ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 52,Issue 6,2026 Table of Contents
2026, 52(6):641-656.
DOI: 10.7519/j.issn.1000-0526.2026.051303
Abstract:
From 23 to 29 July 2025, extreme torrential rain occurred in Beijing. Based on multi-source observation data, ERA5 reanalysis data, RISE and RMAPS-NOW high-resolution grid analysis data, this paper analyzes the characteristics of rainfall stages, the spatio-temporal features and causes of this event. The results show that the July 2025 extreme torrential rain occurred under a stable circulation background where the subtropical high was significantly westward and northward and stronger than usual, with active tropical systems on its southern side. The lower atmosphere had warm anomalies, with a positive water vapor flux anomaly at 925 hPa and a significantly enhanced water vapor convergence zone in northern and eastern Beijing, creating conditions of high temperature, high humidity and high CAPE. This rainfall event was featured with long duration, much more accumulated precipitation, nocturnal occurrence, localization and extremity. According to the daily evolution of precipitation, the whole event is divided into four stages, corresponding to the swing of the 588 dagpm contour position of the subtropical high. Extreme torrential rain occurred in Stage 2 and Stage 3, concentrated in the northern mountainous areas of Beijing, featuring a “weak overall but strong locally” pattern, and also accompanied by extreme short-time heavy rainfall. The focus analysis on this two stages indicates that, the radar echoes in Stage 2 showed obvious backward triggering and “train effect” characteristics, lasting 4-5 h with torrential rain occurring at altitudes of 200-600 m. However, in Stage 3 there were continuous generation and dissipation of convection within banded echoes, lasting more than ten hours, with the occurrence of torrential rain mainly below the 300 m altitude. Further analysis on the causes shows that both Stage 2 and Stage 3 had favorable dynamic and topographic conditions, including the exit area of the mesoscale low-level jet, the convergence of southerly and southeasterly winds in the boundary layer, as well as the horn-shaped terrain and windward slopes of the mountains. The difference between two stages is that it was warm-sector torrential rain in the Stage 2, during which the exit area of southern boundary-layer jet was in the northern shallow mountains, the low-level convergence along the mountains was conducive to continuous triggering of convection, and the “train effect” caused the occurrence of extreme torrential rain. While the Stage 3 was influenced by a weak cold air and was related to dynamic convergence on the southern side of a shear line and a mesoscale low vortex. Accordingly, the conceptual model of the July 2025 extreme torrential rain is established.
From 23 to 29 July 2025, extreme torrential rain occurred in Beijing. Based on multi-source observation data, ERA5 reanalysis data, RISE and RMAPS-NOW high-resolution grid analysis data, this paper analyzes the characteristics of rainfall stages, the spatio-temporal features and causes of this event. The results show that the July 2025 extreme torrential rain occurred under a stable circulation background where the subtropical high was significantly westward and northward and stronger than usual, with active tropical systems on its southern side. The lower atmosphere had warm anomalies, with a positive water vapor flux anomaly at 925 hPa and a significantly enhanced water vapor convergence zone in northern and eastern Beijing, creating conditions of high temperature, high humidity and high CAPE. This rainfall event was featured with long duration, much more accumulated precipitation, nocturnal occurrence, localization and extremity. According to the daily evolution of precipitation, the whole event is divided into four stages, corresponding to the swing of the 588 dagpm contour position of the subtropical high. Extreme torrential rain occurred in Stage 2 and Stage 3, concentrated in the northern mountainous areas of Beijing, featuring a “weak overall but strong locally” pattern, and also accompanied by extreme short-time heavy rainfall. The focus analysis on this two stages indicates that, the radar echoes in Stage 2 showed obvious backward triggering and “train effect” characteristics, lasting 4-5 h with torrential rain occurring at altitudes of 200-600 m. However, in Stage 3 there were continuous generation and dissipation of convection within banded echoes, lasting more than ten hours, with the occurrence of torrential rain mainly below the 300 m altitude. Further analysis on the causes shows that both Stage 2 and Stage 3 had favorable dynamic and topographic conditions, including the exit area of the mesoscale low-level jet, the convergence of southerly and southeasterly winds in the boundary layer, as well as the horn-shaped terrain and windward slopes of the mountains. The difference between two stages is that it was warm-sector torrential rain in the Stage 2, during which the exit area of southern boundary-layer jet was in the northern shallow mountains, the low-level convergence along the mountains was conducive to continuous triggering of convection, and the “train effect” caused the occurrence of extreme torrential rain. While the Stage 3 was influenced by a weak cold air and was related to dynamic convergence on the southern side of a shear line and a mesoscale low vortex. Accordingly, the conceptual model of the July 2025 extreme torrential rain is established.
