ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 51,Issue 8,2025 Table of Contents
2025, 51(8):901-913.
DOI: 10.7519/j.issn.1000-0526.2025.061901
Abstract:
This article aims to explore the potential application and challenges of large language model (LLM) in weather forecasting. Through analyzing LLM application in meteorology and related industries, including knowledge retrieval, foundation models, diagnostic analysis, tool calling and text generation, the article demonstrates that LLM has tremendous potentials in improving weather forecast accuracy and business intelligence. LLM serve as powerful assistive tools for forecasters by efficiently processing massive meteorological knowledge, integrating cross-domain multi-source information, and generating customized forecast products. The effectiveness of LLM in weather forecasting can be further enhanced by building high-quality meteorological corpora, optimizing benchmark testing frameworks, and incorporating external tools. While LLM brings new technological opportunities to the meteorological field, their widespread application still requires continuous exploration and improvement in corpus quality, model optimization, and human-machine collaboration. Moreover, LLM still has limitations in understanding the spatio-temporal dynamics of atmospheric motions and issues with bias and hallucination, which need to be addressed through data cleaning, debiasing, fine-tuning, and retrieval-augmented generation techniques.
This article aims to explore the potential application and challenges of large language model (LLM) in weather forecasting. Through analyzing LLM application in meteorology and related industries, including knowledge retrieval, foundation models, diagnostic analysis, tool calling and text generation, the article demonstrates that LLM has tremendous potentials in improving weather forecast accuracy and business intelligence. LLM serve as powerful assistive tools for forecasters by efficiently processing massive meteorological knowledge, integrating cross-domain multi-source information, and generating customized forecast products. The effectiveness of LLM in weather forecasting can be further enhanced by building high-quality meteorological corpora, optimizing benchmark testing frameworks, and incorporating external tools. While LLM brings new technological opportunities to the meteorological field, their widespread application still requires continuous exploration and improvement in corpus quality, model optimization, and human-machine collaboration. Moreover, LLM still has limitations in understanding the spatio-temporal dynamics of atmospheric motions and issues with bias and hallucination, which need to be addressed through data cleaning, debiasing, fine-tuning, and retrieval-augmented generation techniques.
2025, 51(8):914-927.
DOI: 10.7519/j.issn.1000-0526.2025.052301
Abstract:
Heavy precipitation is one of the most widespread hazardous weather affecting the socioeconomic stability and people’s livelihoods in China. Accurately forecasting such events poses significant scientific challenges. The multi-scale nonlinear characteristics of moist physical processes make numerical weather prediction and objective corrections for precipitation become considerably more difficult than for other meteorological variables like wind and temperature. Utilizing station observations and numerical model forecasts, this paper explores the application effect of machine learning algorithm (LightGBM) in correcting 3 h accumulated precipitation forecasts for the Beijing-Tianjin-Hebei Region through strategies such as constructing and sampling precipitation datasets, inputting relevant physical features, and training on residuals. The results demonstrate that, to address the long-tailed distribution challenge of precipitation samples, when constructing the dataset it is crucial to comprehensively consider maintaining the true distribution of precipitation while moderately increasing the proportion of samples with stronger precipitation intensities. This is a key step in enhancing the correction effectiveness of heavy precipitation. Statistical tests on the independent test set show that the LightGBM correction scheme achieves significant improvements in skill scores for precipitation ranging from 0.1 mm to 20 mm compared to the raw model forecasts, and the increase rate ascends upward as the threshold rises. Statistical tests and individual case evaluations of precipitation by type show that the LightGBM correction scheme presents comprehensive adjustments in rainfall intensity and fall area in different types of precipitation forecasts. Among them, the number of forced precipitation samples by severe weather systems is relatively superior, and the correction effects on each precipitation threshold are more remarkable. Additionally, the evaluation of classified heavy precipitation indicates that it is more challenging to achieve improvements in the correction of convective heavy precipitation forced by weak weather systems, in particular in the cases with precipitation ≥15 mm. The adjustment of the fall area of precipitation is more challenging. This suggests that the unbalanced distribution of precipitation samples remains a challenge for machine learning correction. However, machine learning has shown particular promise for correcting larger magnitudes of heavy precipitation events with lower forecast accuracy from the model, that is, the lower the model’s forecast ability, the greater the room and extent for the correction improvement of machine learning. The analysis of feature importance shows that the input of physical features such as dynamics, thermodynamics and water vapor, which are closely related to precipitation, has a positive contribution to enhancing the correction score of LightGBM.
