ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 51,Issue 12,2025 Table of Contents
2025, 51(12):1581-1595.
DOI: 10.7519/j.issn.1000-0526.2025.090801
Abstract:
In order to explore the detection ability of X-band phased-array radar for extreme precipitation, based on the basic products of X-band phased-array radar and three-dimensional wind field reversal from June to August in 2023 and 2024, the dual-polarization parameter characteristics of 9 extreme precipitation events in two years’ summer and the storm structure of 3 typical precipitation processes are analyzed by statistical and diagnostic analysis methods. The results show that the mean and median values of horizontal polarization reflectivity factor (ZH), differential reflectivity factor (ZDR) and specific differential phase (KDP) below 4 km gradually increase with the increase of minutely precipitation intensity. KDP increases most obviously. The KDP of super heavy precipitation >2.5 mm·min-1 increases by 30%-246% compared with the lowest level of 1-1.5 mm·min-1 at each altitude, and increases by 15%-167% compared with the lower level of 2-2.5 mm·min-1. ZH and KDP decrease with increasing altitude, and the maximum values are in the height of 0-1 km. The dual-polarization parameters of the lower layer gradually increase 3-10 min before the extreme minutely precipitation, and decrease 2-5 min after the extreme value, with the maximum fluctuation range of KDP. Under the 2 km height, the increase of KDP before the extreme value is 133%-205%, and the decrease range after the extreme value is 49%-55%. The intensity of the minutely precipitation depends mainly on the KDP (particle concentration). By analyzing the characteristics of extreme precipitation storm, we can see that the storm belongs to backward propagation type, with a new cell born every 4-6 min on the west side and its whole life span ≥65 min. There is a convergence zone of 4-5 km thick in the storm. The ZDR maximum center of the storm is below 3 km, and larger raindrops are concentrated in the lower layer. The possible reason for the increase of KDP laging behind the increase of ZDR is that during the descending process, the raindrops first grow in size and then break up into smaller high-concentration raindrops.
In order to explore the detection ability of X-band phased-array radar for extreme precipitation, based on the basic products of X-band phased-array radar and three-dimensional wind field reversal from June to August in 2023 and 2024, the dual-polarization parameter characteristics of 9 extreme precipitation events in two years’ summer and the storm structure of 3 typical precipitation processes are analyzed by statistical and diagnostic analysis methods. The results show that the mean and median values of horizontal polarization reflectivity factor (ZH), differential reflectivity factor (ZDR) and specific differential phase (KDP) below 4 km gradually increase with the increase of minutely precipitation intensity. KDP increases most obviously. The KDP of super heavy precipitation >2.5 mm·min-1 increases by 30%-246% compared with the lowest level of 1-1.5 mm·min-1 at each altitude, and increases by 15%-167% compared with the lower level of 2-2.5 mm·min-1. ZH and KDP decrease with increasing altitude, and the maximum values are in the height of 0-1 km. The dual-polarization parameters of the lower layer gradually increase 3-10 min before the extreme minutely precipitation, and decrease 2-5 min after the extreme value, with the maximum fluctuation range of KDP. Under the 2 km height, the increase of KDP before the extreme value is 133%-205%, and the decrease range after the extreme value is 49%-55%. The intensity of the minutely precipitation depends mainly on the KDP (particle concentration). By analyzing the characteristics of extreme precipitation storm, we can see that the storm belongs to backward propagation type, with a new cell born every 4-6 min on the west side and its whole life span ≥65 min. There is a convergence zone of 4-5 km thick in the storm. The ZDR maximum center of the storm is below 3 km, and larger raindrops are concentrated in the lower layer. The possible reason for the increase of KDP laging behind the increase of ZDR is that during the descending process, the raindrops first grow in size and then break up into smaller high-concentration raindrops.
2025, 51(12):1596-1607.