2026, 52(6):657-672.
DOI: 10.7519/j.issn.1000-0526.2026.031101
Abstract:
With multi-source observations and ERA5 reanalysis data, this paper systematically analyzes an extreme torrential rain event that occurred in North China in late July 2025, and reveals the diversity in the initiation, organization and propagation mechanisms of mesoscale convective systems during three days with extreme rainfall (24, 25 and 27 July) under a similar large-scale warm-sector background. Besides, the synoptic causes for the extreme torrential rain are investigated. The results show that this extreme rainfall event was characterized by significant extremity with heavy rainfall intensity and large accumulated precipitation amount. The multiple extreme precipitation centers within the southern warm-sector rainband posed big challenges to the forecasting operation. On 24 July, the convection initiated at the foothills, and the extreme rainfall was dominated by a “train effect” from backward-propagating and band-shaped convection, which resulted in the most widespread rainfall area spanning mountains, foothills and plains. On 25 July, the convection originated in mountains, and the extreme rainfall was associated with a quasi-stationary system, leading to the localized precipitation. On July 27, the convection mostly initiated in plains and intensified after moving to the foothills. The extreme rainfall was related to the “train effect” from line-shaped convections, and the heavy precipitation occurred mainly in mountainous areas. On 24 July, the synoptic scale forcing was most significant. The strong and deep low-level jet (LLJ), coupled with orographic lifting and sustained moisture transport, played a key role in the initiation and back-propagation of the convection. The convergent frontal zone formed by the outflow of the cold pool had a very important role in organizing the rainfall system and its expansion into plains. On 25 July, under the control of the subtropical high, a weaker and shallower LLJ restricted the downstream propagation of the convective system, making orographic lifting the dominant mechanism for the triggering and enhancement of heavy precipitation. This was accompanied by a mesocyclone-like structure that produced the heaviest local hourly rainfall. On 27 July, under the weakest synoptic scale forcing, the convection was triggered by boundary layer easterly airflow and low-level weak convergence line. The mesoscale front area formed by the cold pool in mountainous areas and the warm ridge in plains provided favorable conditions for backward propagation. Moreover, the convergence and lifting of the southwest airflow in front of the terrain built powerful dynamic conditions for the significant intensification of the convection moving from plains into foothills.
With multi-source observations and ERA5 reanalysis data, this paper systematically analyzes an extreme torrential rain event that occurred in North China in late July 2025, and reveals the diversity in the initiation, organization and propagation mechanisms of mesoscale convective systems during three days with extreme rainfall (24, 25 and 27 July) under a similar large-scale warm-sector background. Besides, the synoptic causes for the extreme torrential rain are investigated. The results show that this extreme rainfall event was characterized by significant extremity with heavy rainfall intensity and large accumulated precipitation amount. The multiple extreme precipitation centers within the southern warm-sector rainband posed big challenges to the forecasting operation. On 24 July, the convection initiated at the foothills, and the extreme rainfall was dominated by a “train effect” from backward-propagating and band-shaped convection, which resulted in the most widespread rainfall area spanning mountains, foothills and plains. On 25 July, the convection originated in mountains, and the extreme rainfall was associated with a quasi-stationary system, leading to the localized precipitation. On July 27, the convection mostly initiated in plains and intensified after moving to the foothills. The extreme rainfall was related to the “train effect” from line-shaped convections, and the heavy precipitation occurred mainly in mountainous areas. On 24 July, the synoptic scale forcing was most significant. The strong and deep low-level jet (LLJ), coupled with orographic lifting and sustained moisture transport, played a key role in the initiation and back-propagation of the convection. The convergent frontal zone formed by the outflow of the cold pool had a very important role in organizing the rainfall system and its expansion into plains. On 25 July, under the control of the subtropical high, a weaker and shallower LLJ restricted the downstream propagation of the convective system, making orographic lifting the dominant mechanism for the triggering and enhancement of heavy precipitation. This was accompanied by a mesocyclone-like structure that produced the heaviest local hourly rainfall. On 27 July, under the weakest synoptic scale forcing, the convection was triggered by boundary layer easterly airflow and low-level weak convergence line. The mesoscale front area formed by the cold pool in mountainous areas and the warm ridge in plains provided favorable conditions for backward propagation. Moreover, the convergence and lifting of the southwest airflow in front of the terrain built powerful dynamic conditions for the significant intensification of the convection moving from plains into foothills.
