy in forecasting the start time of precipitation, followed by the forecast of end time, and the accuracy in forecasting the peak time is relatively low.
2026, 52(5):595-607.
DOI: 10.7519/j.issn.1000-0526.2025.080601
Abstract:
Based on the S-band dual-polarization radar data from Qingpu (Shanghai), Nantong (Jiangsu), Hangzhou, Jiaxing, Huzhou and Ningbo (Zhejiang), the applicability evaluation of three volume coverage patterns (VCP21D, VCP11D and VCP216D) is assessed under general precipitation, severe convective and typhoon conditions. The evaluation is based on three methods, that is, the subjective identification of characteristic tracer factors, the interpolation of reflectivity factor isosurfaces, and the comparison of dual-radar wind field retrieval. The results indicate that the precipitation mode (VCP21D) and the convective modes (VCP11D and VCP216D） can accurately identify the 0℃ layer bright band characteristic. VCP21D, compared to VCP216D, shows better stability in recognizing the melting layer height, with a smaller standard deviation and a better match to actual conditions. The convective modes VCP11D and VCP216D can significantly enhance vertical observational resolution relative to the precipitation mode VCP21D. This improvement is crucial for detecting key severe weather phenomena, such as ZDR columns and mesoscale cyclones. Meanwhile, compared to VCP11D, the additional 1.0° elevation angle in VCP216D is particularly effective in capturing mesoscale features such as low-level gust fronts and sea breeze fronts. Also, this additional scan can eliminate the impact from ground clutter echoes for data quality improvement. In the comparison of isosurface interpolations the data from VCP11D and VCP216D are more detailed than that from VCP21D at the 5 km altitude. In the comparison of wind field retrieval, the availability and accuracy of the retrieved data, from the dual-radar wind field retrieval results of convective modes VCP11D and VCP216D are more significantly improved compared to those of the precipitation mode VCP21D.
Based on the S-band dual-polarization radar data from Qingpu (Shanghai), Nantong (Jiangsu), Hangzhou, Jiaxing, Huzhou and Ningbo (Zhejiang), the applicability evaluation of three volume coverage patterns (VCP21D, VCP11D and VCP216D) is assessed under general precipitation, severe convective and typhoon conditions. The evaluation is based on three methods, that is, the subjective identification of characteristic tracer factors, the interpolation of reflectivity factor isosurfaces, and the comparison of dual-radar wind field retrieval. The results indicate that the precipitation mode (VCP21D) and the convective modes (VCP11D and VCP216D） can accurately identify the 0℃ layer bright band characteristic. VCP21D, compared to VCP216D, shows better stability in recognizing the melting layer height, with a smaller standard deviation and a better match to actual conditions. The convective modes VCP11D and VCP216D can significantly enhance vertical observational resolution relative to the precipitation mode VCP21D. This improvement is crucial for detecting key severe weather phenomena, such as ZDR columns and mesoscale cyclones. Meanwhile, compared to VCP11D, the additional 1.0° elevation angle in VCP216D is particularly effective in capturing mesoscale features such as low-level gust fronts and sea breeze fronts. Also, this additional scan can eliminate the impact from ground clutter echoes for data quality improvement. In the comparison of isosurface interpolations the data from VCP11D and VCP216D are more detailed than that from VCP21D at the 5 km altitude. In the comparison of wind field retrieval, the availability and accuracy of the retrieved data, from the dual-radar wind field retrieval results of convective modes VCP11D and VCP216D are more significantly improved compared to those of the precipitation mode VCP21D.
2026, 52(5):608-620.
