ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 52,Issue 3,2026 Table of Contents
2026, 52(3):257-272.
DOI: 10.7519/j.issn.1000-0526.2026.030902
Abstract:
The Meiyu frontal heavy rainfall is a heavy precipitation phenomenon of China’s Yangtze River Basin and the East Asian Region. Conducting integrative field experiments on Meiyu frontal heavy rainfall to thoroughly study the thermodynamic and dynamic processes, moisture transport, microphysical structure, and their evolution mechanisms has important scientific value. In summer, over 40% of the moisture for precipitation in the Yangtze River Basin originate from the Indian Ocean and the Bay of Bengal in South Asia. Moreover, there is also a moisture pathway from the Qinghai-Xizang Plateau, and more than 60% of the precipitation are closely related to the plateau and weather systems on its eastern side. Therefore, the observational study of the South Asian moisture transport pathway to the Yangtze River Basin and of the vertical structural evolution of eastward-moving plateau cloud clusters is very important. To this end, the Wuhan Institute of Heavy Rain of China Meteorological Administration conducted in-depth studies on the mechanism for moisture to be transported from South Asia and the southern slope of Qinghai-Xizang Plateau into the Yangtze River Basin, on the mechanism how eastward-moving plateau cloud clusters affect Meiyu frontal heavy rainfall, and on the frontal structure of the Meiyu frontal surface, cloud microphysical processes, and the upstream to downstream effects of the Meiyu frontal system. They have established a field experiment system for heavy rainfall spanning from moisture source regions and plateau-system source areas to the middle and lower reaches of Yangtze River, and carried out a series of field experiments, which include observation of Meiyu frontal surface and mesoscale systems of heavy rainfall, observation of typical orographic heavy rainfall, observation of the vertical structure of eastward-moving plateau cloud clusters, observation of the characteristics and evolution of the moisture channel in the Yarlung Zangbo Grand Canyon, and tracking observation of extreme heavy rainfall systems. The research results can provide useful references for organizing similar experiments in the future.
The Meiyu frontal heavy rainfall is a heavy precipitation phenomenon of China’s Yangtze River Basin and the East Asian Region. Conducting integrative field experiments on Meiyu frontal heavy rainfall to thoroughly study the thermodynamic and dynamic processes, moisture transport, microphysical structure, and their evolution mechanisms has important scientific value. In summer, over 40% of the moisture for precipitation in the Yangtze River Basin originate from the Indian Ocean and the Bay of Bengal in South Asia. Moreover, there is also a moisture pathway from the Qinghai-Xizang Plateau, and more than 60% of the precipitation are closely related to the plateau and weather systems on its eastern side. Therefore, the observational study of the South Asian moisture transport pathway to the Yangtze River Basin and of the vertical structural evolution of eastward-moving plateau cloud clusters is very important. To this end, the Wuhan Institute of Heavy Rain of China Meteorological Administration conducted in-depth studies on the mechanism for moisture to be transported from South Asia and the southern slope of Qinghai-Xizang Plateau into the Yangtze River Basin, on the mechanism how eastward-moving plateau cloud clusters affect Meiyu frontal heavy rainfall, and on the frontal structure of the Meiyu frontal surface, cloud microphysical processes, and the upstream to downstream effects of the Meiyu frontal system. They have established a field experiment system for heavy rainfall spanning from moisture source regions and plateau-system source areas to the middle and lower reaches of Yangtze River, and carried out a series of field experiments, which include observation of Meiyu frontal surface and mesoscale systems of heavy rainfall, observation of typical orographic heavy rainfall, observation of the vertical structure of eastward-moving plateau cloud clusters, observation of the characteristics and evolution of the moisture channel in the Yarlung Zangbo Grand Canyon, and tracking observation of extreme heavy rainfall systems. The research results can provide useful references for organizing similar experiments in the future.
2026, 52(3):273-286.