2026, 52(6):673-685.
DOI: 10.7519/j.issn.1000-0526.2026.051301
Abstract:
Based on the surface precipitation data and the ERA5 reanalysis data, the torrential rain that occurred in North China from 23 to 29 July 2025 is investigated from the perspectives of circulation evolution and water vapor conditions. The findings reveal that the torrential rain was a across-warp type process, lasting for seven days, with the main precipitation area zonally spanning over 1000 km. Two primary rain belts were identified, located in the plateau region (secondary terrain area) and the plain region (tertiary terrain area) of North China, respectively. The anomalous large-scale circulation patterns were observed during this precipitation event. The abnormally persistent northward-shifting western Pacific subtropical high (WPSH), a stagnant westerly shortwave trough, and the shear lines generated in the lower troposphere were the key weather systems responsible for the distinct spatio-temporal distribution of precipitation compared to the past typical torrential rain events in North China. The eastward extension of the South Asian high and the westward movement of WPSH dominated the evolution of the torrential rain event and the zonal pattern of precipitation. The low-latitude weather system remained active, and the typhoons Francisco and Co-may, which emerged continuously near East China coastline, transported water vapor in a relay manner with the abnormally westward WPSH. Two water vapor transport belts and convergence zones were formed within the main precipitation area, feeding the two rain belts in the secondary and tertiary terrain regions. The water vapor budget analysis indicates that there was abundant water vapor supply during this torrential rain event, and the abnormally westward WPSH led to a southwestward transport of water vapor. Affected by the advance and retreat of the WPSH, water vapor exhibited obvious cross-topographic transport characteristics.
Based on the surface precipitation data and the ERA5 reanalysis data, the torrential rain that occurred in North China from 23 to 29 July 2025 is investigated from the perspectives of circulation evolution and water vapor conditions. The findings reveal that the torrential rain was a across-warp type process, lasting for seven days, with the main precipitation area zonally spanning over 1000 km. Two primary rain belts were identified, located in the plateau region (secondary terrain area) and the plain region (tertiary terrain area) of North China, respectively. The anomalous large-scale circulation patterns were observed during this precipitation event. The abnormally persistent northward-shifting western Pacific subtropical high (WPSH), a stagnant westerly shortwave trough, and the shear lines generated in the lower troposphere were the key weather systems responsible for the distinct spatio-temporal distribution of precipitation compared to the past typical torrential rain events in North China. The eastward extension of the South Asian high and the westward movement of WPSH dominated the evolution of the torrential rain event and the zonal pattern of precipitation. The low-latitude weather system remained active, and the typhoons Francisco and Co-may, which emerged continuously near East China coastline, transported water vapor in a relay manner with the abnormally westward WPSH. Two water vapor transport belts and convergence zones were formed within the main precipitation area, feeding the two rain belts in the secondary and tertiary terrain regions. The water vapor budget analysis indicates that there was abundant water vapor supply during this torrential rain event, and the abnormally westward WPSH led to a southwestward transport of water vapor. Affected by the advance and retreat of the WPSH, water vapor exhibited obvious cross-topographic transport characteristics.
2026, 52(6):686-701.