DOI: 10.7519/j.issn.1000-0526.2025.122001
Abstract:
In mid-December 2023, Shanxi Province experienced a weather event of low temperature, rain, snow and icing. On 13 December, wire icing occurred in Yuanqu County located in southern Shanxi, significantly disrupting people’s lives and production activities. Using conventional meteorological observations, Doppler weather radar data and the ERA5 reanalysis datasets, this paper investigates the formation mechanism of this serious disaster event caused by wire icing. The results show that this wire icing event in Yuanqu was of the mixed rime and glaze type, which was caused by the combined effects of short-duration freezing rain in the early stage and prolonged freezing fog in the subsequent period. Freezing fog played a more significant role than freezing rain. In the north frontal zone at 500 hPa, the horizontal trough in front of the high pressure ridge near the Ural Mountains and a deep cold vortex near the Sea of Okhotsk both remained stable with less movement. The short-wave trough ahead of the bottom of the horizontal trough moved rapidly eastward into Shanxi, while in the south frontal zone, the southwest jet stream at 700 hPa was strong. The cold trough in the northeast-southwest direction at 850 hPa was located over North China, and the surface inverted-trough in Hetao Area developed violently, interacting with the return-flow weather pattern. Besides, the southwest warm-humid airflow ascended along the low-level cold air cushion, providing a favorable large-scale circulation background for the formation of freezing rain and freezing fog in Yuanqu. The temperature advection configuration of “upper warming and lower cooling” in vertical direction led to a “cold-warm-cold temperature” structure in Yuanqu from low to high levels, with the mid-level temperature above 0℃ and the lower-level temperature below 0℃. The short-term freezing rain in Yuanqu, triggered by the short-wave trough, was attributed to the melting mechanism. The vertical humidity advection configuration of “wet above and dry at bottom”, coupled with sinking motion together made the lower atmosphere highly saturated. Additionally, temperature inversion continued in the lower atmosphere. These conditions collectively facilitated the long-term persistence of freezing fog in Yuanqu. Abundant supercooled water droplets that had two phases of rapid growth continuously impinged on the wire surfaces, causing ice accretion to become thickened progressively. Southeasterly airflow ascended along the windward slopes of the horn-shaped terrain, and also played a certain role in promoting the thickening of accretion on wires.
In mid-December 2023, Shanxi Province experienced a weather event of low temperature, rain, snow and icing. On 13 December, wire icing occurred in Yuanqu County located in southern Shanxi, significantly disrupting people’s lives and production activities. Using conventional meteorological observations, Doppler weather radar data and the ERA5 reanalysis datasets, this paper investigates the formation mechanism of this serious disaster event caused by wire icing. The results show that this wire icing event in Yuanqu was of the mixed rime and glaze type, which was caused by the combined effects of short-duration freezing rain in the early stage and prolonged freezing fog in the subsequent period. Freezing fog played a more significant role than freezing rain. In the north frontal zone at 500 hPa, the horizontal trough in front of the high pressure ridge near the Ural Mountains and a deep cold vortex near the Sea of Okhotsk both remained stable with less movement. The short-wave trough ahead of the bottom of the horizontal trough moved rapidly eastward into Shanxi, while in the south frontal zone, the southwest jet stream at 700 hPa was strong. The cold trough in the northeast-southwest direction at 850 hPa was located over North China, and the surface inverted-trough in Hetao Area developed violently, interacting with the return-flow weather pattern. Besides, the southwest warm-humid airflow ascended along the low-level cold air cushion, providing a favorable large-scale circulation background for the formation of freezing rain and freezing fog in Yuanqu. The temperature advection configuration of “upper warming and lower cooling” in vertical direction led to a “cold-warm-cold temperature” structure in Yuanqu from low to high levels, with the mid-level temperature above 0℃ and the lower-level temperature below 0℃. The short-term freezing rain in Yuanqu, triggered by the short-wave trough, was attributed to the melting mechanism. The vertical humidity advection configuration of “wet above and dry at bottom”, coupled with sinking motion together made the lower atmosphere highly saturated. Additionally, temperature inversion continued in the lower atmosphere. These conditions collectively facilitated the long-term persistence of freezing fog in Yuanqu. Abundant supercooled water droplets that had two phases of rapid growth continuously impinged on the wire surfaces, causing ice accretion to become thickened progressively. Southeasterly airflow ascended along the windward slopes of the horn-shaped terrain, and also played a certain role in promoting the thickening of accretion on wires.
2026, 52(5):621-630.
DOI: 10.7519/j.issn.1000-0526.2026.011701
Abstract:
To improve snowfall forecast accuracy and aviation meteorological services at Lhasa Airport, by employing the improved Jenkinson-Collison (J-C) circulation type classification method, this paper classifies and diagnoses 56 snowfall events that occurred during 2013—2020 at Lhasa Airport. The results are that, with the improved method, a classification success rate of 92.9% is achieved, and three types, i.e., the cyclonic type (12.5%), low-pressure trough type (42.9%) and westerly advection type (37.5%) of snowfall events are identified. There are only 7.1% of snowfall events remaining unclassified-significantly lower than that by the traditional method. The characteristics of the three identified circulation types are significantly different. The cyclonic type, corresponding to plateau vortices, features the strongest dynamics and water vapor conditions, with the most average snowfall (3.4 mm) and longest duration (358 min). The low-pressure trough type, influenced by the southern branch trough, shows the most unstable atmospheric stratification but relatively weak dynamics and water vapor, resulting in the lowest mean snowfall (1.2 mm) and shortest duration (170 min). The westerly advection type, dominated by warm ridges and upper-level jets, has the most stable atmospheric stratification, with a “low-level convergence and upper-level divergence” moisture structure. Its intermediate snowfall indicators lie between those of the other two types, that is, the average snowfall is 1.8 mm and the duration is 280 min. These findings could provide a scientific basis for refined snowfall forecasting and help improve the aviation meteorological services at Lhasa Airport.
