Volume 53,Issue 5,2025 Table of Contents
2025, 53(5):617-633.
DOI: 10.19517/j.1671-6345.20250025
Abstract:
Accurate nowcasting provides key information for disaster weather warning and artificial weather operations. Nowcasting is mostly based on radar echo extrapolation, where the evolution of echoes results from complex interactions among cloud systems and various thermal-dynamic features of meteorological elements. In this study, a multi-channel radar echo extrapolation architecture (UGR) based on UNet and GAN with Radarcells is proposed, and a self-defined loss function combining weighted mean square error and binary cross-entropy is designed by introducing a penalty term in the GAN network to improve model training. Four sets of radar units (Radarcells) are encoded using radar combined reflectivity mosaic data and four types of physical elements acquired from the Beijing rapid updating circular numerical forecasting system (CMA-BJ). Then, 20 UGR-based models are trained with the Radarcells as inputs for sequential forecasting every six minutes to achieve a rolling 120-minute echo extrapolation. To verify the improvement effect after introducing weather background on echo extrapolation, UNet-based models and ConvLSTM-based models that use only radar echoes as input are trained for comparison, respectively. Critical Success Index (CSI), Probability of Detection (POD), False Alarm Rate (FAR), and Bias score (Bias) are used to evaluate the model on the test set. The results show that the echo intensity range and spatiotemporal evolution predicted by UGR-based models incorporating weather background information perform better than the UNet-based and ConvLSTM-based models driven solely by radar data, especially in predicting strong echoes more accurately. Under the reflectivity thresholds of 25 and 35 dBz, the average values of CSI, FAR, POD, and Bias calculated by UGR-based models are improveed by 10.5%, 8.6%, 10.3%, 4.8%, and 13.4%, 4.6%, 11.0%, and 7.4%, respectively, compared to those by UNet-based models. The study suggests that extrapolation models incorporating weather background information can effectively improve issues of echo blurriness and insufficient echo formation-dissipation compared with previous deep learning-based extrapolation models.
Accurate nowcasting provides key information for disaster weather warning and artificial weather operations. Nowcasting is mostly based on radar echo extrapolation, where the evolution of echoes results from complex interactions among cloud systems and various thermal-dynamic features of meteorological elements. In this study, a multi-channel radar echo extrapolation architecture (UGR) based on UNet and GAN with Radarcells is proposed, and a self-defined loss function combining weighted mean square error and binary cross-entropy is designed by introducing a penalty term in the GAN network to improve model training. Four sets of radar units (Radarcells) are encoded using radar combined reflectivity mosaic data and four types of physical elements acquired from the Beijing rapid updating circular numerical forecasting system (CMA-BJ). Then, 20 UGR-based models are trained with the Radarcells as inputs for sequential forecasting every six minutes to achieve a rolling 120-minute echo extrapolation. To verify the improvement effect after introducing weather background on echo extrapolation, UNet-based models and ConvLSTM-based models that use only radar echoes as input are trained for comparison, respectively. Critical Success Index (CSI), Probability of Detection (POD), False Alarm Rate (FAR), and Bias score (Bias) are used to evaluate the model on the test set. The results show that the echo intensity range and spatiotemporal evolution predicted by UGR-based models incorporating weather background information perform better than the UNet-based and ConvLSTM-based models driven solely by radar data, especially in predicting strong echoes more accurately. Under the reflectivity thresholds of 25 and 35 dBz, the average values of CSI, FAR, POD, and Bias calculated by UGR-based models are improveed by 10.5%, 8.6%, 10.3%, 4.8%, and 13.4%, 4.6%, 11.0%, and 7.4%, respectively, compared to those by UNet-based models. The study suggests that extrapolation models incorporating weather background information can effectively improve issues of echo blurriness and insufficient echo formation-dissipation compared with previous deep learning-based extrapolation models.
2025, 53(5):634-645.
DOI: 10.19517/j.1671-6345.20250002
Abstract:
Polarimetric technology gradually becomes widespread in operational weather radars in China. In addition to conventional radar parameters such as reflectivity factor, Doppler velocity, and spectrum width, polarimetric weather radars also obtain dual-polarisation parameters such as differential reflectivity, differential phase, and correlation coefficient, which further expand the application range of weather radar data. Compared to conventional radar parameters, dual-polarisation parameters are more susceptible to ground clutter, requiring higher performance from ground clutter suppression algorithms. Operational weather radars currently use CMD (Clutter Mitigation Decision) and GMAP (Gaussian Model Adaptive Processing) algorithms for ground clutter identification and filtering. However, existing research indicates that these operational algorithms have insufficient ground clutter suppression capabilities, resulting in lower data quality for dual-polarisation parameters such as correlation coefficients, which affects the performance of radar products such as hydrometeor classification and melting layer identification. This paper proposes a ground clutter suppression algorithm for polarimetric weather radars, improving the existing operational algorithms in terms of ground clutter identification and filtering. For ground clutter identification, the CMD algorithm is initially executed. Based on its preliminary identification results, additional steps such as the correlation coefficient test are added for secondary identification of ground clutter, improving identification performance under low clutter-to-signal ratio conditions. For ground clutter filtering, a time-domain regression filter is used to replace the frequency-domain GMAP algorithm, avoiding the sampling information loss caused by windowing in GMAP. This ensures that ground clutter suppression no longer affects the accuracy of parameter estimation. The algorithm’s ground clutter suppression performance is evaluated based on eight precipitation events (one snowfall and seven rainfalls) observed by the S-band standard weather radar at the Changsha Meteorological Radar Calibration Centre. To quantify the performance of the ground clutter suppression algorithm, a data quality assessment metric based on correlation coefficient, differential reflectivity texture, and differential phase texture is proposed, named High-Quality Polarimetric Data Ratio (HPR). This metric characterises the extent to which dual-polarisation parameter data quality is affected by ground clutter. It is defined as the ratio of the number of range bins where the correlation coefficient, differential reflectivity texture, and differential phase texture meet specific thresholds to the total number of effective range bins, under conditions where meteorological echoes cover ground clutter within a certain distance from the radar. Evaluation results indicate that the algorithm significantly improves ground clutter suppression under low clutter-to-signal ratio conditions compared to existing operational algorithms, with an HPR value increase of about 0.4. This means that the usability of dual-polarisation parameters within a 100 km range of the radar increases by approximately 40%. Next, a more in-depth evaluation of the algorithm continues, along with the initiation of its trial operation in practical applications.