DOI: 10.7519/j.issn.1000-0526.2024.122601
Abstract:
From 29 to 30 June 2023, a localized abrupt severe torrential rain occurred in the western region of Hunan Province, but forecasters and numerical models both failed to forecast the rainfall intensity. In this study, the mesoscale characteristics and possible causes of forecast biases are analyzed based on the multiple observations data, ERA5 reanalysis data and numerical forecast products. The results show that the northwest air flow behind the upper-level trough drove the cold air to the south and merged with the southwest warm-humid air flows which were strengthened at night, which led to the occurrence of this process. The severe torrential rain was generated by a backward propagation of quasi-stationary mesoscale convective system (MCS), which was composed of multiple strongly developing γ-MCSs, manifested as an organized linear echo band. Under the favorable environmental background, the long-time maintenance of the boundary layer convergence line, the wind velocity fluctuation of the low-level jet and the vertical structure of low-level convergence and high-level divergence contributed to the triggering and organization of the convective cells. The merging, strengthening backward propagation of MCS and the train effect of convective cells were important causes for the severe torrential rain. Significant errors were made in the short-time subjective forecasts because of the forecast biases of the low-level dynamic and thermodynamic fields of the numerical models, the deficiency of forecasters’ ability to correct the model forecast, and the complex topography of western Hunan Province. Therefore, it is very crucial to use the automatic weather station data, satellite data and radar data with high spatio-temporal resolution to analyze the changes of the mesoscale environmental conditions, strengthen the short-time nowcasting and issue early warning in time.
From 29 to 30 June 2023, a localized abrupt severe torrential rain occurred in the western region of Hunan Province, but forecasters and numerical models both failed to forecast the rainfall intensity. In this study, the mesoscale characteristics and possible causes of forecast biases are analyzed based on the multiple observations data, ERA5 reanalysis data and numerical forecast products. The results show that the northwest air flow behind the upper-level trough drove the cold air to the south and merged with the southwest warm-humid air flows which were strengthened at night, which led to the occurrence of this process. The severe torrential rain was generated by a backward propagation of quasi-stationary mesoscale convective system (MCS), which was composed of multiple strongly developing γ-MCSs, manifested as an organized linear echo band. Under the favorable environmental background, the long-time maintenance of the boundary layer convergence line, the wind velocity fluctuation of the low-level jet and the vertical structure of low-level convergence and high-level divergence contributed to the triggering and organization of the convective cells. The merging, strengthening backward propagation of MCS and the train effect of convective cells were important causes for the severe torrential rain. Significant errors were made in the short-time subjective forecasts because of the forecast biases of the low-level dynamic and thermodynamic fields of the numerical models, the deficiency of forecasters’ ability to correct the model forecast, and the complex topography of western Hunan Province. Therefore, it is very crucial to use the automatic weather station data, satellite data and radar data with high spatio-temporal resolution to analyze the changes of the mesoscale environmental conditions, strengthen the short-time nowcasting and issue early warning in time.
2025, 51(12):1608-1620.
DOI: 10.7519/j.issn.1000-0526.2025.060602
Abstract:
Utilizing the best track data of tropical cyclones from China Meteorological Administration (CMA) in 1949-2024, observed data of typhoon track and intensity from National Meteorological Centre (NMC) in 2024, and ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), this paper analyzes the main characteristics of typhoon activities over the Northwest Pacific and South China Sea in 2024. The results indicate that the number of generated typhoons and average maximum intensity in typhoon lifespan approximated climatological mean, with their average birth locations by north and by west. The landfall typhoon count surpassed the climatological mean with enhanced intensity and concentrated impact areas. The autumn typhoon activities significantly exceeded climatological mean. Notable inland movement of the typhoons caused extensive heavy precipitation processes. The NMC demonstrated superior track forecasting capability compared to the Japan Meteorological Agency (JMA) and the U.S. Joint Typhoon Warning Center (JTWC), while the typhoon intensity prediction errors by NMC exceeded that of JMA but was lower than that of JTWC. Key challenging issues in the operational forecasting include that, firstly, significant track forecast errors arose from numerical models’ limited capability in catching the complex structural evolution of typhoons after their landfall and their interactions with steering flows, which makes the correction to such processes highly challenging. Then, forecasting the persistence of typhoon remnant vortices over land and their associated precipitation impacts is the second challenging issue, and the third difficulty is predicting the rapid intensification (RI) of Typhoon Yagi and the prolonged maintenance of super typhoon.
Utilizing the best track data of tropical cyclones from China Meteorological Administration (CMA) in 1949-2024, observed data of typhoon track and intensity from National Meteorological Centre (NMC) in 2024, and ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), this paper analyzes the main characteristics of typhoon activities over the Northwest Pacific and South China Sea in 2024. The results indicate that the number of generated typhoons and average maximum intensity in typhoon lifespan approximated climatological mean, with their average birth locations by north and by west. The landfall typhoon count surpassed the climatological mean with enhanced intensity and concentrated impact areas. The autumn typhoon activities significantly exceeded climatological mean. Notable inland movement of the typhoons caused extensive heavy precipitation processes. The NMC demonstrated superior track forecasting capability compared to the Japan Meteorological Agency (JMA) and the U.S. Joint Typhoon Warning Center (JTWC), while the typhoon intensity prediction errors by NMC exceeded that of JMA but was lower than that of JTWC. Key challenging issues in the operational forecasting include that, firstly, significant track forecast errors arose from numerical models’ limited capability in catching the complex structural evolution of typhoons after their landfall and their interactions with steering flows, which makes the correction to such processes highly challenging. Then, forecasting the persistence of typhoon remnant vortices over land and their associated precipitation impacts is the second challenging issue, and the third difficulty is predicting the rapid intensification (RI) of Typhoon Yagi and the prolonged maintenance of super typhoon.