DOI: 10.7519/j.issn.1000-0526.2025.091301
Abstract:
In order to study the characteristics of convective cloud precipitation in Mount Oomolangma Region (MOR), the comprehensive observation with multi-source remote sensing detection equipment for convective cloud precipitation on the northern slope of Mount Oomolangma about 6 years was conducted based on the second comprehensive scientific investigation and research plan of the Qinghai-Xizang Plateau. In this paper, the methods such as data quality control, data retrieval and data statistics are adopted to carry out comparative analysis, and the results show that precipitation in the MOR has unique structural characteristics of convective cloud precipitation compared to the precipitation in other regions. In terms of macroscopic characteristics, the MOR precipitation occurs frequently but has a short duration, most of which are isolated convective cell. The horizontal scale and vertical extension thickness of the precipitation are much smaller than in other areas, and rainfall rate is weak in the boundary layer. As for microscopic characteristics, there are two phases of precipitation (graupel and liquid raindrops) in the boundary layer under different vertical profile distributions of atmospheric temperature, with distinctive characteristics of a narrow raindrop size distribution width, higher raindrop number concentration and smaller raindrop equivalent diameter. This study may bridge the gap in detailed observations of the synoptic structure of the convective cloud precipitation on the northern slope of the MOR in summer and provide a significant reference for further researches on the changes in the weather conditions in Qinghai-Xizang Plateau and their potential impacts on the East Asian climate.
In order to study the characteristics of convective cloud precipitation in Mount Oomolangma Region (MOR), the comprehensive observation with multi-source remote sensing detection equipment for convective cloud precipitation on the northern slope of Mount Oomolangma about 6 years was conducted based on the second comprehensive scientific investigation and research plan of the Qinghai-Xizang Plateau. In this paper, the methods such as data quality control, data retrieval and data statistics are adopted to carry out comparative analysis, and the results show that precipitation in the MOR has unique structural characteristics of convective cloud precipitation compared to the precipitation in other regions. In terms of macroscopic characteristics, the MOR precipitation occurs frequently but has a short duration, most of which are isolated convective cell. The horizontal scale and vertical extension thickness of the precipitation are much smaller than in other areas, and rainfall rate is weak in the boundary layer. As for microscopic characteristics, there are two phases of precipitation (graupel and liquid raindrops) in the boundary layer under different vertical profile distributions of atmospheric temperature, with distinctive characteristics of a narrow raindrop size distribution width, higher raindrop number concentration and smaller raindrop equivalent diameter. This study may bridge the gap in detailed observations of the synoptic structure of the convective cloud precipitation on the northern slope of the MOR in summer and provide a significant reference for further researches on the changes in the weather conditions in Qinghai-Xizang Plateau and their potential impacts on the East Asian climate.
2026, 52(3):287-300.
DOI: 10.7519/j.issn.1000-0526.2025.101501
Abstract:
On the night of 12 July 2023, a rarely-seen extreme torrential rain event occurred near Feixian County in Shandong Province, under the influence of a severe precipitation supercell. This study comprehensively uses multi-source observational data and ERA5 reanalysis data in investigating the circulation background, coupling mechanism of upper- and lower-level jets, characteristics of meso- and small-scale weather systems, and microphysical features of precipitation associated with this extreme event. The results show that this event was driven by the synergistic effect of multi-scale systems. Favorable circulation patterns, ambient wind fields, and water vapor transport provided large-scale background conditions for the short-time heavy rainfall. The rapidly developing southwest boundary layer jet (SW-BLJ) coupled with the synoptic-scale southwest low-level jet (SW-LLJ) over southern Shandong enhanced upward motion in the middle and lower troposphere. Meanwhile, convergence in the middle and lower troposphere, combined with upper-level divergence in the northeastern divergent zone of the South Asian high, further strengthened deep-layer upward motion. Under the combined influence of the double low-level jets, mesoscale convective systems developed intensely in southern Shandong, promoting the occurrence of heavy rainfall. Furthermore, the event exhibited mixed characteristics of both tropical maritime and continental convective types. At the initial stage of precipitation, there was a sharp increase in raindrop number concentration and a significant enlargement of raindrop diameters. During the peak rainfall period, small particles with a mass-weighted mean diameter (Dm)<2 mm accounted for nearly 85.00%, and the number of large raindrops increased with the intensification of rainfall intensity. In terms of contribution to precipitation, although medium-sized particles (2-3 mm) only accounted for 11.77%, they contributed most to precipitation, reaching 32.74%, followed by small particles (1-2 mm). Although particles with Dm<1 mm were the most numerous, accounting for 43.35%, their contribution to precipitation was less than 4.00%. The severe precipitation supercell stage showed an “upscaling” feature. Particles in the 3-4 mm range, although accounting for less than 6.00% in terms of frequency, contributed 30.04% of the total precipitation, while particles with Dm>6 mm contributed 8.81% to the precipitation.