Polarimetric technology gradually becomes widespread in operational weather radars in China. In addition to conventional radar parameters such as reflectivity factor, Doppler velocity, and spectrum width, polarimetric weather radars also obtain dual-polarisation parameters such as differential reflectivity, differential phase, and correlation coefficient, which further expand the application range of weather radar data. Compared to conventional radar parameters, dual-polarisation parameters are more susceptible to ground clutter, requiring higher performance from ground clutter suppression algorithms. Operational weather radars currently use CMD (Clutter Mitigation Decision) and GMAP (Gaussian Model Adaptive Processing) algorithms for ground clutter identification and filtering. However, existing research indicates that these operational algorithms have insufficient ground clutter suppression capabilities, resulting in lower data quality for dual-polarisation parameters such as correlation coefficients, which affects the performance of radar products such as hydrometeor classification and melting layer identification. This paper proposes a ground clutter suppression algorithm for polarimetric weather radars, improving the existing operational algorithms in terms of ground clutter identification and filtering. For ground clutter identification, the CMD algorithm is initially executed. Based on its preliminary identification results, additional steps such as the correlation coefficient test are added for secondary identification of ground clutter, improving identification performance under low clutter-to-signal ratio conditions. For ground clutter filtering, a time-domain regression filter is used to replace the frequency-domain GMAP algorithm, avoiding the sampling information loss caused by windowing in GMAP. This ensures that ground clutter suppression no longer affects the accuracy of parameter estimation. The algorithm’s ground clutter suppression performance is evaluated based on eight precipitation events (one snowfall and seven rainfalls) observed by the S-band standard weather radar at the Changsha Meteorological Radar Calibration Centre. To quantify the performance of the ground clutter suppression algorithm, a data quality assessment metric based on correlation coefficient, differential reflectivity texture, and differential phase texture is proposed, named High-Quality Polarimetric Data Ratio (HPR). This metric characterises the extent to which dual-polarisation parameter data quality is affected by ground clutter. It is defined as the ratio of the number of range bins where the correlation coefficient, differential reflectivity texture, and differential phase texture meet specific thresholds to the total number of effective range bins, under conditions where meteorological echoes cover ground clutter within a certain distance from the radar. Evaluation results indicate that the algorithm significantly improves ground clutter suppression under low clutter-to-signal ratio conditions compared to existing operational algorithms, with an HPR value increase of about 0.4. This means that the usability of dual-polarisation parameters within a 100 km range of the radar increases by approximately 40%. Next, a more in-depth evaluation of the algorithm continues, along with the initiation of its trial operation in practical applications.
2025, 53(5):646-655.
DOI: 10.19517/j.1671-6345.20250072
Abstract:
Radar meteorological algorithms play a crucial role in meteorological monitoring and forecasting, and their rapid deployment and application in operational systems significantly improve radar data quality and operational efficiency. To improve the data quality of weather radar and the effectiveness of localised applications, this study relies on the open interface architecture of the new generation weather radar operational software (ROSE 3.0) to carry out localised design and system integration research of radar meteorological algorithm modules. At first, the open interface functionality, modular development process, and integration methods of ROSE 3.0 are introduced in detail. Subsequently, taking the radar data quality control algorithm as the research object, a non-meteorological echo recognition model for dual-polarisation radar is developed using the fuzzy logic method. Key radar parameters such as radial velocity, differential reflectivity texture, correlation coefficient, and differential phase shift texture are utilised to implement the radar data quality control algorithm through the ROSE 3.0 standard API function and dynamic link library. Then, this radar data quality control algorithm is successfully deployed in the ROSE 3.0 operational systems of Suizhou and Jingzhou radar stations in Hubei Province. Using two radar observation cases from Suizhou and Jingzhou radar stations for operational tests, it is shown that the radar data quality control algorithm robustly and efficiently identifies and eliminates non-meteorological echoes such as ground clutter, biological targets, and aluminium foil interference, while the precipitation echoes experience no loss. By comparing and analysing the radar reflectivity factors before and after quality control, a significant improvement in the quality and reliability of radar data is found, which enhances the operational efficiency of precipitation monitoring and short-term forecasting systems. Additionally, the results of this study also validate the significant advantages of ROSE 3.0 in supporting rapid algorithm integration, flexible expansion, and business transformation, effectively promoting the localised deployment of radar meteorological algorithms. It should be pointed out that this study mainly focuses on the interface mechanism and integration process of ROSE 3.0. The effectiveness of radar data quality control is mainly analysed through case studies, and there is a lack of systematic analysis on the accuracy evaluation and localisation optimisation strategies of radar data quality control algorithms. In future research, it is necessary to continuously improve the accuracy evaluation of weather radar quality control algorithms and further expand ROSE 3.0 functionality to support multiple data formats and more complex meteorological processing techniques, in order to better serve the new needs of meteorological operational development.
Radar meteorological algorithms play a crucial role in meteorological monitoring and forecasting, and their rapid deployment and application in operational systems significantly improve radar data quality and operational efficiency. To improve the data quality of weather radar and the effectiveness of localised applications, this study relies on the open interface architecture of the new generation weather radar operational software (ROSE 3.0) to carry out localised design and system integration research of radar meteorological algorithm modules. At first, the open interface functionality, modular development process, and integration methods of ROSE 3.0 are introduced in detail. Subsequently, taking the radar data quality control algorithm as the research object, a non-meteorological echo recognition model for dual-polarisation radar is developed using the fuzzy logic method. Key radar parameters such as radial velocity, differential reflectivity texture, correlation coefficient, and differential phase shift texture are utilised to implement the radar data quality control algorithm through the ROSE 3.0 standard API function and dynamic link library. Then, this radar data quality control algorithm is successfully deployed in the ROSE 3.0 operational systems of Suizhou and Jingzhou radar stations in Hubei Province. Using two radar observation cases from Suizhou and Jingzhou radar stations for operational tests, it is shown that the radar data quality control algorithm robustly and efficiently identifies and eliminates non-meteorological echoes such as ground clutter, biological targets, and aluminium foil interference, while the precipitation echoes experience no loss. By comparing and analysing the radar reflectivity factors before and after quality control, a significant improvement in the quality and reliability of radar data is found, which enhances the operational efficiency of precipitation monitoring and short-term forecasting systems. Additionally, the results of this study also validate the significant advantages of ROSE 3.0 in supporting rapid algorithm integration, flexible expansion, and business transformation, effectively promoting the localised deployment of radar meteorological algorithms. It should be pointed out that this study mainly focuses on the interface mechanism and integration process of ROSE 3.0. The effectiveness of radar data quality control is mainly analysed through case studies, and there is a lack of systematic analysis on the accuracy evaluation and localisation optimisation strategies of radar data quality control algorithms. In future research, it is necessary to continuously improve the accuracy evaluation of weather radar quality control algorithms and further expand ROSE 3.0 functionality to support multiple data formats and more complex meteorological processing techniques, in order to better serve the new needs of meteorological operational development.
2025, 53(5):656-667.
DOI: 10.19517/j.1671-6345.20240387
Abstract:
With the continuous increase in the demand for refined meteorological forecasting, high-resolution numerical models make progress in the forecasting capability of extreme precipitation events. However, limited by factors such as errors in the initial field of the model, parameterisation uncertainties, and terrain, there are still systematic bias problems in precipitation forecasting. The convective-scale numerical model in Guangxi (GX(R1)) faces the technical bottleneck of urgently needing to improve the accuracy in short-term strong precipitation forecasting. In response to this issue, this study proposes a deep learning method based on multi-source data fusion, the MResUNet model. This model achieves performance breakthroughs through three technical improvements: first, the squeeze-and-excitation (SE) module is introduced to construct a channel attention mechanism, dynamically adjust feature weights, and suppress noise interference in the model output; second, the atrous spatial pyramid pooling (ASPP) module is integrated to fuse multi-scale features and improve the positioning accuracy of precipitation areas; third, a multi-modal data fusion framework is constructed to integrate the advantages of convective-scale numerical forecasting products in Guangxi, radar echo data, ground observation data, and the ResUNet++ model. The weighted loss function is optimised, and different weights are assigned to different precipitation intensities according to the measured rainfall data at stations, significantly enhancing the model’s sensitivity to extreme precipitation events. To verify the effectiveness of the MResUNet model, four sets of control experiments are designed: Scheme 1 retains the original output of the GX(R1) model as the benchmark; Scheme 2 uses the input containing only numerical model products (X1, X2); Scheme 3 uses only radar echo data (X3); Scheme 4 fuses the model products and radar echo data and implements a multi-source data fusion strategy. The experimental results show that all MResUNet schemes are significantly better than the original model output, and the TS (Threat Score) scores exhibit positive skill characteristics. In particular, for Scheme 4, under the thresholds of strong precipitation of ≥20 mm/h and ≥30 mm/h, the TS scores increase by 9.77% and 8.98%, respectively. It shows significant advantages in reducing the false alarm rate. The hit rate at the precipitation level of ≥30 mm is higher than that of Scheme 3 and much higher than that of Scheme 2, with an increase of 5.45% and 177%, respectively, and the prediction bias is the closest to the ideal value of 1. Further, the extreme precipitation event of “Dragon Boat Water” in Guangxi in 2023 and typical typhoon precipitation processes are selected for case verification. The analysis shows that the MResUNet effectively solves the problem of dispersed prediction of the strong precipitation centre in the GX(R1) model and demonstrates excellent prediction capabilities in both the magnitude of precipitation intensity and the location of precipitation areas. This study proves that by deeply integrating multi-source observational data and numerical model products and optimising the architecture of the deep learning model, the forecasting accuracy of different precipitation magnitudes can be significantly improved. In particular, the scheme based on multi-source data fusion shows obvious advantages in strong precipitation forecasting. The research results provide a new technical approach to solve the technical problems in short-term precipitation forecasting.