2025, 51(12):1621-1634.
DOI: 10.7519/j.issn.1000-0526.2025.091701
Abstract:
In 2023, tropical cyclone activity over the Northwest Pacific had the characteristics of lower frequency but higher intensity. Six damaging tropical cyclones or typhoons impacted China, and typhoon disasters exhibited a concentrated manifestation during the autumn. Driven by the transition phase of El Nino superimposed on anomalously warm sea surface temperature and associated atmospheric circulation anomalies in the North Pacific, typhoons brought record-breaking hourly precipitation extremums unseen in 1984-2022 in China, while their wind speed impacts were generally comparable to historical averages. Regarding disaster losses, these typhoons collectively resulted in 12 fatalities/missing persons, 7000 collapsed houses, an affected crop area of 34.76×104 hm2, and direct economic losses amounting to 47.49 billion yuan. All disaster loss metrics were significantly lower than the averages of 1984-2022. A pronounced discrepancy emerged when comparing standardized disaster impact indices. The percentile ranking of the Comprehensive Typhoon Disaster Impact Index was, on average, 26.1 percentage points lower than that of the Integrated Precipitation and Wind Impact Index. This significant contrast primarily stems from the effectiveness of the disaster prevention and mitigation system. Meteorological departments provided crucial support for risk preparedness through accurate forecasts for typhoon tracks and torrential rain bands. Furthermore, efficient coordination among emergency management agencies at all levels, which was achieved by comprehensive measures such as pre-disaster risk management and emergency interagency response, played a pivotal role in significantly reducing disaster losses.
In 2023, tropical cyclone activity over the Northwest Pacific had the characteristics of lower frequency but higher intensity. Six damaging tropical cyclones or typhoons impacted China, and typhoon disasters exhibited a concentrated manifestation during the autumn. Driven by the transition phase of El Nino superimposed on anomalously warm sea surface temperature and associated atmospheric circulation anomalies in the North Pacific, typhoons brought record-breaking hourly precipitation extremums unseen in 1984-2022 in China, while their wind speed impacts were generally comparable to historical averages. Regarding disaster losses, these typhoons collectively resulted in 12 fatalities/missing persons, 7000 collapsed houses, an affected crop area of 34.76×104 hm2, and direct economic losses amounting to 47.49 billion yuan. All disaster loss metrics were significantly lower than the averages of 1984-2022. A pronounced discrepancy emerged when comparing standardized disaster impact indices. The percentile ranking of the Comprehensive Typhoon Disaster Impact Index was, on average, 26.1 percentage points lower than that of the Integrated Precipitation and Wind Impact Index. This significant contrast primarily stems from the effectiveness of the disaster prevention and mitigation system. Meteorological departments provided crucial support for risk preparedness through accurate forecasts for typhoon tracks and torrential rain bands. Furthermore, efficient coordination among emergency management agencies at all levels, which was achieved by comprehensive measures such as pre-disaster risk management and emergency interagency response, played a pivotal role in significantly reducing disaster losses.
2025, 51(12):1635-1644.
DOI: 10.7519/j.issn.1000-0526.2025.073001
Abstract:
Based on the 10 min observation data from the traffic meteorological stations along the Tianjin expressway during 2018-2023, a long short-term memory network model is used to forecast the pavement temperature in the next 3 h, and the forecasting effect and spatial transfer applicability of the model in the case of extreme pavement temperatures are evaluated and analyzed. The results show that the model forecasting effect is the best when the observation data of the time step 3 h are used as the input. The model can forecast the pavement temperature accurately, and the forecasting ability decreases with the extension of the forecast lead time, with the mean absolute error of 0.38-2.21℃, the root mean square error of 0.63-3.31℃, and the accuracy rate of 76.15%-99.48%. The model can accurately forecast the occurrence time and extreme values of extreme low temperature events on pavements, with the forecasting accuracy being about 90% for the 1 h forecast. For extreme high pavement temperature, the 1 h forecast of the model can also simulate the variation trend and the time for extreme high pavement temperature to occur. The model has a certain spatial transfer ability, and the average accuracy of the model forecast is more than 70% after the transfer, with an mean absolute deviation less than 3℃. Within a certain range, compared with the transfer distance, the forecasting ability of the model at its original station has a greater impact on the forecasting performance of the model after transfer.