On the night of 12 July 2023, a rarely-seen extreme torrential rain event occurred near Feixian County in Shandong Province, under the influence of a severe precipitation supercell. This study comprehensively uses multi-source observational data and ERA5 reanalysis data in investigating the circulation background, coupling mechanism of upper- and lower-level jets, characteristics of meso- and small-scale weather systems, and microphysical features of precipitation associated with this extreme event. The results show that this event was driven by the synergistic effect of multi-scale systems. Favorable circulation patterns, ambient wind fields, and water vapor transport provided large-scale background conditions for the short-time heavy rainfall. The rapidly developing southwest boundary layer jet (SW-BLJ) coupled with the synoptic-scale southwest low-level jet (SW-LLJ) over southern Shandong enhanced upward motion in the middle and lower troposphere. Meanwhile, convergence in the middle and lower troposphere, combined with upper-level divergence in the northeastern divergent zone of the South Asian high, further strengthened deep-layer upward motion. Under the combined influence of the double low-level jets, mesoscale convective systems developed intensely in southern Shandong, promoting the occurrence of heavy rainfall. Furthermore, the event exhibited mixed characteristics of both tropical maritime and continental convective types. At the initial stage of precipitation, there was a sharp increase in raindrop number concentration and a significant enlargement of raindrop diameters. During the peak rainfall period, small particles with a mass-weighted mean diameter (Dm)<2 mm accounted for nearly 85.00%, and the number of large raindrops increased with the intensification of rainfall intensity. In terms of contribution to precipitation, although medium-sized particles (2-3 mm) only accounted for 11.77%, they contributed most to precipitation, reaching 32.74%, followed by small particles (1-2 mm). Although particles with Dm<1 mm were the most numerous, accounting for 43.35%, their contribution to precipitation was less than 4.00%. The severe precipitation supercell stage showed an “upscaling” feature. Particles in the 3-4 mm range, although accounting for less than 6.00% in terms of frequency, contributed 30.04% of the total precipitation, while particles with Dm>6 mm contributed 8.81% to the precipitation.
2026, 52(3):301-311.
DOI: 10.7519/j.issn.1000-0526.2025.092801
Abstract:
This article utilizes North China regional radar composite reflectivity factor mosaic products, single-site radar data from northern Shanxi, ERA5 reanalysis data, and surface observation data collected in the warm season (May-September) of 2021-2023, and conducts statistical analyses on the changing characteristics of squall lines descending mountains in this region. The results show that a total of 29 squall lines are identified, and following the moving directions of the squall lines, they are classified into four types: west-moving, northwest-moving, north-moving, and basin-originating. Then, based on their intensity changes when descending mountains, they are classified into three types: intensifying, weak-ening, and maintaining types, of which the weakening type is the most common, accounting for 67% of the total. All west-moving squall lines belong to the weakening type upon descent, while all north-moving types of squall lines are of the intensifying type. The northwest-moving type includes squall lines that intensify, weaken or maintain the intensity upon descent. Analysis of the environmental background ahead of the descending path for intensifying and weakening types within the northwest-moving squall lines reveals that, compared to weakening squall lines, the intensifying ones exhibit slightly stronger dynamic conditions, while conditions related to moisture, convective available potential energy and vertical wind shear are comparable or slightly poorer. Therefore, it’s difficult to accurately forecast whether a squall line will intensify or weaken upon descent based solely on the environmental conditions ahead of its path. For short-time nowcasting, radar data can be used to forecast whether a squall line will intensify or weaken upon descent. The intensifying squall lines upon descent typically exhibit stronger echo intensity, with moving speed around 17 m·s-1. Large gradient zone of reflectivity factor is concentrated in the front section of a squall line, with an overall bow-shaped squall line accompanied by a gust front. Radial velocity cross-section shows a distinct organized structure with forward inflow ascending slantwise along the rear inflow. In contrast, the weakening squall lines upon descent typically have weak or moderate echo intensity, and their moving speeds are generally below 10 m·s-1. Large gradient zone of reflectivity factor appears at the rear part of a squall line, which has overall a straighter shape, not accompanied by gust front. On the radial velocity cross-section, there is no distinct organized structure with forward inflow ascending slantwise along the rear inflow.