Heavy precipitation is one of the most widespread hazardous weather affecting the socioeconomic stability and people’s livelihoods in China. Accurately forecasting such events poses significant scientific challenges. The multi-scale nonlinear characteristics of moist physical processes make numerical weather prediction and objective corrections for precipitation become considerably more difficult than for other meteorological variables like wind and temperature. Utilizing station observations and numerical model forecasts, this paper explores the application effect of machine learning algorithm (LightGBM) in correcting 3 h accumulated precipitation forecasts for the Beijing-Tianjin-Hebei Region through strategies such as constructing and sampling precipitation datasets, inputting relevant physical features, and training on residuals. The results demonstrate that, to address the long-tailed distribution challenge of precipitation samples, when constructing the dataset it is crucial to comprehensively consider maintaining the true distribution of precipitation while moderately increasing the proportion of samples with stronger precipitation intensities. This is a key step in enhancing the correction effectiveness of heavy precipitation. Statistical tests on the independent test set show that the LightGBM correction scheme achieves significant improvements in skill scores for precipitation ranging from 0.1 mm to 20 mm compared to the raw model forecasts, and the increase rate ascends upward as the threshold rises. Statistical tests and individual case evaluations of precipitation by type show that the LightGBM correction scheme presents comprehensive adjustments in rainfall intensity and fall area in different types of precipitation forecasts. Among them, the number of forced precipitation samples by severe weather systems is relatively superior, and the correction effects on each precipitation threshold are more remarkable. Additionally, the evaluation of classified heavy precipitation indicates that it is more challenging to achieve improvements in the correction of convective heavy precipitation forced by weak weather systems, in particular in the cases with precipitation ≥15 mm. The adjustment of the fall area of precipitation is more challenging. This suggests that the unbalanced distribution of precipitation samples remains a challenge for machine learning correction. However, machine learning has shown particular promise for correcting larger magnitudes of heavy precipitation events with lower forecast accuracy from the model, that is, the lower the model’s forecast ability, the greater the room and extent for the correction improvement of machine learning. The analysis of feature importance shows that the input of physical features such as dynamics, thermodynamics and water vapor, which are closely related to precipitation, has a positive contribution to enhancing the correction score of LightGBM.
2025, 51(8):928-940.
DOI: 10.7519/j.issn.1000-0526.2025.011201
Abstract:
The refinement level of wind forecast by numerical prediction models cannot meet the needs of inland waterway transportation, and its adaptability to different regions varies. This article takes most of western hilly area and central plain area in Hubei Province, including parts of the Yangtze River waterway, as the research areas. Referring to the 10 m wind real-time product of ART_1 km, this article analyzes the adaptability of 10 m wind forecast of the European Centre for Medium-Range Weather Forecasts high-resolution atmospheric model deterministic forecasting product (EC-HRES) and the China Meteorological Administration mesoscale model forecasting product (CMA-MESO) in the research areas. A U-Net++ deep convolutional network model is constructed to achieve downscaling correction of wind speed forecast. The correction model improves the sampling module and adds waterway item and terrain item into the loss function, enhancing expressive ability and robustness of the model and improving the correction effect on the waterway. The verification result shows that this method can effectively reduce the prediction error of wind speed made by numerical prediction models in the waterway areas.
The refinement level of wind forecast by numerical prediction models cannot meet the needs of inland waterway transportation, and its adaptability to different regions varies. This article takes most of western hilly area and central plain area in Hubei Province, including parts of the Yangtze River waterway, as the research areas. Referring to the 10 m wind real-time product of ART_1 km, this article analyzes the adaptability of 10 m wind forecast of the European Centre for Medium-Range Weather Forecasts high-resolution atmospheric model deterministic forecasting product (EC-HRES) and the China Meteorological Administration mesoscale model forecasting product (CMA-MESO) in the research areas. A U-Net++ deep convolutional network model is constructed to achieve downscaling correction of wind speed forecast. The correction model improves the sampling module and adds waterway item and terrain item into the loss function, enhancing expressive ability and robustness of the model and improving the correction effect on the waterway. The verification result shows that this method can effectively reduce the prediction error of wind speed made by numerical prediction models in the waterway areas.
2025, 51(8):941-953.