Based on the 10 min observation data from the traffic meteorological stations along the Tianjin expressway during 2018-2023, a long short-term memory network model is used to forecast the pavement temperature in the next 3 h, and the forecasting effect and spatial transfer applicability of the model in the case of extreme pavement temperatures are evaluated and analyzed. The results show that the model forecasting effect is the best when the observation data of the time step 3 h are used as the input. The model can forecast the pavement temperature accurately, and the forecasting ability decreases with the extension of the forecast lead time, with the mean absolute error of 0.38-2.21℃, the root mean square error of 0.63-3.31℃, and the accuracy rate of 76.15%-99.48%. The model can accurately forecast the occurrence time and extreme values of extreme low temperature events on pavements, with the forecasting accuracy being about 90% for the 1 h forecast. For extreme high pavement temperature, the 1 h forecast of the model can also simulate the variation trend and the time for extreme high pavement temperature to occur. The model has a certain spatial transfer ability, and the average accuracy of the model forecast is more than 70% after the transfer, with an mean absolute deviation less than 3℃. Within a certain range, compared with the transfer distance, the forecasting ability of the model at its original station has a greater impact on the forecasting performance of the model after transfer.
2025, 51(12):1645-1655.
DOI: 10.7519/j.issn.1000-0526.2025.071101
Abstract:
This paper constructs a deep learning model based on the sequential fusion encoder (SFE) for forecasting winter precipitation phase. The model integrates the advantages of convolutional neural network (CNN), convolutional gated recurrent unit (ConvGRU), and Transformer. It can conduct automatic learning and extraction of complex features from meteorological data, handle non-linear relationships, and process large-scale datasets. We utilize hourly precipitation observation data from Wuhan Station in 2010-2024 and ERA5 reanalysis data, select 60-channel grid data (including temperature and geopotential height) from 9 atmospheric layers (1000-500 hPa) as predictors. To address sample imbalance, minute-level data augmentation is employed, involving resampling at intervals of 30 min for rain, 1 min for sleet, and 5 min for snow. Finally, a sample size of 19 932 is obtained. Test results show that this model performs excellently in forecasting solid precipitation (snow and sleet), with F1-scores of 0.92-0.93 in the training set and 0.67-0.68 in the validation set. However, its ability to identify rapid transitions between precipitation phases needs to be improved. Verified by two complex weather processes in February 2024, the model is found to be able to serve as a supplement to numerical prediction and provide an efficient solution for intelligent forecasting of winter precipitation phase, aiding in enhancing the forecasting capabilities of meteorological stations.
This paper constructs a deep learning model based on the sequential fusion encoder (SFE) for forecasting winter precipitation phase. The model integrates the advantages of convolutional neural network (CNN), convolutional gated recurrent unit (ConvGRU), and Transformer. It can conduct automatic learning and extraction of complex features from meteorological data, handle non-linear relationships, and process large-scale datasets. We utilize hourly precipitation observation data from Wuhan Station in 2010-2024 and ERA5 reanalysis data, select 60-channel grid data (including temperature and geopotential height) from 9 atmospheric layers (1000-500 hPa) as predictors. To address sample imbalance, minute-level data augmentation is employed, involving resampling at intervals of 30 min for rain, 1 min for sleet, and 5 min for snow. Finally, a sample size of 19 932 is obtained. Test results show that this model performs excellently in forecasting solid precipitation (snow and sleet), with F1-scores of 0.92-0.93 in the training set and 0.67-0.68 in the validation set. However, its ability to identify rapid transitions between precipitation phases needs to be improved. Verified by two complex weather processes in February 2024, the model is found to be able to serve as a supplement to numerical prediction and provide an efficient solution for intelligent forecasting of winter precipitation phase, aiding in enhancing the forecasting capabilities of meteorological stations.
2025, 51(12):1656-1668.