With the continuous increase in the demand for refined meteorological forecasting, high-resolution numerical models make progress in the forecasting capability of extreme precipitation events. However, limited by factors such as errors in the initial field of the model, parameterisation uncertainties, and terrain, there are still systematic bias problems in precipitation forecasting. The convective-scale numerical model in Guangxi (GX(R1)) faces the technical bottleneck of urgently needing to improve the accuracy in short-term strong precipitation forecasting. In response to this issue, this study proposes a deep learning method based on multi-source data fusion, the MResUNet model. This model achieves performance breakthroughs through three technical improvements: first, the squeeze-and-excitation (SE) module is introduced to construct a channel attention mechanism, dynamically adjust feature weights, and suppress noise interference in the model output; second, the atrous spatial pyramid pooling (ASPP) module is integrated to fuse multi-scale features and improve the positioning accuracy of precipitation areas; third, a multi-modal data fusion framework is constructed to integrate the advantages of convective-scale numerical forecasting products in Guangxi, radar echo data, ground observation data, and the ResUNet++ model. The weighted loss function is optimised, and different weights are assigned to different precipitation intensities according to the measured rainfall data at stations, significantly enhancing the model’s sensitivity to extreme precipitation events. To verify the effectiveness of the MResUNet model, four sets of control experiments are designed: Scheme 1 retains the original output of the GX(R1) model as the benchmark; Scheme 2 uses the input containing only numerical model products (X1, X2); Scheme 3 uses only radar echo data (X3); Scheme 4 fuses the model products and radar echo data and implements a multi-source data fusion strategy. The experimental results show that all MResUNet schemes are significantly better than the original model output, and the TS (Threat Score) scores exhibit positive skill characteristics. In particular, for Scheme 4, under the thresholds of strong precipitation of ≥20 mm/h and ≥30 mm/h, the TS scores increase by 9.77% and 8.98%, respectively. It shows significant advantages in reducing the false alarm rate. The hit rate at the precipitation level of ≥30 mm is higher than that of Scheme 3 and much higher than that of Scheme 2, with an increase of 5.45% and 177%, respectively, and the prediction bias is the closest to the ideal value of 1. Further, the extreme precipitation event of “Dragon Boat Water” in Guangxi in 2023 and typical typhoon precipitation processes are selected for case verification. The analysis shows that the MResUNet effectively solves the problem of dispersed prediction of the strong precipitation centre in the GX(R1) model and demonstrates excellent prediction capabilities in both the magnitude of precipitation intensity and the location of precipitation areas. This study proves that by deeply integrating multi-source observational data and numerical model products and optimising the architecture of the deep learning model, the forecasting accuracy of different precipitation magnitudes can be significantly improved. In particular, the scheme based on multi-source data fusion shows obvious advantages in strong precipitation forecasting. The research results provide a new technical approach to solve the technical problems in short-term precipitation forecasting.
5 Evaluation of Prediction Effect of NCEP CFSv2 on Situation Field of Two Intense Freezing Processes
2025, 53(5):668-680.
DOI: 10.19517/j.1671-6345.20250016
Abstract:
The historical hindcast data of different starting dates provided by the second-generation climate forecasting system (NCEP CFSv2) of the United States Environmental Forecasting Centre are used to predict the wind, temperature, and height during the two strong freezing processes in 2008 and 2011. Through comparative analysis with the actual field, the predictive ability of the CFSv2 model for severe freezing processes is evaluated. The results show that at the different starting dates within one week in advance, the NCEP CFSv2 model has a certain ability to predict the wind field characteristics of the two processes (at 850 hPa: northeasterly return flow in the northeastern part of Guizhou, southerly wind in south Guizhou, southwest wind in west Guizhou; 700 hPa southwest jet stream), temperature field characteristics (the 0 ℃ line position of 850 hPa,700 hPa and whole layer temperature field; temperature gradient of middle and low layers; temperature inversion zone of 850 hPa and 700 hPa), and height field characteristics (the variation of the 500 hPa 564 dagpm characteristic line, the subtropical westerly jet stream). However, there are deviations in the forecast of wind speed at 850 hPa, the position of the South China quasi-stationary front, the north-south position of the 0 ℃ line on the entire layer temperature field, the north-south position of the 564 dagpm characteristic line, and the intensity of the subtropical westerly jet stream. At the same time, there are situations such as the wind speed of the 700 hPa southwest jet stream being relatively small, the temperature gradient of the 850 hPa temperature field being relatively small, the 0 ℃ line position of the 700 hPa temperature field being relatively south, the intensity of the inversion zone being relatively weak and the area being relatively small, and the temperature intensity of the middle and upper layers being relatively strong. Moreover, there is basically no predictive ability for the warm-core structure of the entire floor temperature field. From the prediction effect of different starting dates, the prediction effect of 1 d in advance is obviously better than that of 4 d and 7 d in advance.
The historical hindcast data of different starting dates provided by the second-generation climate forecasting system (NCEP CFSv2) of the United States Environmental Forecasting Centre are used to predict the wind, temperature, and height during the two strong freezing processes in 2008 and 2011. Through comparative analysis with the actual field, the predictive ability of the CFSv2 model for severe freezing processes is evaluated. The results show that at the different starting dates within one week in advance, the NCEP CFSv2 model has a certain ability to predict the wind field characteristics of the two processes (at 850 hPa: northeasterly return flow in the northeastern part of Guizhou, southerly wind in south Guizhou, southwest wind in west Guizhou; 700 hPa southwest jet stream), temperature field characteristics (the 0 ℃ line position of 850 hPa,700 hPa and whole layer temperature field; temperature gradient of middle and low layers; temperature inversion zone of 850 hPa and 700 hPa), and height field characteristics (the variation of the 500 hPa 564 dagpm characteristic line, the subtropical westerly jet stream). However, there are deviations in the forecast of wind speed at 850 hPa, the position of the South China quasi-stationary front, the north-south position of the 0 ℃ line on the entire layer temperature field, the north-south position of the 564 dagpm characteristic line, and the intensity of the subtropical westerly jet stream. At the same time, there are situations such as the wind speed of the 700 hPa southwest jet stream being relatively small, the temperature gradient of the 850 hPa temperature field being relatively small, the 0 ℃ line position of the 700 hPa temperature field being relatively south, the intensity of the inversion zone being relatively weak and the area being relatively small, and the temperature intensity of the middle and upper layers being relatively strong. Moreover, there is basically no predictive ability for the warm-core structure of the entire floor temperature field. From the prediction effect of different starting dates, the prediction effect of 1 d in advance is obviously better than that of 4 d and 7 d in advance.