This article utilizes North China regional radar composite reflectivity factor mosaic products, single-site radar data from northern Shanxi, ERA5 reanalysis data, and surface observation data collected in the warm season (May-September) of 2021-2023, and conducts statistical analyses on the changing characteristics of squall lines descending mountains in this region. The results show that a total of 29 squall lines are identified, and following the moving directions of the squall lines, they are classified into four types: west-moving, northwest-moving, north-moving, and basin-originating. Then, based on their intensity changes when descending mountains, they are classified into three types: intensifying, weak-ening, and maintaining types, of which the weakening type is the most common, accounting for 67% of the total. All west-moving squall lines belong to the weakening type upon descent, while all north-moving types of squall lines are of the intensifying type. The northwest-moving type includes squall lines that intensify, weaken or maintain the intensity upon descent. Analysis of the environmental background ahead of the descending path for intensifying and weakening types within the northwest-moving squall lines reveals that, compared to weakening squall lines, the intensifying ones exhibit slightly stronger dynamic conditions, while conditions related to moisture, convective available potential energy and vertical wind shear are comparable or slightly poorer. Therefore, it’s difficult to accurately forecast whether a squall line will intensify or weaken upon descent based solely on the environmental conditions ahead of its path. For short-time nowcasting, radar data can be used to forecast whether a squall line will intensify or weaken upon descent. The intensifying squall lines upon descent typically exhibit stronger echo intensity, with moving speed around 17 m·s-1. Large gradient zone of reflectivity factor is concentrated in the front section of a squall line, with an overall bow-shaped squall line accompanied by a gust front. Radial velocity cross-section shows a distinct organized structure with forward inflow ascending slantwise along the rear inflow. In contrast, the weakening squall lines upon descent typically have weak or moderate echo intensity, and their moving speeds are generally below 10 m·s-1. Large gradient zone of reflectivity factor appears at the rear part of a squall line, which has overall a straighter shape, not accompanied by gust front. On the radial velocity cross-section, there is no distinct organized structure with forward inflow ascending slantwise along the rear inflow.
5 A Deep Learning-Based Method for Synoptic Situation Similarity Forecasting: Synoptic Similarity Net
2026, 52(3):312-324.
DOI: 10.7519/j.issn.1000-0526.2025.122501
Abstract:
Analog forecasting is a widely adopted statistical method in operational meteorological services. Traditional single-layer similarity approaches have such limitations as the lack of three-dimensional spatial information, the unstable performance of single similarity criteria, and the frequent interference from synoptic system pattern and intensity (magnitude). To address these challenges and explore the feasibility of deep learning models in synoptic situation recognition and forecasting, in this study we develop a novel approach using the ECMWF fifth-generation reanalysis (ERA5) dataset. We construct a deep learning architecture that integrates convolutional neural networks (CNN) with Transformer modules, incorporating self-attention mechanisms. Verification shows that this model can effectively capture three-dimensional spatial features of synoptic situation. Then, utilizing the extracted feature vectors, we design a comprehensive similarity framework that combines three complementary metrics: Pearson correlation (empha-sizing pattern shape), Euclidean distance (emphasizing magnitude), and Chebyshev distance (considering both shape and magnitude). This integration forms our proposed method: Synoptic Similarity Net. Finally, the operational application effect of this method is tested and evaluated in detail. The results indicate that this method can achieve the highest average structural similarity index (SSIM) and lowest mean squared error (MSE) relative to the traditional methods, demonstrating significant improvements in both metrics. Case studies across seasons confirm that the historical analogs identified by Synoptic Similarity Net exhibit both greater numerical accuracy and superior spatial pattern consistency compared to the original synoptic fields. These results demonstrate the promising potential of this method for meteorological operational applications.