DOI: 10.7519/j.issn.1000-0526.2025.010902
Abstract:
Based on the total irradiance data from 23 radiation observation stations in Henan Province in 2022 and CMA-WSP2.0 model products, in this paper, characteristic variables are selected by LASSO regression, training data sets and test data sets are established, and models are trained by machine learning methods (random forest, XGBoost and LightGBM) with the training data sets. Besides, the total irradiance forecasts by CMA-WSP2.0 model in Henan Province are corrected, and the corrected results are tested by site and region, by season and by total irradiance classification. The results are as follows. The three machine learning methods (random forest, XGBoost and LightGBM) have good correction effects. Compared with the CMA-WSP2.0 prediction results, the mean absolute error (MAE) and root mean square error (RMSE) are significantly reduced, and the 24 h accuracy rate and 24 h qualification rate are significantly improved. Among them, LightGBM has the best correction effect. The MAE decreases by 18.32-32.91 W·m-2, the reduction proportion of MAE decreases by 38%-56%, and the reduction proportion of RMSE decreases by 36%-52%. Moreover, the 24 h average accuracy and 24 h average qualification rate are raised by 7.3% and 5.7%, respectively. The results of regional statistics are consistent with those of the stations. For the five regions of Henan Province, the correction effect for western Henan is the best. The corrected deviation range of the three machine machine learning methods is narrower than that of the CMA-WSP2.0 test set, and the probability of the deviation distribution near zero is greater. Among the seasonal test results, the three machine learning methods have more significant correction effect for the winter prediction. For different total irradiance levels, the three machine learning methods can effectively improve the CMA-WSP2.0 prediction, but the correction effect tends to gradually weaken with the increase of total irradiance levels. These findings could provide a useful reference for improving the ability of total irradiance forecast in Henan Province.
Based on the total irradiance data from 23 radiation observation stations in Henan Province in 2022 and CMA-WSP2.0 model products, in this paper, characteristic variables are selected by LASSO regression, training data sets and test data sets are established, and models are trained by machine learning methods (random forest, XGBoost and LightGBM) with the training data sets. Besides, the total irradiance forecasts by CMA-WSP2.0 model in Henan Province are corrected, and the corrected results are tested by site and region, by season and by total irradiance classification. The results are as follows. The three machine learning methods (random forest, XGBoost and LightGBM) have good correction effects. Compared with the CMA-WSP2.0 prediction results, the mean absolute error (MAE) and root mean square error (RMSE) are significantly reduced, and the 24 h accuracy rate and 24 h qualification rate are significantly improved. Among them, LightGBM has the best correction effect. The MAE decreases by 18.32-32.91 W·m-2, the reduction proportion of MAE decreases by 38%-56%, and the reduction proportion of RMSE decreases by 36%-52%. Moreover, the 24 h average accuracy and 24 h average qualification rate are raised by 7.3% and 5.7%, respectively. The results of regional statistics are consistent with those of the stations. For the five regions of Henan Province, the correction effect for western Henan is the best. The corrected deviation range of the three machine machine learning methods is narrower than that of the CMA-WSP2.0 test set, and the probability of the deviation distribution near zero is greater. Among the seasonal test results, the three machine learning methods have more significant correction effect for the winter prediction. For different total irradiance levels, the three machine learning methods can effectively improve the CMA-WSP2.0 prediction, but the correction effect tends to gradually weaken with the increase of total irradiance levels. These findings could provide a useful reference for improving the ability of total irradiance forecast in Henan Province.
2025, 51(8):954-963.
DOI: 10.7519/j.issn.1000-0526.2025.041602
Abstract:
Based on the data from automatic weather stations and buoy observation stations, and ERA5 reanalysis data from 2015 to 2023, this article analyzes the characteristics of sea fog at Yangshan Port. Classification decision tree models are trained and constructed based on a comprehensive case database of sea fog events. Their forcast results are compared with those of the ECMWF model. The results indicate that the year 2016 has the highest number of foggy days, with spring and early summer being the peak season, followed by winter. During dense fog events at Yangshan Station, the dominant wind directions change from northeast to north and southeast. Southeast winds prevail during non-precipitation periods, while north winds dominate during precipitation. Monthly wind patterns alter from predominantly northerly in winter to northeasterly and southeasterly in spring. Wind direction and speed varied at different stages of sea fog. In the developing stage of sea fog, southeast winds are dominant; during the mature stage, northeast winds prevail; and during the dissipating stage, north winds dominat with high speeds. Fog events accompanied by precipitation are more frequent and long time lasting. The classification decision tree models have identified the temperature-dewpoint spread as a key factor in the formation of various sea fog types. Decision tree models demonstrate a lower miss rate and higher prediction performance than the ECMWF model, particularly in forecasting the formation and duration of advection fog. This can provide valuable insights for forecasting frontal and radiation fog events.