2025, 53(5):681-690.
DOI: 10.19517/j.1671-6345.20250029
Abstract:
To investigate the spatiotemporal distribution characteristics of Convective Initiation (CI) during the warm season (May-September) over the North China region, this study utilises composite reflectivity mosaic data from regional radar networks across North China for the warm seasons spanning 2022 to 2024. An objective identification algorithm specifically designed to detect “isolated” CI events is developed. Leveraging this algorithm, a comprehensive CI database for North China is subsequently constructed. Detailed statistical analyses are then performed on this database to elucidate both the temporal and spatial patterns of CI occurrence within the region. The principal findings derived from this research are summarised as follows: (1) Sensitivity experiments focus on two key parameters within the objective CI identification algorithm: the lower threshold for convective cell area (A) and the screening radius (R). The results demonstrate a significant sensitivity in the number of identified CI events to variations in these parameters. Specifically, setting a larger threshold value for the minimum convective cell area (A) effectively filters out nascent convective cells that fail to develop further. Conversely, employing a larger screening radius (R) aids in filtering out newly initiated convective cells occurring in close proximity to pre-existing, already identified convective cells, thereby refining the detection to focus on truly isolated initiation events. (2) The temporal distribution of CI events during the warm season in North China exhibits distinct patterns. Monthly analysis reveals that the highest frequency of CI occurrences is concentrated in July and August, while May registers the lowest number. Diurnally, CI events display a pronounced unimodal structure, with a clear peak in activity observed between 13:00 and 17:00. (3) Spatially, CI events during the warm season in North China show a highly heterogeneous distribution. High-frequency CI zones are predominantly concentrated along major mountain ranges and their foothills. These include the Yinshan Mountains in Inner Mongolia, the northern Yanshan Mountains near Beijing, the Lvliang Mountains on the western flank of Shanxi Province; the Taihang Mountains along the eastern border of Shanxi Province, the Zhongtiao Mountains in southern Shanxi, the eastern extensions of the Qinling Mountains in Henan Province, and the western and northern flanks of the Taiyi Mountains in Shandong Province. Notably, exceptionally high CI frequency zones are identified within the southern segment of the Taihang Mountains (eastern Shanxi) and the eastern Qinling Mountain extensions in Henan. Conversely, regions exhibiting significantly lower CI frequency encompass the Inner Mongolia Plateau, the Hetao Plateau, the basins situated between the mountain ranges flanking Shanxi Province, and the expansive North China Plain. This clear spatial dichotomy underscores that CI hotspots are overwhelmingly associated with mountainous terrain, while CI-sparse areas predominantly coincide with relatively flat plateaus, basins, and plains. These research findings, detailing the objective identification methodology and the resulting spatiotemporal climatology of CI over North China, provide valuable insights and offer a substantive reference for enhancing CI potential forecasting and nowcasting capabilities in the region.
To investigate the spatiotemporal distribution characteristics of Convective Initiation (CI) during the warm season (May-September) over the North China region, this study utilises composite reflectivity mosaic data from regional radar networks across North China for the warm seasons spanning 2022 to 2024. An objective identification algorithm specifically designed to detect “isolated” CI events is developed. Leveraging this algorithm, a comprehensive CI database for North China is subsequently constructed. Detailed statistical analyses are then performed on this database to elucidate both the temporal and spatial patterns of CI occurrence within the region. The principal findings derived from this research are summarised as follows: (1) Sensitivity experiments focus on two key parameters within the objective CI identification algorithm: the lower threshold for convective cell area (A) and the screening radius (R). The results demonstrate a significant sensitivity in the number of identified CI events to variations in these parameters. Specifically, setting a larger threshold value for the minimum convective cell area (A) effectively filters out nascent convective cells that fail to develop further. Conversely, employing a larger screening radius (R) aids in filtering out newly initiated convective cells occurring in close proximity to pre-existing, already identified convective cells, thereby refining the detection to focus on truly isolated initiation events. (2) The temporal distribution of CI events during the warm season in North China exhibits distinct patterns. Monthly analysis reveals that the highest frequency of CI occurrences is concentrated in July and August, while May registers the lowest number. Diurnally, CI events display a pronounced unimodal structure, with a clear peak in activity observed between 13:00 and 17:00. (3) Spatially, CI events during the warm season in North China show a highly heterogeneous distribution. High-frequency CI zones are predominantly concentrated along major mountain ranges and their foothills. These include the Yinshan Mountains in Inner Mongolia, the northern Yanshan Mountains near Beijing, the Lvliang Mountains on the western flank of Shanxi Province; the Taihang Mountains along the eastern border of Shanxi Province, the Zhongtiao Mountains in southern Shanxi, the eastern extensions of the Qinling Mountains in Henan Province, and the western and northern flanks of the Taiyi Mountains in Shandong Province. Notably, exceptionally high CI frequency zones are identified within the southern segment of the Taihang Mountains (eastern Shanxi) and the eastern Qinling Mountain extensions in Henan. Conversely, regions exhibiting significantly lower CI frequency encompass the Inner Mongolia Plateau, the Hetao Plateau, the basins situated between the mountain ranges flanking Shanxi Province, and the expansive North China Plain. This clear spatial dichotomy underscores that CI hotspots are overwhelmingly associated with mountainous terrain, while CI-sparse areas predominantly coincide with relatively flat plateaus, basins, and plains. These research findings, detailing the objective identification methodology and the resulting spatiotemporal climatology of CI over North China, provide valuable insights and offer a substantive reference for enhancing CI potential forecasting and nowcasting capabilities in the region.
2025, 53(5):691-705.
DOI: 10.19517/j.1671-6345.20240378
Abstract:
On the morning of August 26, 2017, an extreme convective heavy rainfall event occurred in the main urban area of Hangzhou, with a maximum hourly precipitation rate reaching 127.0 mm, breaking the historical record for hourly rainfall in the region. Both mesoscale regional models and global large-scale numerical models failed to forecast this extreme rainfall event. This study utilises conventional observational data, high-resolution surface observations, weather radar data, and NCEP/GFS 0.25°×0.25° 3-hourly analysis fields to investigate the synoptic background, physical parameter fields, and the development mechanisms of meso- and micro-scale systems during this event. The main findings are as follows. (1) Synoptic Background and Large-Scale Conditions: The rainfall event occurred under the influence of the western Pacific subtropical high (WPSH). At the 500 hPa level, Hangzhou was located near the ridge line of the WPSH, while in the lower troposphere and at the surface, it was situated within a saddle-shaped pressure field. This configuration resulted in weak steering flows, and the saddle-shaped structure restricted the movement of weather systems, allowing slow-moving or quasi-stationary mesoscale convective systems (MCSs) to persist, which was highly favourable for heavy rainfall. (2) Favourable Thermodynamic and Moisture Conditions: The environmental conditions exhibited strong convective instability, with a low lifting condensation level (LCL) and a deep warm cloud layer, enhancing precipitation efficiency. The boundary layer was nearly saturated, and extreme values were observed in parameters such as precipitable water (PWAT) and surface dew point temperature. Additionally, weak vertical wind shear in the environment contributed to high precipitation efficiency by allowing convective updrafts to remain vertically aligned without significant disruption. (3) Boundary Layer Convergence and Dynamic Lifting Mechanisms: The northeasterly flow at the base of a surface high-pressure system intensified and was obstructed by local topography, leading to cyclonic convergence and wind speed convergence in the boundary layer over Hangzhou and northern Shaoxing. A convergence line formed 3 hours before precipitation initiation and persisted for an extended period, providing sustained dynamic lifting for convective triggering and development. (4) Low-Level Jet and Moisture Transport: A boundary-layer easterly ultra-low-level jet (ULLJ) combined with southeasterly warm and moist flows at 850 hPa provided continuous moisture transport and energy supply, sustaining the convection and contributing to the extreme rainfall intensity. (5) Quasi-Stationary MCS: The generation, merging, and intersection of convective cells, along with the parallel but opposite directions of advection and propagation, caused the MCS to remain nearly stationary. This led to multiple heavy rainfall cores overlapping in the same region, which was a crucial factor in the extreme precipitation accumulation.