DOI: 10.7519/j.issn.1000-0526.2026.042701
Abstract:
Using refined hourly meteorological observations, radar data, and other multi-source observation data, along with ERA5 reanalysis data, this paper analyzes the characteristics and causes of the ultra-long-duration regional extreme torrential rain event that occurred in Beijing from 23 to 29 July 2025. The results indicate that this event was the longest-lasting heavy precipitation event in the past decade, and the number of stations with daily precipitation ≥50 mm and the daily maximum precipitation during the event ranked the second after the extreme torrential rain event on 30 July 2023. This event also exhibited significant nocturnal rainfall characteristics, with the maximum hourly rainfall intensity concentrated in the period from midnight to early morning hours. The areas of heavy precipitation were primarily distributed over the plains in front of the Yanshan Mountains and on the windward slopes. This torrential rain event was mainly influenced by the subtropical high system, low-latitude tropical weather systems, and mid-to-high-latitude westerly troughs. The analysis of the geopotential height anomaly field from July 23 to 28 reveals that the subtropical high was positioned more northward than usual. The tropical low-pressure system overlapped with the abnormally northward-shifted peripheral airflow of the subtropical high, forming a sustained southerly airflow that was transported northward to the Beijing Region, which provided abundant moisture for the extreme precipitation. The moisture content was significantly higher than the climatological average, with areas of high specific humidity overlapping with southerly wind anomalies. Coupled with the orographic uplift effect of the Yanshan Mountains, these factors significantly enhanced precipitation intensity. A prolonged high-humidity environment was maintained below 500 hPa throughout this event, with the average specific humidity at 925 hPa (14.3 g·kg-1), 850 hPa (12.2 g·kg-1), and 700 hPa (7.4 g·kg-1) exceeding the average values recorded in torrential rain events over the past decade (based on statistics from the Beijing Sounding Station). Under the daytime conditions of high temperature and humidity, unstable energy was accumulated. At night, the enhanced low-level jet, combined with terrain lifting, contributed to the pronounced nocturnal rainfall characteristics of this torrential rain event. The major impact systems of the extreme torrential rain over the night of 26 July could not be identified with the conventional data including ERA5 reanalysis data, but the radar imagery clearly reveals that the main impact systems of the extreme torrential rain were the low-level convergence line and the small-scale low-level jet.
Using refined hourly meteorological observations, radar data, and other multi-source observation data, along with ERA5 reanalysis data, this paper analyzes the characteristics and causes of the ultra-long-duration regional extreme torrential rain event that occurred in Beijing from 23 to 29 July 2025. The results indicate that this event was the longest-lasting heavy precipitation event in the past decade, and the number of stations with daily precipitation ≥50 mm and the daily maximum precipitation during the event ranked the second after the extreme torrential rain event on 30 July 2023. This event also exhibited significant nocturnal rainfall characteristics, with the maximum hourly rainfall intensity concentrated in the period from midnight to early morning hours. The areas of heavy precipitation were primarily distributed over the plains in front of the Yanshan Mountains and on the windward slopes. This torrential rain event was mainly influenced by the subtropical high system, low-latitude tropical weather systems, and mid-to-high-latitude westerly troughs. The analysis of the geopotential height anomaly field from July 23 to 28 reveals that the subtropical high was positioned more northward than usual. The tropical low-pressure system overlapped with the abnormally northward-shifted peripheral airflow of the subtropical high, forming a sustained southerly airflow that was transported northward to the Beijing Region, which provided abundant moisture for the extreme precipitation. The moisture content was significantly higher than the climatological average, with areas of high specific humidity overlapping with southerly wind anomalies. Coupled with the orographic uplift effect of the Yanshan Mountains, these factors significantly enhanced precipitation intensity. A prolonged high-humidity environment was maintained below 500 hPa throughout this event, with the average specific humidity at 925 hPa (14.3 g·kg-1), 850 hPa (12.2 g·kg-1), and 700 hPa (7.4 g·kg-1) exceeding the average values recorded in torrential rain events over the past decade (based on statistics from the Beijing Sounding Station). Under the daytime conditions of high temperature and humidity, unstable energy was accumulated. At night, the enhanced low-level jet, combined with terrain lifting, contributed to the pronounced nocturnal rainfall characteristics of this torrential rain event. The major impact systems of the extreme torrential rain over the night of 26 July could not be identified with the conventional data including ERA5 reanalysis data, but the radar imagery clearly reveals that the main impact systems of the extreme torrential rain were the low-level convergence line and the small-scale low-level jet.
BAO Hongjun
,
ZENG Sihai
,
WANG Jianwen
,
YUN Xiaobo
,
LIN Jian
,
ZHANG Bo
,
LI Zhijia
,
LUAN Chengmei
,
WANG Meng
,
XU Fengwen
2026, 52(6):702-712.