Analog forecasting is a widely adopted statistical method in operational meteorological services. Traditional single-layer similarity approaches have such limitations as the lack of three-dimensional spatial information, the unstable performance of single similarity criteria, and the frequent interference from synoptic system pattern and intensity (magnitude). To address these challenges and explore the feasibility of deep learning models in synoptic situation recognition and forecasting, in this study we develop a novel approach using the ECMWF fifth-generation reanalysis (ERA5) dataset. We construct a deep learning architecture that integrates convolutional neural networks (CNN) with Transformer modules, incorporating self-attention mechanisms. Verification shows that this model can effectively capture three-dimensional spatial features of synoptic situation. Then, utilizing the extracted feature vectors, we design a comprehensive similarity framework that combines three complementary metrics: Pearson correlation (empha-sizing pattern shape), Euclidean distance (emphasizing magnitude), and Chebyshev distance (considering both shape and magnitude). This integration forms our proposed method: Synoptic Similarity Net. Finally, the operational application effect of this method is tested and evaluated in detail. The results indicate that this method can achieve the highest average structural similarity index (SSIM) and lowest mean squared error (MSE) relative to the traditional methods, demonstrating significant improvements in both metrics. Case studies across seasons confirm that the historical analogs identified by Synoptic Similarity Net exhibit both greater numerical accuracy and superior spatial pattern consistency compared to the original synoptic fields. These results demonstrate the promising potential of this method for meteorological operational applications.
2026, 52(3):325-336.
DOI: 10.7519/j.issn.1000-0526.2025.091601
Abstract:
Based on thunderstorm gale cases in Sichuan Basin from March 1 to September 30 in 2018-2022, combined with three-dimensional radar mosaic data and surface maximum wind observations, this paper constructs a thunderstorm gale sample dataset and develops a grid-point thunderstorm gale warning model. Independent validation is performed on thunderstorm gale events in 2023 and the warning performance of four models is evaluated. The results show that the LightGBM model achieves the highest probability of detection (POD), reaching 0.536 at a 15 min lead time and a 10 km evaluation radius, but it also exhibits the highest false alarm rate (FAR). The random forest model demonstrates the optimal comprehensive performance, with the highest critical success index (CSI) being 0.306 at a 30 min lead time and a 10 km evaluation radius. Both CSI and POD decrease significantly with prolonging warning lead time or decreasing evaluation radius, with a particularly notable decline in CSI when the lead time extends from 30 to 45 min. Synoptic conditions significantly influence the warning performance. Under pronounced cold air influence, factors such as echo intensity, echo top height, and 45 dBz echo top height are more likely to have high values, favoring the development of severe convection. However, newly initiated storms at convective fronts often lead to the increase in missed detections. In the absence of strong cold air, thunderstorm gales mainly occur at the leading edge of convective systems, resulting in higher POD. The temporal variation of vertically integrated liquid water content contributes most to the decision-making of models, followed by vertically integrated liquid water content density, echo top height, and maximum reflectivity factor. This highlights the central role of deep convection in the generation of thunderstorm gales. In the scenarios without cold air intrusion, downdrafts play a dominant role in thunderstorm gale warnings. Analysis of key feature values and high SHAP values reveals that temporal variations in convective echoes are critical for effective warnings. Samples with high echo-tracking wind speeds often correspond to positive SHAP values, indicating an increasing probability of convective wind events when echo motion accelerates.
Based on thunderstorm gale cases in Sichuan Basin from March 1 to September 30 in 2018-2022, combined with three-dimensional radar mosaic data and surface maximum wind observations, this paper constructs a thunderstorm gale sample dataset and develops a grid-point thunderstorm gale warning model. Independent validation is performed on thunderstorm gale events in 2023 and the warning performance of four models is evaluated. The results show that the LightGBM model achieves the highest probability of detection (POD), reaching 0.536 at a 15 min lead time and a 10 km evaluation radius, but it also exhibits the highest false alarm rate (FAR). The random forest model demonstrates the optimal comprehensive performance, with the highest critical success index (CSI) being 0.306 at a 30 min lead time and a 10 km evaluation radius. Both CSI and POD decrease significantly with prolonging warning lead time or decreasing evaluation radius, with a particularly notable decline in CSI when the lead time extends from 30 to 45 min. Synoptic conditions significantly influence the warning performance. Under pronounced cold air influence, factors such as echo intensity, echo top height, and 45 dBz echo top height are more likely to have high values, favoring the development of severe convection. However, newly initiated storms at convective fronts often lead to the increase in missed detections. In the absence of strong cold air, thunderstorm gales mainly occur at the leading edge of convective systems, resulting in higher POD. The temporal variation of vertically integrated liquid water content contributes most to the decision-making of models, followed by vertically integrated liquid water content density, echo top height, and maximum reflectivity factor. This highlights the central role of deep convection in the generation of thunderstorm gales. In the scenarios without cold air intrusion, downdrafts play a dominant role in thunderstorm gale warnings. Analysis of key feature values and high SHAP values reveals that temporal variations in convective echoes are critical for effective warnings. Samples with high echo-tracking wind speeds often correspond to positive SHAP values, indicating an increasing probability of convective wind events when echo motion accelerates.