On the morning of August 26, 2017, an extreme convective heavy rainfall event occurred in the main urban area of Hangzhou, with a maximum hourly precipitation rate reaching 127.0 mm, breaking the historical record for hourly rainfall in the region. Both mesoscale regional models and global large-scale numerical models failed to forecast this extreme rainfall event. This study utilises conventional observational data, high-resolution surface observations, weather radar data, and NCEP/GFS 0.25°×0.25° 3-hourly analysis fields to investigate the synoptic background, physical parameter fields, and the development mechanisms of meso- and micro-scale systems during this event. The main findings are as follows. (1) Synoptic Background and Large-Scale Conditions: The rainfall event occurred under the influence of the western Pacific subtropical high (WPSH). At the 500 hPa level, Hangzhou was located near the ridge line of the WPSH, while in the lower troposphere and at the surface, it was situated within a saddle-shaped pressure field. This configuration resulted in weak steering flows, and the saddle-shaped structure restricted the movement of weather systems, allowing slow-moving or quasi-stationary mesoscale convective systems (MCSs) to persist, which was highly favourable for heavy rainfall. (2) Favourable Thermodynamic and Moisture Conditions: The environmental conditions exhibited strong convective instability, with a low lifting condensation level (LCL) and a deep warm cloud layer, enhancing precipitation efficiency. The boundary layer was nearly saturated, and extreme values were observed in parameters such as precipitable water (PWAT) and surface dew point temperature. Additionally, weak vertical wind shear in the environment contributed to high precipitation efficiency by allowing convective updrafts to remain vertically aligned without significant disruption. (3) Boundary Layer Convergence and Dynamic Lifting Mechanisms: The northeasterly flow at the base of a surface high-pressure system intensified and was obstructed by local topography, leading to cyclonic convergence and wind speed convergence in the boundary layer over Hangzhou and northern Shaoxing. A convergence line formed 3 hours before precipitation initiation and persisted for an extended period, providing sustained dynamic lifting for convective triggering and development. (4) Low-Level Jet and Moisture Transport: A boundary-layer easterly ultra-low-level jet (ULLJ) combined with southeasterly warm and moist flows at 850 hPa provided continuous moisture transport and energy supply, sustaining the convection and contributing to the extreme rainfall intensity. (5) Quasi-Stationary MCS: The generation, merging, and intersection of convective cells, along with the parallel but opposite directions of advection and propagation, caused the MCS to remain nearly stationary. This led to multiple heavy rainfall cores overlapping in the same region, which was a crucial factor in the extreme precipitation accumulation.
2025, 53(5):706-718.
DOI: 10.19517/j.1671-6345.20240360
Abstract:
Using the data from ground automatic stations, the Kuihua 8 satellite, radiosonde, wind profile radar, microwave radiometer, automatic observation system minute-by-minute observation, and ERA5 reanalysis, the generation and dissipation mechanism of a thick fog process on 6 March 2024 along the coast of southern Fujian and Xiamen Airport is analysed. The results show that during the formation of the fog, the surface was under a weak pressure field ahead of the cold front, with unified southwest warm and moist air currents prevailing in the middle and lower atmospheric levels. The heavy fog first appeared on the eastern sea surface, and there was a cold water zone in the Taiwan Strait. At 1000 hPa, there was strong warm advection in the Taiwan Strait, which was superimposed on the cold water zone to produce advection cooling fog. The temperature difference between the sea and air near the sea was between 0 and 3.2 ℃. After its formation, the sea fog spread westward to the land at night, showing a good spatiotemporal coupling relationship with the westward advance of the 1000 hPa warm advection. The formation of a nocturnal surface inversion and the increase in the thickness of the weak wind layer provided stable stratification conditions for the generation and development of the fog, while the long-term water vapour saturation at the surface provided sufficient moisture conditions for the generation and development of the fog. High-resolution satellite data monitored the evolutionary process where the sea fog spread from Kinmen and Xiang’an to Xiamen Airport at night, after which the fog in Xiamen Airport and its nearby small area dissipated while the sea fog persisted. Xiamen Airport experienced heavy fog from 03:22 to 04:47 (Beijing Time) on the 6th, lasting approximately 1.5 hours. With rapid onset and dissipation, it was classified as an explosive heavy fog event. The short-term sudden changes in surface wind direction and the sudden increase in wind speed at Xiamen Airport enhanced the local cold advection transport, introducing cold air from Tong’an Bay on the northeast side into the airport, accelerating the condensation of water vapour in the near-surface layer of the airport, which was an important thermal factor for the explosive formation of the heavy fog. The enhanced long-wave radiation before the fog was another thermal cooling factor. Analysis shows that the diffusion of the fog mass in Tong’an Bay combined with the “trumpet-shaped” terrain caused passive adjustment of the wind direction at the airport, and the propagation of fluctuations from the upstream cold air southward led to short-term changes in the wind at Xiamen Airport.
Using the data from ground automatic stations, the Kuihua 8 satellite, radiosonde, wind profile radar, microwave radiometer, automatic observation system minute-by-minute observation, and ERA5 reanalysis, the generation and dissipation mechanism of a thick fog process on 6 March 2024 along the coast of southern Fujian and Xiamen Airport is analysed. The results show that during the formation of the fog, the surface was under a weak pressure field ahead of the cold front, with unified southwest warm and moist air currents prevailing in the middle and lower atmospheric levels. The heavy fog first appeared on the eastern sea surface, and there was a cold water zone in the Taiwan Strait. At 1000 hPa, there was strong warm advection in the Taiwan Strait, which was superimposed on the cold water zone to produce advection cooling fog. The temperature difference between the sea and air near the sea was between 0 and 3.2 ℃. After its formation, the sea fog spread westward to the land at night, showing a good spatiotemporal coupling relationship with the westward advance of the 1000 hPa warm advection. The formation of a nocturnal surface inversion and the increase in the thickness of the weak wind layer provided stable stratification conditions for the generation and development of the fog, while the long-term water vapour saturation at the surface provided sufficient moisture conditions for the generation and development of the fog. High-resolution satellite data monitored the evolutionary process where the sea fog spread from Kinmen and Xiang’an to Xiamen Airport at night, after which the fog in Xiamen Airport and its nearby small area dissipated while the sea fog persisted. Xiamen Airport experienced heavy fog from 03:22 to 04:47 (Beijing Time) on the 6th, lasting approximately 1.5 hours. With rapid onset and dissipation, it was classified as an explosive heavy fog event. The short-term sudden changes in surface wind direction and the sudden increase in wind speed at Xiamen Airport enhanced the local cold advection transport, introducing cold air from Tong’an Bay on the northeast side into the airport, accelerating the condensation of water vapour in the near-surface layer of the airport, which was an important thermal factor for the explosive formation of the heavy fog. The enhanced long-wave radiation before the fog was another thermal cooling factor. Analysis shows that the diffusion of the fog mass in Tong’an Bay combined with the “trumpet-shaped” terrain caused passive adjustment of the wind direction at the airport, and the propagation of fluctuations from the upstream cold air southward led to short-term changes in the wind at Xiamen Airport.