Based on the data from automatic weather stations and buoy observation stations, and ERA5 reanalysis data from 2015 to 2023, this article analyzes the characteristics of sea fog at Yangshan Port. Classification decision tree models are trained and constructed based on a comprehensive case database of sea fog events. Their forcast results are compared with those of the ECMWF model. The results indicate that the year 2016 has the highest number of foggy days, with spring and early summer being the peak season, followed by winter. During dense fog events at Yangshan Station, the dominant wind directions change from northeast to north and southeast. Southeast winds prevail during non-precipitation periods, while north winds dominate during precipitation. Monthly wind patterns alter from predominantly northerly in winter to northeasterly and southeasterly in spring. Wind direction and speed varied at different stages of sea fog. In the developing stage of sea fog, southeast winds are dominant; during the mature stage, northeast winds prevail; and during the dissipating stage, north winds dominat with high speeds. Fog events accompanied by precipitation are more frequent and long time lasting. The classification decision tree models have identified the temperature-dewpoint spread as a key factor in the formation of various sea fog types. Decision tree models demonstrate a lower miss rate and higher prediction performance than the ECMWF model, particularly in forecasting the formation and duration of advection fog. This can provide valuable insights for forecasting frontal and radiation fog events.
2025, 51(8):964-977.
DOI: 10.7519/j.issn.1000-0526.2025.041302
Abstract:
To evaluate the accuracy and operational applicability of FY-4A/4B GIIRS-retrieved temperature and humidity profiles in Guizhou, the FY-4A temperature profile and FY-4B temperature and humidity profiles are verified against sounding observations at Guiyang and Weining stations and ERA5 reanalysis data. The results indicate that, in practical operations, it is considered that only the unusable data can be eliminated, while the data with the best and better quality can be retained so as to maximize the integrity of the temperature and humidity profiles. Clouds significantly degrade GIIRS retrieval performance. Under cloudy conditions, the root mean square error of FY-4A temperature increases by 1.19℃ (clear sky) and 0.96℃ (cloud edge), FY-4B temperature increases by 1.52℃ （clear sky） and 1.21℃ (cloud edge), and specific humidity increases by 1.28 g·kg-1 (clear sky) and 0.95 g·kg-1 (cloud edge), respectively. Moreover, cloud cover also amplifies vertical data dispersion. Seasonal comparisons between sounding and satellite profiles demonstrate that FY-4A/4B captures terrain-induced differences in atmospheric stratification between Guiyang and Weining. For three regional hail events in 2023, GIIRS products agree well with sounding observations. The high-resolution profiles reveal pre-hail instability, which is “upper dry and lower wet”, offering valuable forecast indicators. However, the near-surface layer retrieval errors lead to the underestimation of convective available potential energy (CAPE) and distort low-level sounding structures. After the surface 2 m temperature and dew point temperature corrections, a large CAPE and a sounding structure conducive to the occurrence of thunderstorms and strong winds are displayed. This is a good support for the short-time forecasting and nowcasting of severe convections.
To evaluate the accuracy and operational applicability of FY-4A/4B GIIRS-retrieved temperature and humidity profiles in Guizhou, the FY-4A temperature profile and FY-4B temperature and humidity profiles are verified against sounding observations at Guiyang and Weining stations and ERA5 reanalysis data. The results indicate that, in practical operations, it is considered that only the unusable data can be eliminated, while the data with the best and better quality can be retained so as to maximize the integrity of the temperature and humidity profiles. Clouds significantly degrade GIIRS retrieval performance. Under cloudy conditions, the root mean square error of FY-4A temperature increases by 1.19℃ (clear sky) and 0.96℃ (cloud edge), FY-4B temperature increases by 1.52℃ （clear sky） and 1.21℃ (cloud edge), and specific humidity increases by 1.28 g·kg-1 (clear sky) and 0.95 g·kg-1 (cloud edge), respectively. Moreover, cloud cover also amplifies vertical data dispersion. Seasonal comparisons between sounding and satellite profiles demonstrate that FY-4A/4B captures terrain-induced differences in atmospheric stratification between Guiyang and Weining. For three regional hail events in 2023, GIIRS products agree well with sounding observations. The high-resolution profiles reveal pre-hail instability, which is “upper dry and lower wet”, offering valuable forecast indicators. However, the near-surface layer retrieval errors lead to the underestimation of convective available potential energy (CAPE) and distort low-level sounding structures. After the surface 2 m temperature and dew point temperature corrections, a large CAPE and a sounding structure conducive to the occurrence of thunderstorms and strong winds are displayed. This is a good support for the short-time forecasting and nowcasting of severe convections.
2025, 51(8):978-992.