ZHANG Yefang
,
WU Qishu
,
LI Tingting
,
FENG Zhenzhen
,
LIU Bing
,
HUANG Lingguang
,
HE Qingfang
,
LIAO Kuo
2026, 52(3):337-347.
DOI: 10.7519/j.issn.1000-0526.2026.010501
Abstract:
Based on the dual-polarization radar particle classification products and cloud-to-ground lightning location data from Fuzhou, Xiamen and Longyan from April 2022 to June 2024, in this study we select 54 convective storm samples with or without cloud-to-ground lightning and investigate the temporal variation characteristics of graupel particle layer thickness during the developing processes of these samples. The results show that the graupel particle layer thickness of convective storms with cloud-to-ground lightning reaches at least 2.26 km at the initiation moment of the first cloud-to-ground lightning, while 95% of the samples without cloud-to-ground lightning, have graupel particle layer thickness less than 2.2 km throughout their lifecycles. Considering both the lead time and accuracy of cloud-to-ground lightning initiation forecasts, we propose to use a graupel particle layer thickness greater than 2 km as a forecast indicator for cloud-to-ground lightning initiation in Fujian Province. The forecast potential is evaluated, and the calculated TS score of the sample is 0.864 and the average forecast lead time is 28.13 min. Taking four convective storms on 14 June 2022 in southern Fujian Province as examples, the above-mentioned forecast indicators are applied and analyzed in these cases. The results show that the proposed forecast indicator can accurately forecast whether cloud-to-ground lightning will occur in the four convective storms, and the forecast lead time for cloud-to-ground lightning initiation is 6 min.
Based on the dual-polarization radar particle classification products and cloud-to-ground lightning location data from Fuzhou, Xiamen and Longyan from April 2022 to June 2024, in this study we select 54 convective storm samples with or without cloud-to-ground lightning and investigate the temporal variation characteristics of graupel particle layer thickness during the developing processes of these samples. The results show that the graupel particle layer thickness of convective storms with cloud-to-ground lightning reaches at least 2.26 km at the initiation moment of the first cloud-to-ground lightning, while 95% of the samples without cloud-to-ground lightning, have graupel particle layer thickness less than 2.2 km throughout their lifecycles. Considering both the lead time and accuracy of cloud-to-ground lightning initiation forecasts, we propose to use a graupel particle layer thickness greater than 2 km as a forecast indicator for cloud-to-ground lightning initiation in Fujian Province. The forecast potential is evaluated, and the calculated TS score of the sample is 0.864 and the average forecast lead time is 28.13 min. Taking four convective storms on 14 June 2022 in southern Fujian Province as examples, the above-mentioned forecast indicators are applied and analyzed in these cases. The results show that the proposed forecast indicator can accurately forecast whether cloud-to-ground lightning will occur in the four convective storms, and the forecast lead time for cloud-to-ground lightning initiation is 6 min.
2026, 52(3):348-357.
DOI: 10.7519/j.issn.1000-0526.2025.071701
Abstract:
Low-altitude flight activities are highly susceptible to complex wind fields, so civil aviation me-teorological departments place significant emphasis on refined monitoring and early warning technologies of low-altitude winds. This study analyzes a cold front strong wind case at Guanghan Airport on 26 December 2021, by using the data from Doppler wind lidar and airport automatic observation equipment, as well as reanalysis data. The results indicate that the Doppler wind lidar can clearly capture that during the influence of cold air, surface strong winds occur approximately 4-6 h after the low-level jet weakens, with significant in homogeneity in downward momentum transfer and vertical motion changes. Sinking motion enhances the downward momentum transfer of the low-level jet, which result in increased surface wind speeds, while upward motion hinders downward momentum transfer, leading to intermittent surface strong winds. The 12° PPI scanning of the Doppler wind lidar can provide early warnings of potential strong winds along the glide path and at the surface 1.0-1.5 h in advance. The intrusion of residual cold air behind the front in the study case caused a secondary wind event, which should be taken into account when forecasting strong winds. This study demonstrates that Doppler wind lidar can significantly improve the monitoring precision of low-altitude wind field evolution, and could provide valuable insights for ensur-ing the safety and efficiency of low-altitude flight operations.