2025, 53(5):719-737.
DOI: 10.19517/j.1671-6345.20250015
Abstract:
This study systematically investigates the microphysical characteristics of warm-season precipitation over northern Zhejiang Province, China, based on comprehensive observations from six laser precipitation monitors (LPMs) and two S-band dual-polarisation weather radars (HZ-SPOL and JX-SPOL) collected between March and October 2022. In addition, the performance of dual-polarisation radar retrievals of raindrop size distribution (DSD) parameters, specifically the mass-weighted mean diameter (D0) and the normalised intercept parameter (Nw) of the gamma distribution, is quantitatively evaluated. The results reveal the following: (1) Precipitation in the northern Zhejiang region is predominantly characterised by high concentrations of small raindrops, with an average D0 of 1.0 mm and a mean Nw of 104.3m-3·mm-1, exhibiting typical features of warm-cloud precipitation. A classification approach based on the statistical properties (mean and standard deviation) of rainfall rate effectively discriminates between stratiform and convective precipitation. Stratiform precipitation shows relatively narrow DSDs and low spatial variability across sites, with low rainfall intensity (R<10 mm·h-1) and smaller drop sizes (D0 <1.0 mm). In contrast, convective precipitation exhibits significantly broader spectra, with D0 and average R values reaching up to 1.8 mm and 19 mm·h-1, respectively, and displays much greater spatial heterogeneity. Medium-sized raindrops (1.0-2.0 mm) contribute the largest fraction (33.4%) to total rainfall accumulation, while larger drops (D0 >2.5 mm), though relatively infrequent, still account for 10.3% of total rainfall volume. (2) Simulated polarimetric variables at S-band—reflectivity (Z), differential reflectivity (ZDR), and specific differential phase (Kdp)—calculated using a T-matrix scattering model based on local DSDs, show good agreement with actual radar observations, validating the applicability and robustness of the scattering model in the region. Retrieval relationships for D0 and Nw are established using the localised DSD dataset. Sensitivity analysis indicates that D0 is primarily influenced by ZDR, with a negligible bias of -0.005 mm, while Nw is influenced by both Z and ZDR and exhibits larger variability, with a mean bias of 0.029 m-3·mm-1. (3) Validation is carried out using six precipitation events observed at Changxing in 2023, comparing retrievals from two radars: Jiaxing (JX-SPOL) and Hangzhou (HZ-SPOL). The JX-SPOL retrievals of D0 show better agreement with LPM measurements, with a mean bias of 0.087 mm and RMSE of 0.418 mm, outperforming HZ-SPOL (bias -0.195 mm, RMSE 0.444 mm). For Nw, JX-SPOL yields a higher correlation coefficient (0.536) compared to HZ-SPOL (0.199), and the absolute deviation and RMSE (HZ-SPOL 1.046 m-3·mm-1 and JX-SPOL 0.819 m-3·mm-1) are relatively close, although systematic biases remain: JX-SPOL tends to underestimate by 0.395 m-3·mm-1, whereas HZ-SPOL overestimates by 0.494 m-3·mm-1. Overall, this study provides a scientific basis for improving dual-polarisation radar retrievals of precipitation microphysics in Zhejiang Province. The findings highlight the importance of localised DSD datasets in optimising retrieval algorithms and emphasise the need for further validation efforts using diverse precipitation events and multi-site observations to enhance the accuracy, stability, and generalisability of radar-based microphysical retrieval techniques.
This study systematically investigates the microphysical characteristics of warm-season precipitation over northern Zhejiang Province, China, based on comprehensive observations from six laser precipitation monitors (LPMs) and two S-band dual-polarisation weather radars (HZ-SPOL and JX-SPOL) collected between March and October 2022. In addition, the performance of dual-polarisation radar retrievals of raindrop size distribution (DSD) parameters, specifically the mass-weighted mean diameter (D0) and the normalised intercept parameter (Nw) of the gamma distribution, is quantitatively evaluated. The results reveal the following: (1) Precipitation in the northern Zhejiang region is predominantly characterised by high concentrations of small raindrops, with an average D0 of 1.0 mm and a mean Nw of 104.3m-3·mm-1, exhibiting typical features of warm-cloud precipitation. A classification approach based on the statistical properties (mean and standard deviation) of rainfall rate effectively discriminates between stratiform and convective precipitation. Stratiform precipitation shows relatively narrow DSDs and low spatial variability across sites, with low rainfall intensity (R<10 mm·h-1) and smaller drop sizes (D0 <1.0 mm). In contrast, convective precipitation exhibits significantly broader spectra, with D0 and average R values reaching up to 1.8 mm and 19 mm·h-1, respectively, and displays much greater spatial heterogeneity. Medium-sized raindrops (1.0-2.0 mm) contribute the largest fraction (33.4%) to total rainfall accumulation, while larger drops (D0 >2.5 mm), though relatively infrequent, still account for 10.3% of total rainfall volume. (2) Simulated polarimetric variables at S-band—reflectivity (Z), differential reflectivity (ZDR), and specific differential phase (Kdp)—calculated using a T-matrix scattering model based on local DSDs, show good agreement with actual radar observations, validating the applicability and robustness of the scattering model in the region. Retrieval relationships for D0 and Nw are established using the localised DSD dataset. Sensitivity analysis indicates that D0 is primarily influenced by ZDR, with a negligible bias of -0.005 mm, while Nw is influenced by both Z and ZDR and exhibits larger variability, with a mean bias of 0.029 m-3·mm-1. (3) Validation is carried out using six precipitation events observed at Changxing in 2023, comparing retrievals from two radars: Jiaxing (JX-SPOL) and Hangzhou (HZ-SPOL). The JX-SPOL retrievals of D0 show better agreement with LPM measurements, with a mean bias of 0.087 mm and RMSE of 0.418 mm, outperforming HZ-SPOL (bias -0.195 mm, RMSE 0.444 mm). For Nw, JX-SPOL yields a higher correlation coefficient (0.536) compared to HZ-SPOL (0.199), and the absolute deviation and RMSE (HZ-SPOL 1.046 m-3·mm-1 and JX-SPOL 0.819 m-3·mm-1) are relatively close, although systematic biases remain: JX-SPOL tends to underestimate by 0.395 m-3·mm-1, whereas HZ-SPOL overestimates by 0.494 m-3·mm-1. Overall, this study provides a scientific basis for improving dual-polarisation radar retrievals of precipitation microphysics in Zhejiang Province. The findings highlight the importance of localised DSD datasets in optimising retrieval algorithms and emphasise the need for further validation efforts using diverse precipitation events and multi-site observations to enhance the accuracy, stability, and generalisability of radar-based microphysical retrieval techniques.
2025, 53(5):738-751.