DOI: 10.7519/j.issn.1000-0526.2025.031902
Abstract:
Based on C-band weather radar products and multi-source observations, the persistent severe storm weather process and two major rainstorm cells in southwest Yunnan during 13-15 March 2023 are analyzed. The results indicate that this severe storm weather process occurred in the circulation background of the surface cold front’s retreating eastward, the establishment and intensification of the southwesterly jet in the low (upper) air, and the persistent intrusion of the mid-level northwesterly flow, and the storm mainly developed and intensified near the crossing area of the mid- and high-altitude jets. The continuous and stable transport of the low-level warm advection and the mid-level cold advection in southwest Yunnan intensified the unstable stratification of the ambient atmosphere. The convective available potential energy (CAPE) was 826.6-1481.6 J·kg-1, the vertical wind shears from 0 to 3 km, and 0 to 6 km were 14.4-19.9 m·s-1 and 27.6-34.5 m·s-1, respectively. The high unstable stratification and the strong vertical wind shear provided good ambient condition for the formation, development and maintenance of the catastrophic storms. The daytime storm was triggered by the coupling of southerly wind uplift forced by warm advection with weak surface convergence lines. The significant thermal and moisture difference on either side of the Wuliang Mountains enhanced the storm’s development. Comparatively, the nighttime storm initially formed near the mid-to-low-level baroclinic frontogenesis zone, triggered by upslope lifting during its moving eastward and intensified under the influence of the low-level southwesterly jet. Under the influence of diurnal difference and diverse topographic forcing, the radar echo characteristics of the storm cells exhibited distinct features. Storm cell No.1 displayed radar echo morphologies such as an inflow notch, a bounded weak echo region (BWER), and a “V” notch, with radial velocity indicating a mesocyclone structure. During the hailfall period, the average composite reflectivity was 60.5 dBz, the average vertically integrated liquid (VIL) was 36.1 kg·m-2, and the average VIL density (VILD) was 4.0 g·m-3. In contrast, storm cell No.2 had a prominent rear-inflow jet and a forward-flank inflow notch, with more pronounced topographic responses in its echo. After crossing the Lancang River, the strong echo area, VIL, and VILD increased abruptly, with VILD rising from 1.7 g·m-3 to 4.5 g·m-3. The life cycles and surface severe weather manifestations of the storm cells differed significantly. Storm cell No.1 had a lifespan of 6 h, accompanied by continuous hailfall during its influence period, with thunderstorm winds observed before and after its passage. The precipitation phase transitioned to a mix of hail and short-term heavy rainfall exceeding 20 mm·h-1 in the later stage. Storm cell No.2 had a lifespan of 3 h, with hailfall occurring only in the later stage of its development. The other types of convective weather were less intense.
Based on C-band weather radar products and multi-source observations, the persistent severe storm weather process and two major rainstorm cells in southwest Yunnan during 13-15 March 2023 are analyzed. The results indicate that this severe storm weather process occurred in the circulation background of the surface cold front’s retreating eastward, the establishment and intensification of the southwesterly jet in the low (upper) air, and the persistent intrusion of the mid-level northwesterly flow, and the storm mainly developed and intensified near the crossing area of the mid- and high-altitude jets. The continuous and stable transport of the low-level warm advection and the mid-level cold advection in southwest Yunnan intensified the unstable stratification of the ambient atmosphere. The convective available potential energy (CAPE) was 826.6-1481.6 J·kg-1, the vertical wind shears from 0 to 3 km, and 0 to 6 km were 14.4-19.9 m·s-1 and 27.6-34.5 m·s-1, respectively. The high unstable stratification and the strong vertical wind shear provided good ambient condition for the formation, development and maintenance of the catastrophic storms. The daytime storm was triggered by the coupling of southerly wind uplift forced by warm advection with weak surface convergence lines. The significant thermal and moisture difference on either side of the Wuliang Mountains enhanced the storm’s development. Comparatively, the nighttime storm initially formed near the mid-to-low-level baroclinic frontogenesis zone, triggered by upslope lifting during its moving eastward and intensified under the influence of the low-level southwesterly jet. Under the influence of diurnal difference and diverse topographic forcing, the radar echo characteristics of the storm cells exhibited distinct features. Storm cell No.1 displayed radar echo morphologies such as an inflow notch, a bounded weak echo region (BWER), and a “V” notch, with radial velocity indicating a mesocyclone structure. During the hailfall period, the average composite reflectivity was 60.5 dBz, the average vertically integrated liquid (VIL) was 36.1 kg·m-2, and the average VIL density (VILD) was 4.0 g·m-3. In contrast, storm cell No.2 had a prominent rear-inflow jet and a forward-flank inflow notch, with more pronounced topographic responses in its echo. After crossing the Lancang River, the strong echo area, VIL, and VILD increased abruptly, with VILD rising from 1.7 g·m-3 to 4.5 g·m-3. The life cycles and surface severe weather manifestations of the storm cells differed significantly. Storm cell No.1 had a lifespan of 6 h, accompanied by continuous hailfall during its influence period, with thunderstorm winds observed before and after its passage. The precipitation phase transitioned to a mix of hail and short-term heavy rainfall exceeding 20 mm·h-1 in the later stage. Storm cell No.2 had a lifespan of 3 h, with hailfall occurring only in the later stage of its development. The other types of convective weather were less intense.