DOI: 10.7519/j.issn.1000-0526.2025.091901
Abstract:
Summer is a critical period for fishery production. Fishpond water has high temperature and low saturated dissolved oxygen, while aquatic organisms have vigorous metabolism and high oxygen consumption, so often leading to oxygen deficiency and economic losses. Dissolved oxygen monitoring equipment is limited by pond water quality, making it difficult to ensure monitoring accuracy. Research on meteorological prediction models for dissolved oxygen in summer aquaculture water can offer references for regulating dissolved oxygen, warning fishpond suffocation risks and integrating intelligent oxygenation systems, and can also offer solutions for improving fishpond water quality monitoring accuracy. Based on in-situ monitored dissolved oxygen and water temperature data in different layers of freshwater in fishponds and observed meteorological data from meteorological stations in Jianghan Plain, the spatio-temporal variation of layered dissolved oxygen in summer is analyzed. A real-time prediction model for dissolved oxygen based onmeteorological factors is established using the RF-SVR method and is compared with other models. The results show that the variation characteristics of dissolved oxygen in different layers are significant in sunny and hot weather in summer, that is, the dissolved oxygen starts to rise at 07:00-08:00 BT, decreases vertically with water depth, peaks at 17:00-20:00 BT, and then begins to decrease, with values in different layers getting close to each other gradually, and its lowest level appears at 03:00-08:00 BT. Under severe cooling and low-light conditions, pond dissolved oxygen rapidly drops to below 2 mg·L-1 and maintains a low-oxygen environment. Partial dependence analysis indicates that summer solar radiation exposure, sea-level pressure and maximum temperature are the main limiting factors for dissolved oxygen changes in aquaculture ponds and have a significant lag effect. The values of MAE, NSE, R2 and RMSE of meteorological prediction model for dissolved oxygen based on RF-SVR are 0.50 mg·L-1, 0.63, 0.77 and 0.66 mg·L-1 respectively, showing better simulation accuracy and smaller errors than those of other seven models and being more suitable for summer dissolved oxygen prediction.
Summer is a critical period for fishery production. Fishpond water has high temperature and low saturated dissolved oxygen, while aquatic organisms have vigorous metabolism and high oxygen consumption, so often leading to oxygen deficiency and economic losses. Dissolved oxygen monitoring equipment is limited by pond water quality, making it difficult to ensure monitoring accuracy. Research on meteorological prediction models for dissolved oxygen in summer aquaculture water can offer references for regulating dissolved oxygen, warning fishpond suffocation risks and integrating intelligent oxygenation systems, and can also offer solutions for improving fishpond water quality monitoring accuracy. Based on in-situ monitored dissolved oxygen and water temperature data in different layers of freshwater in fishponds and observed meteorological data from meteorological stations in Jianghan Plain, the spatio-temporal variation of layered dissolved oxygen in summer is analyzed. A real-time prediction model for dissolved oxygen based onmeteorological factors is established using the RF-SVR method and is compared with other models. The results show that the variation characteristics of dissolved oxygen in different layers are significant in sunny and hot weather in summer, that is, the dissolved oxygen starts to rise at 07:00-08:00 BT, decreases vertically with water depth, peaks at 17:00-20:00 BT, and then begins to decrease, with values in different layers getting close to each other gradually, and its lowest level appears at 03:00-08:00 BT. Under severe cooling and low-light conditions, pond dissolved oxygen rapidly drops to below 2 mg·L-1 and maintains a low-oxygen environment. Partial dependence analysis indicates that summer solar radiation exposure, sea-level pressure and maximum temperature are the main limiting factors for dissolved oxygen changes in aquaculture ponds and have a significant lag effect. The values of MAE, NSE, R2 and RMSE of meteorological prediction model for dissolved oxygen based on RF-SVR are 0.50 mg·L-1, 0.63, 0.77 and 0.66 mg·L-1 respectively, showing better simulation accuracy and smaller errors than those of other seven models and being more suitable for summer dissolved oxygen prediction.
2025, 51(12):1669-1682.
DOI: 10.7519/j.issn.1000-0526.2025.090101
Abstract:
Errors of typhoon operational position and intensity estimation, as well as errors of typhoon track and intensity forecast over the Northwest Pacific and the South China Sea in 2023 are evaluated. The results show that the mean error of typhoon position estimation by official typhoon forecasting agencies in 2023 was 17.1 km, and the mean error of typhoon intensity estimation was 2.8 m·s-1. Compared to the errors in 2022, the accuracy of typhoon position estimation got improved in 2023, whereas the accuracy of typhoon intensity estimation decreased. The mean errors of track forecasts with 24， 48 and 72 h lead time by subjective and objective forecast methods in 2023 were reduced than in 2022. In 2023, the mean errors of 24 h track forecast by the official typhoon forecasting agencies reached a historic low. Among objective forecasts, mean errors of track forecasts by ECMWF-IFS, NCEP-GFS, CMA-TRAMS were relatively small. Mean absolute errors of typhoon intensity forecasts by NCEP-GFS, CMA-TYM and ANNGA were generally small, and their intensity forecast skill scores were relatively high.