DOI: 10.19517/j.1671-6345.20240379
Abstract:
The wind field has a significant impact on the spread speed, fire distribution and operational safety of forest and grassland fires. This study aims to explore the application value and practical effect of Doppler wind laser radar at the rescue site of forest and grassland fires. Based on the observation data of Doppler wind laser radar at the “3·15” forest fire rescue site in Yajiang, Sichuan, in 2024, this paper first verifies the accuracy of the three-dimensional wind field inversion using the three-dimensional variational method under complex terrain conditions with the multi-elevation plane position indicator (PPI) data of the scanning wind laser radar. On this basis, the wind field information observed at three fire points in Yajiang by the PPI scanning mode of the wind laser radar is used to conduct a preliminary analysis of the spread of the fire and the horizontal diffusion of smoke. At the same time, the vertical wind profile of the fire scene is retrieved through the Doppler beam swinging (DBS) mode data, and the vertical atmospheric motion of the fire scene is analysed in combination with the extinction coefficient. The results show that the vertical wind field retrieved by the DBS mode is used as the background field of the three-dimensional variational method. The three-dimensional wind field retrieved under complex terrain conditions has high accuracy, and the average deviations of wind speed and wind direction are -2.51% and 3.92%, respectively. The wind direction in the fire determines the direction of fire spread and smoke propagation. The vertical wind profile of the fire scene retrieved through the DBS mode captures the low-altitude jet and momentum transfer phenomenon that may cause sudden changes in fire intensity, and the vertical transport of smoke in the fire can be further analysed in combination with the extinction coefficient. The application of Doppler wind laser radar in wind field monitoring at the scene of forest and grassland fires is of great significance for improving the scientific level of command and management at the rescue site.
The wind field has a significant impact on the spread speed, fire distribution and operational safety of forest and grassland fires. This study aims to explore the application value and practical effect of Doppler wind laser radar at the rescue site of forest and grassland fires. Based on the observation data of Doppler wind laser radar at the “3·15” forest fire rescue site in Yajiang, Sichuan, in 2024, this paper first verifies the accuracy of the three-dimensional wind field inversion using the three-dimensional variational method under complex terrain conditions with the multi-elevation plane position indicator (PPI) data of the scanning wind laser radar. On this basis, the wind field information observed at three fire points in Yajiang by the PPI scanning mode of the wind laser radar is used to conduct a preliminary analysis of the spread of the fire and the horizontal diffusion of smoke. At the same time, the vertical wind profile of the fire scene is retrieved through the Doppler beam swinging (DBS) mode data, and the vertical atmospheric motion of the fire scene is analysed in combination with the extinction coefficient. The results show that the vertical wind field retrieved by the DBS mode is used as the background field of the three-dimensional variational method. The three-dimensional wind field retrieved under complex terrain conditions has high accuracy, and the average deviations of wind speed and wind direction are -2.51% and 3.92%, respectively. The wind direction in the fire determines the direction of fire spread and smoke propagation. The vertical wind profile of the fire scene retrieved through the DBS mode captures the low-altitude jet and momentum transfer phenomenon that may cause sudden changes in fire intensity, and the vertical transport of smoke in the fire can be further analysed in combination with the extinction coefficient. The application of Doppler wind laser radar in wind field monitoring at the scene of forest and grassland fires is of great significance for improving the scientific level of command and management at the rescue site.
2025, 53(5):752-760.
DOI: 10.19517/j.1671-6345.20250010
Abstract:
Foehn wind is a special meteorological phenomenon that occurs on the leeward side of mountains, often leading to a sudden increase in temperature, a decrease in water vapour, and an increase in wind speed. The zenith total delay (ZTD) of Global Navigation Satellite System (GNSS) signals can be used to retrieve atmospheric precipitable water vapour (PWV), which has significant application value in the field of meteorology. However, there is a lack of GNSS data analysis research focused on foehn wind processes in our country. This study analyses a typical foehn wind event based on automatic weather station (AWS) and GNSS observation data from Beijing and surrounding provinces and cities, examining the changes in meteorological elements during this process and the characteristics of GNSS water vapour. During the passage of the foehn wind, significant changes in meteorological elements were observed at the plain stations, with the influence of the foehn wind weakening as the distance from the mountainous area increased. In terms of GNSS water vapour characteristics, the passage of the foehn wind resulted in a reduction of water vapour, with changes in PWV being more pronounced than those in ZTD. Using the PWV/ZTD difference from stations at different elevation differences better reflected the characteristics of water vapour reduction caused by the foehn wind. This study suggests that the foehn wind had a significant impact on the meteorological elements and water vapour distribution in the Beijing plain area, and that GNSS data could effectively monitor the changes in water vapour during the foehn wind process. This research provides a reference for using GNSS data to conduct monitoring and related studies on foehn winds.
Foehn wind is a special meteorological phenomenon that occurs on the leeward side of mountains, often leading to a sudden increase in temperature, a decrease in water vapour, and an increase in wind speed. The zenith total delay (ZTD) of Global Navigation Satellite System (GNSS) signals can be used to retrieve atmospheric precipitable water vapour (PWV), which has significant application value in the field of meteorology. However, there is a lack of GNSS data analysis research focused on foehn wind processes in our country. This study analyses a typical foehn wind event based on automatic weather station (AWS) and GNSS observation data from Beijing and surrounding provinces and cities, examining the changes in meteorological elements during this process and the characteristics of GNSS water vapour. During the passage of the foehn wind, significant changes in meteorological elements were observed at the plain stations, with the influence of the foehn wind weakening as the distance from the mountainous area increased. In terms of GNSS water vapour characteristics, the passage of the foehn wind resulted in a reduction of water vapour, with changes in PWV being more pronounced than those in ZTD. Using the PWV/ZTD difference from stations at different elevation differences better reflected the characteristics of water vapour reduction caused by the foehn wind. This study suggests that the foehn wind had a significant impact on the meteorological elements and water vapour distribution in the Beijing plain area, and that GNSS data could effectively monitor the changes in water vapour during the foehn wind process. This research provides a reference for using GNSS data to conduct monitoring and related studies on foehn winds.
2025, 53(5):761-773.
DOI: 10.19517/j.1671-6345.20240280
Abstract:
By integrating hail data collected from hail prevention sites, meteorological stations, and the China Meteorological Administration’s meteorological disaster management system, the spatiotemporal distribution characteristics of hail in Yunnan from 2012 to 2023 are statistically analysed, and the differences between them and the observation results of meteorological stations are explored to provide a basis for hail monitoring, forecasting, and scientific hail suppression. The results show that the annual average number of hail days and county days in Yunnan is 100.5 days and 324.0, with significant interannual differences and an increasing trend, with a yearly increase of 1.1 days and 6.3 county days. The seasonal characteristics of the two are prominent, with the highest values in summer, followed by spring, and significantly reduced in autumn and winter. The corresponding values are 46, 34.1, 10.8, and 9.6 days and 166.2, 110.4, 16.3, and 31.1 county days, respectively. The monthly variation can be classified into four types. The predominant pattern is dual-peak, with a primary peak in July-August and a secondary peak in March-April. The second pattern is single-peak in July-August for hail days but in March-April for hail county days, and the other patterns are single-peak in March-April for hail days but in July-August for hail county days, and both multi-peak. On average over the years, both exhibit a typical dual-peak pattern with a primary peak in July-August and a secondary peak in March-April. The daily variation of hail shows a single-peak pattern at 16:00, with hail concentrating between 13:00 and 19:00, particularly in summer and autumn. The horizontal spatial distribution of hail days is categorised into four types: scattered hail type, two band-shaped hail type, multiple hail type to the east and north of central Yunnan, and multiple hail type in southern and western Yunnan. On average over the years, the maximum number of hail days is from Qujing to Zhaotong (up to 11.8 days in Xuanwei), followed by the area from Yuxi to northern Honghe, then from Dali to Lijiang, and in Baoshan. However, there are significant differences in the seasonal and monthly horizontal distribution, with hail occurring in the western and southern regions during winter and spring, developing eastward from late spring to early summer, expanding towards the central region in summer, and retreating towards the west and south in autumn. On the vertical distribution, the number of hail days increases first and then decreases with altitude, with the most hails occurring at altitudes of 1.5-2.0 km. The number of hail days at low altitudes (0.0-1.0 km) is a single-peak pattern in April, a dual-peak pattern in April and July at mid-altitudes (1.0-2.0 km), and a single-peak pattern during July-August at high altitudes (≥2.0 km). At mid- and low altitudes, hail mainly occurs in spring, followed by summer, and least in autumn, and at mid- and high altitudes, hail mainly occurs in summer, followed by spring, and least in winter.