2025, 51(8):993-1005.
DOI: 10.7519/j.issn.1000-0526.2025.010901
Abstract:
Based on C-band weather radar products and multi-source observations, the persistent severe storm weather process and two major rainstorm cells in southwest Yunnan during 13-15 March 2023 are analyzed. The results indicate that this severe storm weather process occurred in the circulation background of the surface cold front’s retreating eastward, the establishment and intensification of the southwesterly jet in the low (upper) air, and the persistent intrusion of the mid-level northwesterly flow, and the storm mainly developed and intensified near the crossing area of the mid- and high-altitude jets. The continuous and stable transport of the low-level warm advection and the mid-level cold advection in southwest Yunnan intensified the unstable stratification of the ambient atmosphere. The convective available potential energy (CAPE) was 826.6-1481.6 J·kg-1, the vertical wind shears from 0 to 3 km, and 0 to 6 km were 14.4-19.9 m·s-1 and 27.6-34.5 m·s-1, respectively. The high unstable stratification and the strong vertical wind shear provided good ambient condition for the formation, development and maintenance of the catastrophic storms. The daytime storm was triggered by the coupling of southerly wind uplift forced by warm advection with weak surface convergence lines. The significant thermal and moisture difference on either side of the Wuliang Mountains enhanced the storm’s development. Comparatively, the nighttime storm initially formed near the mid-to-low-level baroclinic frontogenesis zone, triggered by upslope lifting during its moving eastward and intensified under the influence of the low-level southwesterly jet. Under the influence of diurnal difference and diverse topographic forcing, the radar echo characteristics of the storm cells exhibited distinct features. Storm cell No.1 displayed radar echo morphologies such as an inflow notch, a bounded weak echo region (BWER), and a “V” notch, with radial velocity indicating a mesocyclone structure. During the hailfall period, the average composite reflectivity was 60.5 dBz, the average vertically integrated liquid (VIL) was 36.1 kg·m-2, and the average VIL density (VILD) was 4.0 g·m-3. In contrast, storm cell No.2 had a prominent rear-inflow jet and a forward-flank inflow notch, with more pronounced topographic responses in its echo. After crossing the Lancang River, the strong echo area, VIL, and VILD increased abruptly, with VILD rising from 1.7 g·m-3 to 4.5 g·m-3. The life cycles and surface severe weather manifestations of the storm cells differed significantly. Storm cell No.1 had a lifespan of 6 h, accompanied by continuous hailfall during its influence period, with thunderstorm winds observed before and after its passage. The precipitation phase transitioned to a mix of hail and short-term heavy rainfall exceeding 20 mm·h-1 in the later stage. Storm cell No.2 had a lifespan of 3 h, with hailfall occurring only in the later stage of its development. The other types of convective weather were less intense.