DOI: 10.7519/j.issn.1000-0526.2026.011301
Abstract:
In late July 2025, Chaobai River Basin experienced the most serious flood disaster since 1959. In this paper, a flood simulation and forecasting model is developed for Chaobai River Basin based on a distributed hydrological model to retrospectively analyze the characteristics of the July 2025 regional flood in this basin. The Zhangjiafen hydrometrical cross-section of the Baihe River, the Xiahui hydrometrical cross-section of the Chaohe River, and the Putaoyuan hydrometrical cross-section of the Qingshui River are taken for testing hydrological sections, and a basin flood simulation and forecasting model is developed based on GMKHM distributed hydrological model. The GMKHM model adopts hourly precipitation observation data from CMA regional meteorological stations as forcing input, introduces runoff curve numbers and topographic indices to develop a DEM-based over-storage runoff production model, and has a module of recharging deep groundwater added in the calculation of water source separation. The results show that the peak discharge simulation errors are -1.8% and -4.0% respectively for the Zhangjiafen hydrometrical cross-section of the Baihe River and the Xiahui cross-section of the Chaohe River under the GMKHM distributed hydrological model. The model determination coefficient is 0.87 for the Zhangjiafen hydrometrical cross-section and 0.89 for the Xiahui hydrometrical cross-section. For the Putaoyuan hydrometrical cross-section of the Qingshui River, the peak discharge error is 0.9% and the determination coefficient reaches 0.92. Overall, the GMKHM distributed hydrological model performs well in simulating the July 2025 flood event in the Chaobai River Basin.
In late July 2025, Chaobai River Basin experienced the most serious flood disaster since 1959. In this paper, a flood simulation and forecasting model is developed for Chaobai River Basin based on a distributed hydrological model to retrospectively analyze the characteristics of the July 2025 regional flood in this basin. The Zhangjiafen hydrometrical cross-section of the Baihe River, the Xiahui hydrometrical cross-section of the Chaohe River, and the Putaoyuan hydrometrical cross-section of the Qingshui River are taken for testing hydrological sections, and a basin flood simulation and forecasting model is developed based on GMKHM distributed hydrological model. The GMKHM model adopts hourly precipitation observation data from CMA regional meteorological stations as forcing input, introduces runoff curve numbers and topographic indices to develop a DEM-based over-storage runoff production model, and has a module of recharging deep groundwater added in the calculation of water source separation. The results show that the peak discharge simulation errors are -1.8% and -4.0% respectively for the Zhangjiafen hydrometrical cross-section of the Baihe River and the Xiahui cross-section of the Chaohe River under the GMKHM distributed hydrological model. The model determination coefficient is 0.87 for the Zhangjiafen hydrometrical cross-section and 0.89 for the Xiahui hydrometrical cross-section. For the Putaoyuan hydrometrical cross-section of the Qingshui River, the peak discharge error is 0.9% and the determination coefficient reaches 0.92. Overall, the GMKHM distributed hydrological model performs well in simulating the July 2025 flood event in the Chaobai River Basin.
2026, 52(6):713-725.
DOI: 10.7519/j.issn.1000-0526.2025.121901
Abstract:
Current deep learning-based QPE methods using radar reflectivity factors mostly adopt global mapping strategies, which to some extent limits the models’ ability to analyze local precipitation features. To this end, this study proposes a Dual-Branch Composite Wavelet Attention UNet (DCWA-UNet) model. The model is mainly improved from the following two aspects. Firstly, a hybrid architecture consisting of a dual-branch encoder (main branch + simplified convolutional downsampling branch) and a feature aggregation subnetwork is designed, which enables the end-to-end mapping from radar volume scan data to station precipitation intensity, thereby constructing a station-centered sample system. Secondly, a composite wavelet attention module (CWAM) is introduced to enhance the model’s representation capability for radar echoes through multi-scale feature decomposition and dynamic weight allocation. Meanwhile, a weighted mean squared error loss function is adopted to emphasize the gradient contribution of moderate-to-high precipitation. Using ground-based precipitation observation and radar observation data in the Sichuan Basin during the summers of 2019-2021, a dataset containing 4020 samples is constructed for model training and testing, and comparative experiments are conducted with deep learning models such as SimVP. The results show that DCWA-UNet achieves obvious comprehensive performance advantages under different precipitation intensities, with particularly significant improvements in critical success index (CSI) and mean absolute error (MAE) within the precipitation intensity below 30 mm·h-1. For precipitation intensities of ［5, 10) mm·h-1, the CSI of DCWA-UNet is significantly higher than that of SimVP and other comparative models, and the MAE is reduced by 4.9% compared to SimVP; for precipitation intensities of ［10, 30］ mm·h-1, the CSI is improved by 4.0% and the MAE is reduced by 4.0% compared to SimVP. Moreover, the false alarm rate is the lowest among all comparative models.