Low-altitude flight activities are highly susceptible to complex wind fields, so civil aviation me-teorological departments place significant emphasis on refined monitoring and early warning technologies of low-altitude winds. This study analyzes a cold front strong wind case at Guanghan Airport on 26 December 2021, by using the data from Doppler wind lidar and airport automatic observation equipment, as well as reanalysis data. The results indicate that the Doppler wind lidar can clearly capture that during the influence of cold air, surface strong winds occur approximately 4-6 h after the low-level jet weakens, with significant in homogeneity in downward momentum transfer and vertical motion changes. Sinking motion enhances the downward momentum transfer of the low-level jet, which result in increased surface wind speeds, while upward motion hinders downward momentum transfer, leading to intermittent surface strong winds. The 12° PPI scanning of the Doppler wind lidar can provide early warnings of potential strong winds along the glide path and at the surface 1.0-1.5 h in advance. The intrusion of residual cold air behind the front in the study case caused a secondary wind event, which should be taken into account when forecasting strong winds. This study demonstrates that Doppler wind lidar can significantly improve the monitoring precision of low-altitude wind field evolution, and could provide valuable insights for ensur-ing the safety and efficiency of low-altitude flight operations.
ZHOU Xingyan
,
CHEN Yixiao
,
YANG Guowei
,
WANG Yaqi
,
QIAO Qi
,
HONG Haixu
,
YIN Manman
,
YIN Yizhou
,
ZHONG Hailing
2026, 52(3):358-365.
DOI: 10.7519/j.issn.1000-0526.2026.021401
Abstract:
In 2025, the global average surface temperature was 1.40℃ above pre-industrial levels and 0.52℃ above the 1991-2020 average, ranking among the top three warmest years since meteorological records began. The global ocean heat content reached a new record high, while the annual maximum Arctic sea ice extent in March fell to the lowest level in the satellite era. Against such a backdrop, extreme weather and climate events occurred frequently in many parts of the world, leading to serious disaster consequences. Rainstorms and floods severely struck Sudan, Nigeria, Indonesia, Sri Lanka and the Philippines, triggering catastrophic floods and landslides. Extreme heatwaves affected vast regions of North America, East Asia and southern Europe, fueling severe forest fires. In addition, tropical cyclones impacted the Philippines, Vietnam and the Caribbean, severe winter storms gripped North America and East Asia, and moreover, destructive tornados occurred several times in the United States.
In 2025, the global average surface temperature was 1.40℃ above pre-industrial levels and 0.52℃ above the 1991-2020 average, ranking among the top three warmest years since meteorological records began. The global ocean heat content reached a new record high, while the annual maximum Arctic sea ice extent in March fell to the lowest level in the satellite era. Against such a backdrop, extreme weather and climate events occurred frequently in many parts of the world, leading to serious disaster consequences. Rainstorms and floods severely struck Sudan, Nigeria, Indonesia, Sri Lanka and the Philippines, triggering catastrophic floods and landslides. Extreme heatwaves affected vast regions of North America, East Asia and southern Europe, fueling severe forest fires. In addition, tropical cyclones impacted the Philippines, Vietnam and the Caribbean, severe winter storms gripped North America and East Asia, and moreover, destructive tornados occurred several times in the United States.
ZENG Hongling
,
ZHAO Lin
,
ZHONG Hailing
,
GAO Hui
,
ZOU Xukai
,
ZHENG Zhihai
,
WANG Ling
,
SUN Mingyang
,
SUN Linhai
,
ZHAI Jianqing
,
YIN Yizhou
,
WANG Youmin
,
ZHOU Xingyan
,
ZHU Xiaojin
,
DAI Tanlong
,
YIN Manman
,
ZHANG Yingxian
,
FU Shuo
,
ZHAO Yuheng
,
ZHAO Junhu
,
LYU Zhuozhuo
2026, 52(3):366-373.