Based on C-band weather radar products and multi-source observations, the persistent severe storm weather process and two major rainstorm cells in southwest Yunnan during 13-15 March 2023 are analyzed. The results indicate that this severe storm weather process occurred in the circulation background of the surface cold front’s retreating eastward, the establishment and intensification of the southwesterly jet in the low (upper) air, and the persistent intrusion of the mid-level northwesterly flow, and the storm mainly developed and intensified near the crossing area of the mid- and high-altitude jets. The continuous and stable transport of the low-level warm advection and the mid-level cold advection in southwest Yunnan intensified the unstable stratification of the ambient atmosphere. The convective available potential energy (CAPE) was 826.6-1481.6 J·kg-1, the vertical wind shears from 0 to 3 km, and 0 to 6 km were 14.4-19.9 m·s-1 and 27.6-34.5 m·s-1, respectively. The high unstable stratification and the strong vertical wind shear provided good ambient condition for the formation, development and maintenance of the catastrophic storms. The daytime storm was triggered by the coupling of southerly wind uplift forced by warm advection with weak surface convergence lines. The significant thermal and moisture difference on either side of the Wuliang Mountains enhanced the storm’s development. Comparatively, the nighttime storm initially formed near the mid-to-low-level baroclinic frontogenesis zone, triggered by upslope lifting during its moving eastward and intensified under the influence of the low-level southwesterly jet. Under the influence of diurnal difference and diverse topographic forcing, the radar echo characteristics of the storm cells exhibited distinct features. Storm cell No.1 displayed radar echo morphologies such as an inflow notch, a bounded weak echo region (BWER), and a “V” notch, with radial velocity indicating a mesocyclone structure. During the hailfall period, the average composite reflectivity was 60.5 dBz, the average vertically integrated liquid (VIL) was 36.1 kg·m-2, and the average VIL density (VILD) was 4.0 g·m-3. In contrast, storm cell No.2 had a prominent rear-inflow jet and a forward-flank inflow notch, with more pronounced topographic responses in its echo. After crossing the Lancang River, the strong echo area, VIL, and VILD increased abruptly, with VILD rising from 1.7 g·m-3 to 4.5 g·m-3. The life cycles and surface severe weather manifestations of the storm cells differed significantly. Storm cell No.1 had a lifespan of 6 h, accompanied by continuous hailfall during its influence period, with thunderstorm winds observed before and after its passage. The precipitation phase transitioned to a mix of hail and short-term heavy rainfall exceeding 20 mm·h-1 in the later stage. Storm cell No.2 had a lifespan of 3 h, with hailfall occurring only in the later stage of its development. The other types of convective weather were less intense.
2025, 51(8):1006-1017.
DOI: 10.7519/j.issn.1000-0526.2025.021801
Abstract:
Based on the hourly precipitation data from 122 national meteorological stations in Shandong Province from 1966 to 2023, the frequency variations of heavy precipitation are analyzed. Different marginal distribution functions are used to fit the duration and amount of precipitation, and the change patterns of the return periods of heavy precipitation with different durations are investigated based on Copula function. The results are as follows. There exists a significant dependence relation between the duration and amount of heavy precipitation, which can be fitted well using generalized extreme values and logarithmic normal distribution functions. The Gumbel Copula and Clayton Copula functions are suitable for portraying the dependence structure of the binary variables of the short-time heavy precipitation in Shandong. However, the Clayton Copula function is more appropriate when the precipitation lasts for more than 8 h. The return period estimated by daily precipitation may seriously underestimate the hazard of short-time heavy precipitation. For a short-time heavy precipitation event under the same hazard-bearing condition, the shorter the duration, the longer the joint return period. The high-value areas of joint return period estimated by the Copula function gradually narrow down from the east and the south of Shandong to the east of Shandong, along with the increase of precipitation duration. Especially, the hazard of heavy precipitation that comes along once every 60 years is higher in the east and the south of Shandong. This method can more scientifically describe the disaster risks of heavy precipitation in different scenarios, especially in the short-time heavy precipitation scenario, so it can provide a scientific reference for disaster prevention and mitigation planning as well as disaster risk managing in Shandong.
Based on the hourly precipitation data from 122 national meteorological stations in Shandong Province from 1966 to 2023, the frequency variations of heavy precipitation are analyzed. Different marginal distribution functions are used to fit the duration and amount of precipitation, and the change patterns of the return periods of heavy precipitation with different durations are investigated based on Copula function. The results are as follows. There exists a significant dependence relation between the duration and amount of heavy precipitation, which can be fitted well using generalized extreme values and logarithmic normal distribution functions. The Gumbel Copula and Clayton Copula functions are suitable for portraying the dependence structure of the binary variables of the short-time heavy precipitation in Shandong. However, the Clayton Copula function is more appropriate when the precipitation lasts for more than 8 h. The return period estimated by daily precipitation may seriously underestimate the hazard of short-time heavy precipitation. For a short-time heavy precipitation event under the same hazard-bearing condition, the shorter the duration, the longer the joint return period. The high-value areas of joint return period estimated by the Copula function gradually narrow down from the east and the south of Shandong to the east of Shandong, along with the increase of precipitation duration. Especially, the hazard of heavy precipitation that comes along once every 60 years is higher in the east and the south of Shandong. This method can more scientifically describe the disaster risks of heavy precipitation in different scenarios, especially in the short-time heavy precipitation scenario, so it can provide a scientific reference for disaster prevention and mitigation planning as well as disaster risk managing in Shandong.
2025, 51(8):1018-1028.