Current deep learning-based QPE methods using radar reflectivity factors mostly adopt global mapping strategies, which to some extent limits the models’ ability to analyze local precipitation features. To this end, this study proposes a Dual-Branch Composite Wavelet Attention UNet (DCWA-UNet) model. The model is mainly improved from the following two aspects. Firstly, a hybrid architecture consisting of a dual-branch encoder (main branch + simplified convolutional downsampling branch) and a feature aggregation subnetwork is designed, which enables the end-to-end mapping from radar volume scan data to station precipitation intensity, thereby constructing a station-centered sample system. Secondly, a composite wavelet attention module (CWAM) is introduced to enhance the model’s representation capability for radar echoes through multi-scale feature decomposition and dynamic weight allocation. Meanwhile, a weighted mean squared error loss function is adopted to emphasize the gradient contribution of moderate-to-high precipitation. Using ground-based precipitation observation and radar observation data in the Sichuan Basin during the summers of 2019-2021, a dataset containing 4020 samples is constructed for model training and testing, and comparative experiments are conducted with deep learning models such as SimVP. The results show that DCWA-UNet achieves obvious comprehensive performance advantages under different precipitation intensities, with particularly significant improvements in critical success index (CSI) and mean absolute error (MAE) within the precipitation intensity below 30 mm·h-1. For precipitation intensities of ［5, 10) mm·h-1, the CSI of DCWA-UNet is significantly higher than that of SimVP and other comparative models, and the MAE is reduced by 4.9% compared to SimVP; for precipitation intensities of ［10, 30］ mm·h-1, the CSI is improved by 4.0% and the MAE is reduced by 4.0% compared to SimVP. Moreover, the false alarm rate is the lowest among all comparative models.
ZHANG Zeyu
,
BAI Lanqiang
,
CAI Kanglong
,
HUANG Shuting
,
HUANG Xianxiang
,
YANG Lei
,
XU Zongheng
,
WANG Xiuming
,
ZHANG Tao
,
LI Cailing
,
CHEN Pakwai
2026, 52(6):726-741.
DOI: 10.7519/j.issn.1000-0526.2026.050701
Abstract:
Basic characteristics of tornadoes in China in 2024 are presented in this article based on comprehensive analysis of multi-source data and on-site ground damage surveys. Despite the potential underestimation arising from the limitation of observations and the inherently stochastic nature of public reports, a total of 78 tornadoes and 40 waterspouts are identified. The results show that these tornadoes were primarily concentrated in Northeast China, the Beijing-Tianjin-Hebei Region, the Huang-Huai Region and the Pearl River Delta in 2024. Most tornadoes occurred between June and September, with July seeing tornadoes most frequently and their diurnal peaks in the afternoon. In addition, the intensities of 51 tornadoes are classified based on damage surveys into 12 EF0, 26 EF1, 9 EF2, and 4 EF3. In terms of waterspouts, they primarily occurred along the coastal region of South China and in the Bohai Bay, and most of them occurred in August with a pronounced diurnal peak around 06:00 BT. Overall, the tornadoes in 2024 were characterized by a high frequency of mass outbreaks, with four times of mass tornado events accounting for 44% of the annual total tornadoes. Notably, there were 13 tornadoes generated associated with an upper-level trough in Shandong Province on 5 July, making the province experiencing the highest occurrence frequency of tornadoes in 2024. Under the influence of the strong 2023 winter El Nio event, convective available potential energy in South China in spring 2024 was significantly higher than the climatological average. With such a favorable thermodynamic environment, 67% of tornadoes in Guangdong Province occurred from late March to late April, reflecting an unusually high early-spring concentration. Additionally, a high-impact nocturnal severe weather event in Nanchang, Jiangxi Province, underscored the persistent difficulties in identifying and confirming nighttime tornadoes.