DOI: 10.7519/j.issn.1000-0526.2026.030301
Abstract:
Using station observation data and reanalysis data, a summary was conducted on the climatic characteristics of China in 2025. The results are as follows. China exhibited distinctly warm and wet climatic characteristics in 2025. The annual average temperature in China was 10.9℃, which is 1.0℃ above normal and ties with that in 2024, marking the highest level since 1951. The annual precipitation was 668.0 mm on average, which is more than normal by 4.5%. The major weather and climate events in 2025 are as follows. In summer, extremely severe rainstorms occurred in North China, Northeast China and Inner Mongolia, with the central and eastern parts of China experiencing the fourth-severest heat wave events since 1961. Autumn typhoons influenced South China more than normal. In addition, severe convective weather events led to significant localized disasters, while the regional meteorological droughts had periodic characteristics clearly. Moreover, there were more cold wave processes and more high wind days recorded. In spring, sand-dust events became a frequent phenomenon.
Using station observation data and reanalysis data, a summary was conducted on the climatic characteristics of China in 2025. The results are as follows. China exhibited distinctly warm and wet climatic characteristics in 2025. The annual average temperature in China was 10.9℃, which is 1.0℃ above normal and ties with that in 2024, marking the highest level since 1951. The annual precipitation was 668.0 mm on average, which is more than normal by 4.5%. The major weather and climate events in 2025 are as follows. In summer, extremely severe rainstorms occurred in North China, Northeast China and Inner Mongolia, with the central and eastern parts of China experiencing the fourth-severest heat wave events since 1961. Autumn typhoons influenced South China more than normal. In addition, severe convective weather events led to significant localized disasters, while the regional meteorological droughts had periodic characteristics clearly. Moreover, there were more cold wave processes and more high wind days recorded. In spring, sand-dust events became a frequent phenomenon.
2026, 52(3):374-384.
DOI: 10.7519/j.issn.1000-0526.2026.020101
Abstract:
The main characteristics of atmospheric circulation in December 2025 are as follows. The polar vortex in the Northern Hemisphere exhibited a dipole pattern with a negative geopotential height anomaly. The mid-to-high latitude circulation presented a four-wave pattern, while the Eurasian Continent was governed by a meridional pattern with high pressure in the west and low pressure in the east. The national average precipitation was 8.6 mm, decreasing by 28% compared to the long-term average for the same period. Furthermore, the spatial distribution of the precipitation anomaly percentage was characterized by higher values in northern China and lower values in southern China. The monthly average temperature was -1.2℃, which is 1.8℃ above that in the same period of normal years, ranking as the second highest since 1961. The cold airs were frequent, manifesting as multiple waves of intrusion occurring in stages with relatively weak intensity. In December, China experienced four cold air processes. During mid-December, the widespread cold wave occurred with the first wide range of rain and snow in winter, accompanied by a sand-dust event. Additionally, two fog-haze episodes appeared during the intermittent periods of cold air processes in mid-to-late December.
The main characteristics of atmospheric circulation in December 2025 are as follows. The polar vortex in the Northern Hemisphere exhibited a dipole pattern with a negative geopotential height anomaly. The mid-to-high latitude circulation presented a four-wave pattern, while the Eurasian Continent was governed by a meridional pattern with high pressure in the west and low pressure in the east. The national average precipitation was 8.6 mm, decreasing by 28% compared to the long-term average for the same period. Furthermore, the spatial distribution of the precipitation anomaly percentage was characterized by higher values in northern China and lower values in southern China. The monthly average temperature was -1.2℃, which is 1.8℃ above that in the same period of normal years, ranking as the second highest since 1961. The cold airs were frequent, manifesting as multiple waves of intrusion occurring in stages with relatively weak intensity. In December, China experienced four cold air processes. During mid-December, the widespread cold wave occurred with the first wide range of rain and snow in winter, accompanied by a sand-dust event. Additionally, two fog-haze episodes appeared during the intermittent periods of cold air processes in mid-to-late December.
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ISSN:1000-0526 CN:11-2282/P