By integrating hail data collected from hail prevention sites, meteorological stations, and the China Meteorological Administration’s meteorological disaster management system, the spatiotemporal distribution characteristics of hail in Yunnan from 2012 to 2023 are statistically analysed, and the differences between them and the observation results of meteorological stations are explored to provide a basis for hail monitoring, forecasting, and scientific hail suppression. The results show that the annual average number of hail days and county days in Yunnan is 100.5 days and 324.0, with significant interannual differences and an increasing trend, with a yearly increase of 1.1 days and 6.3 county days. The seasonal characteristics of the two are prominent, with the highest values in summer, followed by spring, and significantly reduced in autumn and winter. The corresponding values are 46, 34.1, 10.8, and 9.6 days and 166.2, 110.4, 16.3, and 31.1 county days, respectively. The monthly variation can be classified into four types. The predominant pattern is dual-peak, with a primary peak in July-August and a secondary peak in March-April. The second pattern is single-peak in July-August for hail days but in March-April for hail county days, and the other patterns are single-peak in March-April for hail days but in July-August for hail county days, and both multi-peak. On average over the years, both exhibit a typical dual-peak pattern with a primary peak in July-August and a secondary peak in March-April. The daily variation of hail shows a single-peak pattern at 16:00, with hail concentrating between 13:00 and 19:00, particularly in summer and autumn. The horizontal spatial distribution of hail days is categorised into four types: scattered hail type, two band-shaped hail type, multiple hail type to the east and north of central Yunnan, and multiple hail type in southern and western Yunnan. On average over the years, the maximum number of hail days is from Qujing to Zhaotong (up to 11.8 days in Xuanwei), followed by the area from Yuxi to northern Honghe, then from Dali to Lijiang, and in Baoshan. However, there are significant differences in the seasonal and monthly horizontal distribution, with hail occurring in the western and southern regions during winter and spring, developing eastward from late spring to early summer, expanding towards the central region in summer, and retreating towards the west and south in autumn. On the vertical distribution, the number of hail days increases first and then decreases with altitude, with the most hails occurring at altitudes of 1.5-2.0 km. The number of hail days at low altitudes (0.0-1.0 km) is a single-peak pattern in April, a dual-peak pattern in April and July at mid-altitudes (1.0-2.0 km), and a single-peak pattern during July-August at high altitudes (≥2.0 km). At mid- and low altitudes, hail mainly occurs in spring, followed by summer, and least in autumn, and at mid- and high altitudes, hail mainly occurs in summer, followed by spring, and least in winter.
2025, 53(5):774-782.
DOI: 10.19517/j.1671-6345.20250058
Abstract:
Transportation is a fundamental and service-oriented industry of the national economy, which is significantly influenced by weather and climate factors. Implementing traffic meteorological services effectively improves people’s quality of life and socio-economic benefits. In this study, based on the traffic congestion index and road automatic 〖JP2〗weather station observations from 15 November, 2022, to 14 November,〖JP〗 2024, in Beijing, the temporal characteristics of the traffic congestion index and its meteorological impact are investigated. Firstly, the concept of the meteorological congestion index is proposed, and then the traffic congestion index is decomposed to analyse the distribution characteristics of the total congestion index, basic congestion index, and meteorological congestion index. Subsequently, several major adverse meteorological conditions are discussed for their potential impact on traffic congestion. The results show that the annual average traffic congestion index during the morning and evening peak hours in the Beijing urban area is 1.69 and 1.99, respectively. The maximum congestion index during the morning peak hours of the week occurs on Monday, and that during the evening peak hours occurs on Friday. The adverse meteorological conditions that affect transportation during the morning and evening peak hours account for 32.9% and 26.4% of the total sample, respectively; moreover, the proportion of the meteorological congestion index caused by meteorological conditions during the morning and evening peak hours is 17.2% and 11.1%, respectively, with a maximum of 57.2%. This indicates that the impact of meteorological factors on transportation is greater during the morning peak hours than during the evening peak hours. Additionally, the meteorological congestion index presents seasonal differences, with the maximum value occurring in autumn. It is also found that there is a complex synergistic effect between meteorological factors and traffic congestion. The impact on urban traffic congestion is significant under the following conditions: light or heavy rain, road surface temperature close to freezing or below -10 ℃, wind speed above 10 m/s, and visibility below 1.5 km. Furthermore, the results of variance analysis show that low visibility has the highest contribution rate to the meteorological congestion index, followed by precipitation events, road surface temperature, and wind speed, with contribution rates of 27.6%, 11.7%, 7.2%, and 4.6%, respectively. The research results not only provide a basis for government departments to formulate effective traffic management measures and emergency plans, but also provide a reference for guiding residents to choose reasonable transportation modes.
Transportation is a fundamental and service-oriented industry of the national economy, which is significantly influenced by weather and climate factors. Implementing traffic meteorological services effectively improves people’s quality of life and socio-economic benefits. In this study, based on the traffic congestion index and road automatic 〖JP2〗weather station observations from 15 November, 2022, to 14 November,〖JP〗 2024, in Beijing, the temporal characteristics of the traffic congestion index and its meteorological impact are investigated. Firstly, the concept of the meteorological congestion index is proposed, and then the traffic congestion index is decomposed to analyse the distribution characteristics of the total congestion index, basic congestion index, and meteorological congestion index. Subsequently, several major adverse meteorological conditions are discussed for their potential impact on traffic congestion. The results show that the annual average traffic congestion index during the morning and evening peak hours in the Beijing urban area is 1.69 and 1.99, respectively. The maximum congestion index during the morning peak hours of the week occurs on Monday, and that during the evening peak hours occurs on Friday. The adverse meteorological conditions that affect transportation during the morning and evening peak hours account for 32.9% and 26.4% of the total sample, respectively; moreover, the proportion of the meteorological congestion index caused by meteorological conditions during the morning and evening peak hours is 17.2% and 11.1%, respectively, with a maximum of 57.2%. This indicates that the impact of meteorological factors on transportation is greater during the morning peak hours than during the evening peak hours. Additionally, the meteorological congestion index presents seasonal differences, with the maximum value occurring in autumn. It is also found that there is a complex synergistic effect between meteorological factors and traffic congestion. The impact on urban traffic congestion is significant under the following conditions: light or heavy rain, road surface temperature close to freezing or below -10 ℃, wind speed above 10 m/s, and visibility below 1.5 km. Furthermore, the results of variance analysis show that low visibility has the highest contribution rate to the meteorological congestion index, followed by precipitation events, road surface temperature, and wind speed, with contribution rates of 27.6%, 11.7%, 7.2%, and 4.6%, respectively. The research results not only provide a basis for government departments to formulate effective traffic management measures and emergency plans, but also provide a reference for guiding residents to choose reasonable transportation modes.
External LinksChina Meteorological AdministrationCMA Meteorological Observation CentreChinese Academy of Meteorological SciencesBeijing Meteorological ServiceNational Satellite Meteorological Center
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.