Basic characteristics of tornadoes in China in 2024 are presented in this article based on comprehensive analysis of multi-source data and on-site ground damage surveys. Despite the potential underestimation arising from the limitation of observations and the inherently stochastic nature of public reports, a total of 78 tornadoes and 40 waterspouts are identified. The results show that these tornadoes were primarily concentrated in Northeast China, the Beijing-Tianjin-Hebei Region, the Huang-Huai Region and the Pearl River Delta in 2024. Most tornadoes occurred between June and September, with July seeing tornadoes most frequently and their diurnal peaks in the afternoon. In addition, the intensities of 51 tornadoes are classified based on damage surveys into 12 EF0, 26 EF1, 9 EF2, and 4 EF3. In terms of waterspouts, they primarily occurred along the coastal region of South China and in the Bohai Bay, and most of them occurred in August with a pronounced diurnal peak around 06:00 BT. Overall, the tornadoes in 2024 were characterized by a high frequency of mass outbreaks, with four times of mass tornado events accounting for 44% of the annual total tornadoes. Notably, there were 13 tornadoes generated associated with an upper-level trough in Shandong Province on 5 July, making the province experiencing the highest occurrence frequency of tornadoes in 2024. Under the influence of the strong 2023 winter El Nio event, convective available potential energy in South China in spring 2024 was significantly higher than the climatological average. With such a favorable thermodynamic environment, 67% of tornadoes in Guangdong Province occurred from late March to late April, reflecting an unusually high early-spring concentration. Additionally, a high-impact nocturnal severe weather event in Nanchang, Jiangxi Province, underscored the persistent difficulties in identifying and confirming nighttime tornadoes.
2026, 52(6):742-749.
DOI: 10.7519/j.issn.1000-0526.2026.022501
Abstract:
This paper proposes an online performance monitoring method for weather radars based on ground clutter characteristics to evaluate radar system status and enhance the stability and reliability of observation data. The method identifies stable ground clutter regions by exploiting the spatiotemporal persistence of clutter and the high occurrence probability of strong echoes. An empirical cumulative distribution function is then constructed, with the 98th percentile extracted as the monitoring indicator, and a relative calibration bias metric introduced to quantitatively characterize system deviations. A case study with the Jingzhou Radar demonstrates that this method can effectively capture stable clutter features under various weather conditions, different ranges, and anomalous propagation scenarios. Further application of the method to multiple radars in Hubei Province verify its reliability in detecting system performance biases and data anomalies, confirming its feasibility for online monitoring of weather radar performance. Therefore, this method is a strong support for improving the long-term stability of radar observations.
This paper proposes an online performance monitoring method for weather radars based on ground clutter characteristics to evaluate radar system status and enhance the stability and reliability of observation data. The method identifies stable ground clutter regions by exploiting the spatiotemporal persistence of clutter and the high occurrence probability of strong echoes. An empirical cumulative distribution function is then constructed, with the 98th percentile extracted as the monitoring indicator, and a relative calibration bias metric introduced to quantitatively characterize system deviations. A case study with the Jingzhou Radar demonstrates that this method can effectively capture stable clutter features under various weather conditions, different ranges, and anomalous propagation scenarios. Further application of the method to multiple radars in Hubei Province verify its reliability in detecting system performance biases and data anomalies, confirming its feasibility for online monitoring of weather radar performance. Therefore, this method is a strong support for improving the long-term stability of radar observations.
2026, 52(6):750-758.