To improve snowfall forecast accuracy and aviation meteorological services at Lhasa Airport, by employing the improved Jenkinson-Collison (J-C) circulation type classification method, this paper classifies and diagnoses 56 snowfall events that occurred during 2013—2020 at Lhasa Airport. The results are that, with the improved method, a classification success rate of 92.9% is achieved, and three types, i.e., the cyclonic type (12.5%), low-pressure trough type (42.9%) and westerly advection type (37.5%) of snowfall events are identified. There are only 7.1% of snowfall events remaining unclassified-significantly lower than that by the traditional method. The characteristics of the three identified circulation types are significantly different. The cyclonic type, corresponding to plateau vortices, features the strongest dynamics and water vapor conditions, with the most average snowfall (3.4 mm) and longest duration (358 min). The low-pressure trough type, influenced by the southern branch trough, shows the most unstable atmospheric stratification but relatively weak dynamics and water vapor, resulting in the lowest mean snowfall (1.2 mm) and shortest duration (170 min). The westerly advection type, dominated by warm ridges and upper-level jets, has the most stable atmospheric stratification, with a “low-level convergence and upper-level divergence” moisture structure. Its intermediate snowfall indicators lie between those of the other two types, that is, the average snowfall is 1.8 mm and the duration is 280 min. These findings could provide a scientific basis for refined snowfall forecasting and help improve the aviation meteorological services at Lhasa Airport.
2026, 52(5):631-640.
DOI: 10.7519/j.issn.1000-0526.2026.042401
Abstract:
In February 2026, the Northern Hemisphere polar vortex exhibited a dipole pattern, with its primary center located over northern Canada and a secondary center over the region from Novaya Zemlya to the Sea of Okhotsk in the Eastern Hemisphere. Most mid-to-high latitude areas of China were situated in the front of high pressure ridge, where geopotential heights were dominated by positive anomalies. The main body of cold air activity was relatively weak, resulting in above-normal temperatures in most parts of China. The national average temperature was 2.1℃ higher than the corresponding climatological normal, ranking the third highest for February since 1961. The position and intensity of the southern branch trough this month were close to normal. With the phased cold air activities in northern China, warm and cold air masses converged frequently over northern China, leading to numerous rain and snow events in the northern regions. Four notable precipitation processes occurred nationwide in February, with uneven spatial distribution. Precipitation was above normal in southern North China, Huanghuai Region, south-eastern Northwest China, northern Xinjiang, northeastern Inner Mongolia and northern Northeast China, while below-normal precipitation was observed across most other areas. In addition, two cold wave events, two sand-dust events and five heavy fog events successively affected China in this month. Among them, the sand-dust event from 20 to 23 was the most extensive and intense dust weather process, and the rain and snow event from 25 to 27 was the heaviest and most extensive precipitation process in February
In February 2026, the Northern Hemisphere polar vortex exhibited a dipole pattern, with its primary center located over northern Canada and a secondary center over the region from Novaya Zemlya to the Sea of Okhotsk in the Eastern Hemisphere. Most mid-to-high latitude areas of China were situated in the front of high pressure ridge, where geopotential heights were dominated by positive anomalies. The main body of cold air activity was relatively weak, resulting in above-normal temperatures in most parts of China. The national average temperature was 2.1℃ higher than the corresponding climatological normal, ranking the third highest for February since 1961. The position and intensity of the southern branch trough this month were close to normal. With the phased cold air activities in northern China, warm and cold air masses converged frequently over northern China, leading to numerous rain and snow events in the northern regions. Four notable precipitation processes occurred nationwide in February, with uneven spatial distribution. Precipitation was above normal in southern North China, Huanghuai Region, south-eastern Northwest China, northern Xinjiang, northeastern Inner Mongolia and northern Northeast China, while below-normal precipitation was observed across most other areas. In addition, two cold wave events, two sand-dust events and five heavy fog events successively affected China in this month. Among them, the sand-dust event from 20 to 23 was the most extensive and intense dust weather process, and the rain and snow event from 25 to 27 was the heaviest and most extensive precipitation process in February
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ISSN:1000-0526 CN:11-2282/P