DOI: 10.7519/j.issn.1000-0526.2025.072801
Abstract:
The main characteristics of the general circulation in May 2025 are that the polar vortex in the Northern Hemisphere was partially monopolar with stronger intensity than usual. The circulation transformed from a threewave pattern in winter into a fourwave pattern in summer. The western Pacific subtropical high was stronger, located more westerly than in normal years, while the south branch trough was weaker than usual. The South China Sea summer monsoon erupted in the 6th pentad (29 May) of May, 10 days later than in normal years. The monthly average temperature across China was 17.3℃, 0.8℃ higher than normal, so it was recorded as the third highest for the same historical period since 1961. The monthly average precipitation was 77.6 mm, 10% more than normal. During this month, six torrential rain and severe convection processes occurred in China, with precipitation at many stations breaking their historical extremes, causing floods and secondary geological disasters in Guizhou, Guangxi, Guangdong, Hunan, Jiangxi provinces and other regions. On 4 May, thunderstorm gale at speed of 44.7 m·s-1 was monitored in Qianxi City, Guizhou Province. On 8 May, EF1 tornadoes occurred in Qidong County and Liling City, Hunan Province. In additionThe main characteristics of the general circulation in May 2025 are that the polar vortex in the Northern Hemisphere was partially mono-polar with stronger intensity than usual. The circulation transformed from a three-wave pattern in winter into a four-wave pattern in summer. The western Pacific subtropical high was stronger, located more westerly than in normal years, while the south branch trough was weaker than usual. The South China Sea summer monsoon erupted in the 6th pentad (29 May) of May, 10 days later than in normal years. The monthly average temperature across China was 17.3℃, 0.8℃ higher than normal, so it was recorded as the third highest for the same historical period since 1961. The monthly average precipitation was 77.6 mm, 10% more than normal. During this month, six torrential rain and severe convection processes occurred in China, with precipitation at many stations breaking their historical extremes, causing floods and secondary geological disasters in Guizhou, Guangxi, Guangdong, Hunan, Jiangxi provinces and other regions. On 4 May, thunderstorm gale at speed of 44.7 m·s-1 was monitored in Qianxi City, Guizhou Province. On 8 May, EF1 tornadoes occurred in Qidong County and Liling City, Hunan Province. In addition, there were five sand-dust events affecting northern China during this month, which are noticeably more than the average for the same period from 2000 to 2024. , there were five sanddust events affecting northern China during this month, which are noticeably more than the average for the same period from 2000 to 2024.
The main characteristics of the general circulation in May 2025 are that the polar vortex in the Northern Hemisphere was partially monopolar with stronger intensity than usual. The circulation transformed from a threewave pattern in winter into a fourwave pattern in summer. The western Pacific subtropical high was stronger, located more westerly than in normal years, while the south branch trough was weaker than usual. The South China Sea summer monsoon erupted in the 6th pentad (29 May) of May, 10 days later than in normal years. The monthly average temperature across China was 17.3℃, 0.8℃ higher than normal, so it was recorded as the third highest for the same historical period since 1961. The monthly average precipitation was 77.6 mm, 10% more than normal. During this month, six torrential rain and severe convection processes occurred in China, with precipitation at many stations breaking their historical extremes, causing floods and secondary geological disasters in Guizhou, Guangxi, Guangdong, Hunan, Jiangxi provinces and other regions. On 4 May, thunderstorm gale at speed of 44.7 m·s-1 was monitored in Qianxi City, Guizhou Province. On 8 May, EF1 tornadoes occurred in Qidong County and Liling City, Hunan Province. In additionThe main characteristics of the general circulation in May 2025 are that the polar vortex in the Northern Hemisphere was partially mono-polar with stronger intensity than usual. The circulation transformed from a three-wave pattern in winter into a four-wave pattern in summer. The western Pacific subtropical high was stronger, located more westerly than in normal years, while the south branch trough was weaker than usual. The South China Sea summer monsoon erupted in the 6th pentad (29 May) of May, 10 days later than in normal years. The monthly average temperature across China was 17.3℃, 0.8℃ higher than normal, so it was recorded as the third highest for the same historical period since 1961. The monthly average precipitation was 77.6 mm, 10% more than normal. During this month, six torrential rain and severe convection processes occurred in China, with precipitation at many stations breaking their historical extremes, causing floods and secondary geological disasters in Guizhou, Guangxi, Guangdong, Hunan, Jiangxi provinces and other regions. On 4 May, thunderstorm gale at speed of 44.7 m·s-1 was monitored in Qianxi City, Guizhou Province. On 8 May, EF1 tornadoes occurred in Qidong County and Liling City, Hunan Province. In addition, there were five sand-dust events affecting northern China during this month, which are noticeably more than the average for the same period from 2000 to 2024. , there were five sanddust events affecting northern China during this month, which are noticeably more than the average for the same period from 2000 to 2024.
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ISSN:1000-0526 CN:11-2282/P