Errors of typhoon operational position and intensity estimation, as well as errors of typhoon track and intensity forecast over the Northwest Pacific and the South China Sea in 2023 are evaluated. The results show that the mean error of typhoon position estimation by official typhoon forecasting agencies in 2023 was 17.1 km, and the mean error of typhoon intensity estimation was 2.8 m·s-1. Compared to the errors in 2022, the accuracy of typhoon position estimation got improved in 2023, whereas the accuracy of typhoon intensity estimation decreased. The mean errors of track forecasts with 24， 48 and 72 h lead time by subjective and objective forecast methods in 2023 were reduced than in 2022. In 2023, the mean errors of 24 h track forecast by the official typhoon forecasting agencies reached a historic low. Among objective forecasts, mean errors of track forecasts by ECMWF-IFS, NCEP-GFS, CMA-TRAMS were relatively small. Mean absolute errors of typhoon intensity forecasts by NCEP-GFS, CMA-TYM and ANNGA were generally small, and their intensity forecast skill scores were relatively high.
2025, 51(12):1683-1692.
DOI: 10.7519/j.issn.10000526.2025.111701
Abstract:
The general atmospheric circulation in September 2025 was characterized by the following key features: the Northern Hemisphere polar vortex exhibited a singlecore structure, and biased toward the Eastern Hemisphere. The western Pacific subtropical high (WPSH) was stronger than usual, with its ridge extending farther westward and northward. Under its persistent control, prolonged high temperature conditions occurred in southern China. The warm and humid airflow on the edge of the WPSH converged with cold air moving southward, leading to significantly increased autumn rain in West China. Most of northern China was influenced by relatively flat westerly flow, with limited incursions of cold air. The temperature in North China and Northeast China was relatively high and the precipitation was relatively low. A total of six tropical cyclones or typhoons formed in the month, three of which made landfall in China, and the both figures exceeded the climatological normal by 1 and 1.3, respectively. On 18 September, tyhpoons Mitag, Ragasa and Neoguri successively formed and coexisted. Mitag， Ragasa and Bualoi tracked northwestward, affecting South China, etc. Super Typhoon Ragasa made landfall three times along the southern coast, accompanied by strong winds and prolonged influence. Persistent moisture transport by the WPSH, combined with the effects of tropical cyclones, resulted in significant precipitation across South China. The monthly mean temperature across China was 18.1℃, 1.2℃ above normal, ranking as the fourth highest since 1961. The national average monthly precipitation reached 87.6 mm, 34% above the normal, making it the third in September since 1961. In addition, eight major heavy precipitation processes and four severe convective weather processes were recorded during this month.
The general atmospheric circulation in September 2025 was characterized by the following key features: the Northern Hemisphere polar vortex exhibited a singlecore structure, and biased toward the Eastern Hemisphere. The western Pacific subtropical high (WPSH) was stronger than usual, with its ridge extending farther westward and northward. Under its persistent control, prolonged high temperature conditions occurred in southern China. The warm and humid airflow on the edge of the WPSH converged with cold air moving southward, leading to significantly increased autumn rain in West China. Most of northern China was influenced by relatively flat westerly flow, with limited incursions of cold air. The temperature in North China and Northeast China was relatively high and the precipitation was relatively low. A total of six tropical cyclones or typhoons formed in the month, three of which made landfall in China, and the both figures exceeded the climatological normal by 1 and 1.3, respectively. On 18 September, tyhpoons Mitag, Ragasa and Neoguri successively formed and coexisted. Mitag， Ragasa and Bualoi tracked northwestward, affecting South China, etc. Super Typhoon Ragasa made landfall three times along the southern coast, accompanied by strong winds and prolonged influence. Persistent moisture transport by the WPSH, combined with the effects of tropical cyclones, resulted in significant precipitation across South China. The monthly mean temperature across China was 18.1℃, 1.2℃ above normal, ranking as the fourth highest since 1961. The national average monthly precipitation reached 87.6 mm, 34% above the normal, making it the third in September since 1961. In addition, eight major heavy precipitation processes and four severe convective weather processes were recorded during this month.
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ISSN:1000-0526 CN:11-2282/P