DOI: 10.7519/j.issn.1000-0526.2025.081201
Abstract:
To investigate the impact of meteorological conditions on the key quality components (protein, fat, starch, and amino acids) of maize kernels, the stepwise regression-based prediction models are constructed based on the data from interval sowing experiments. The values calculated by the model are converted into quality grades and the goodness of fit and forecasting ability of the models are analyzed. The results show that all stepwise regression-based prediction models have passed the significance level test, and a relationship between maize kernel quality and meteorological factors from tasseling to milk stages and from milk to mature stages is established. Moreover, the linear relationship between maize kernel quality and meteorological factors is quantified. The results of the model test and forecast test indicate that the mean absolute percentage errors for four quality components are all below 15% and the predictions for starch and protein are closer to the observed values compared to those for fat and amino acids. The observed and predicted contents of maize kernel quality components (regardless of cropping system) are converted into grades for validation. For protein, fat and starch, the combined proportion of samples with predicted grades matching or within one grade of actual grades exceeds 90% (reaching 100% for starch), and is 76.67% for amino acids. The predicted grades aligns well with the actual grades, indicating that the prediction models exhibit high accuracy and can be used for forecasting and evaluating maize kernel quality. The findings can offer an objective and quantitative basis for optimizing environmental resource utilization to improve maize kernel quality and for informing maize ecological zoning.
To investigate the impact of meteorological conditions on the key quality components (protein, fat, starch, and amino acids) of maize kernels, the stepwise regression-based prediction models are constructed based on the data from interval sowing experiments. The values calculated by the model are converted into quality grades and the goodness of fit and forecasting ability of the models are analyzed. The results show that all stepwise regression-based prediction models have passed the significance level test, and a relationship between maize kernel quality and meteorological factors from tasseling to milk stages and from milk to mature stages is established. Moreover, the linear relationship between maize kernel quality and meteorological factors is quantified. The results of the model test and forecast test indicate that the mean absolute percentage errors for four quality components are all below 15% and the predictions for starch and protein are closer to the observed values compared to those for fat and amino acids. The observed and predicted contents of maize kernel quality components (regardless of cropping system) are converted into grades for validation. For protein, fat and starch, the combined proportion of samples with predicted grades matching or within one grade of actual grades exceeds 90% (reaching 100% for starch), and is 76.67% for amino acids. The predicted grades aligns well with the actual grades, indicating that the prediction models exhibit high accuracy and can be used for forecasting and evaluating maize kernel quality. The findings can offer an objective and quantitative basis for optimizing environmental resource utilization to improve maize kernel quality and for informing maize ecological zoning.
2026, 52(6):759-768.
DOI: 10.7519/j.issn.1000-0526.2026.060301
Abstract:
In March 2026, the Northern Hemisphere polar vortex exhibited a dipole pattern and was stronger than normal. The mid- to high-latitude circulation showed an anomalous four-wave pattern, and the circulation over Asia was relatively zonal. Positive geopotential height anomalies dominated nearly the entire Eurasian Continent. The western Pacific subtropical high was close to the climatological average, and the southern branch trough was intermittently active. The average temperature across China was 5.9℃, 1.2℃ higher than that in the same period of normal years. The national average precipitation was 35.3 mm, which was 19.6% above the normal level for the same period. Precipitation was abnormally more than normal in southern North China, Northwest China and central-western Xizang, but less in Northeast China, Jiang-Huai Region, eastnorthern and central-southern of Jiangnan Region and most parts of South China. During this month, there were two cold air processes, one sand-dust weather process and the first large-scale severe convective weather process of the year in China. Fog events occurred frequently over offshore and land-sea adjacent areas, and also over sea surface. Persistent rainy weather was significant in southern China.
In March 2026, the Northern Hemisphere polar vortex exhibited a dipole pattern and was stronger than normal. The mid- to high-latitude circulation showed an anomalous four-wave pattern, and the circulation over Asia was relatively zonal. Positive geopotential height anomalies dominated nearly the entire Eurasian Continent. The western Pacific subtropical high was close to the climatological average, and the southern branch trough was intermittently active. The average temperature across China was 5.9℃, 1.2℃ higher than that in the same period of normal years. The national average precipitation was 35.3 mm, which was 19.6% above the normal level for the same period. Precipitation was abnormally more than normal in southern North China, Northwest China and central-western Xizang, but less in Northeast China, Jiang-Huai Region, eastnorthern and central-southern of Jiangnan Region and most parts of South China. During this month, there were two cold air processes, one sand-dust weather process and the first large-scale severe convective weather process of the year in China. Fog events occurred frequently over offshore and land-sea adjacent areas, and also over sea surface. Persistent rainy weather was significant in southern China.
Mobile website
® 2026 All Rights Reserved
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
ISSN:1000-0526 CN:11-2282/P