ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 50,Issue 1,2024 Table of Contents
2024, 50(1):1-17.
DOI: 10.7519/j.issn.1000-0526.2023.092601
Abstract:
There is no 1 km spatial resolution all-weather land surface temperature (LST) product based on domestic satellites in China. FY-3D satellite provides the clear sky LST products with 1 km resolution from the medium resolution spectral imager (MERSI) Ⅱ and the all-weather LST products with 25 km resolution from the microwave radiation imager (MWRI). Therefore, the integration research of all-weather 1 km resolution LST can be carried out by combining their advantages. By using geographical weighted regression (GWR) method, this study selects altitude, FY-3D normalized difference vegetation index and normalized difference building index to establish LST downscaling regression model in order to downscale FY-3D/MWRI 25 km LST to 1 km, and integrates them with MERSI 1 km LST. For the MWRI track gaps, 1 km LST of cloud covered pixels fused in the previous one day and the next day can be used to supplement, which is close to the all-weather 1 km LST. Based on the above fusion algorithms, multiple Chinese FY-3D/MERSI and MWRI LST products on typical dates from the official website are selected for fusion test, and the existing all-weather 1 km LST products (TPDC LST) were used to evaluate the results of FY-3D 1 km LST fusion products. The results show that the LST downscaling method based on GWR method can effectively eliminate the “patches” effect and low local temperature in traditional microwave downscaling methods based on the combination of altitude with mixed pixel decomposition. The rate of FY-3D 1 km LST can be increased from 22.4%-36.9% before fusion to 69.3%-80.7% after fusion. The spatial correlation between the fusion product and TPDC LST is 0.503-0.787, and the RMSE is 3.6-5.8 K with 2.6-4.9 K in clear sky and 4.1-6.1 K in cloudy sky. The analysis also shows that the current FY-3D/MERSI LST and MWRI LST products from the official website have problems such as obvious lack of value and low accuracy, suggesting that they have a great potential to be improved. This is conducive to further improving the quality of FY-3D LST fusion.
There is no 1 km spatial resolution all-weather land surface temperature (LST) product based on domestic satellites in China. FY-3D satellite provides the clear sky LST products with 1 km resolution from the medium resolution spectral imager (MERSI) Ⅱ and the all-weather LST products with 25 km resolution from the microwave radiation imager (MWRI). Therefore, the integration research of all-weather 1 km resolution LST can be carried out by combining their advantages. By using geographical weighted regression (GWR) method, this study selects altitude, FY-3D normalized difference vegetation index and normalized difference building index to establish LST downscaling regression model in order to downscale FY-3D/MWRI 25 km LST to 1 km, and integrates them with MERSI 1 km LST. For the MWRI track gaps, 1 km LST of cloud covered pixels fused in the previous one day and the next day can be used to supplement, which is close to the all-weather 1 km LST. Based on the above fusion algorithms, multiple Chinese FY-3D/MERSI and MWRI LST products on typical dates from the official website are selected for fusion test, and the existing all-weather 1 km LST products (TPDC LST) were used to evaluate the results of FY-3D 1 km LST fusion products. The results show that the LST downscaling method based on GWR method can effectively eliminate the “patches” effect and low local temperature in traditional microwave downscaling methods based on the combination of altitude with mixed pixel decomposition. The rate of FY-3D 1 km LST can be increased from 22.4%-36.9% before fusion to 69.3%-80.7% after fusion. The spatial correlation between the fusion product and TPDC LST is 0.503-0.787, and the RMSE is 3.6-5.8 K with 2.6-4.9 K in clear sky and 4.1-6.1 K in cloudy sky. The analysis also shows that the current FY-3D/MERSI LST and MWRI LST products from the official website have problems such as obvious lack of value and low accuracy, suggesting that they have a great potential to be improved. This is conducive to further improving the quality of FY-3D LST fusion.
2024, 50(1):18-32.
DOI: 10.7519/j.issn.1000-0526.2023.091401
Abstract:
The structure characteristics and thermodynamic mechanism of the extreme snowstorm in the Northeast and North China during 6-8 November 2021 are analyzed with multiple observations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The results show that this process was successively affected by the upper transversal trough, the westerly trough in Hetao Plain and the upper cold vortex at 500 hPa. The high-level jet over the influence system was gradually strengthened, featuring an “S” curve. Meanwhile, the southern low-level jet was formed, strengthened, and coupled with the high-level jet in Northeast China. The influence systems of this process emanated distinct phased characteristics with significant differences in the structure characteristics and water vapor transport. The cold surface area formed by the return-flow cold front was relatively shallow, and the warm moisture flow was rising obliquely along it. The cold front was relatively steep with a westward tilting updraft, while the ground cyclone front was deep and vertical, making the air flow ascend vertically. With the enhancement of baroclinic forcing, the 850 hPa shear line turned to north-south distribution from quasi-east-west direction and then evolved into a low vortex shear structure. Correspondingly, the horizontal vorticity changed from weak to strong, and the overlying vertical distribution of positive vorticity gradually strengthened, featured with a weak slantwise updraft evolving into a strong vertical ascending zone and a secondary circulation sinking branch forming on the east of the system. Frontogenesis fostered this extreme snowstorm. The pseudo-equivalent potential temperature front area gradually strengthened in the three stages of snowfall, and the vertical front area developed from an inclined state into a nearly vertical structure. The snowfall area was consistent with the evolution of the front area and the snowfall intensity was proportional to the magnitude of frontogenesis function. The diagnosis of the moist potential vorticity shows that all the snowfall areas in the three stages occur in the configuration region where the barotropic (baroclinic) term is greater (less) than 0. Conditional symmetric instability is the main dynamic mechanism of this extreme snowstorm.
The structure characteristics and thermodynamic mechanism of the extreme snowstorm in the Northeast and North China during 6-8 November 2021 are analyzed with multiple observations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The results show that this process was successively affected by the upper transversal trough, the westerly trough in Hetao Plain and the upper cold vortex at 500 hPa. The high-level jet over the influence system was gradually strengthened, featuring an “S” curve. Meanwhile, the southern low-level jet was formed, strengthened, and coupled with the high-level jet in Northeast China. The influence systems of this process emanated distinct phased characteristics with significant differences in the structure characteristics and water vapor transport. The cold surface area formed by the return-flow cold front was relatively shallow, and the warm moisture flow was rising obliquely along it. The cold front was relatively steep with a westward tilting updraft, while the ground cyclone front was deep and vertical, making the air flow ascend vertically. With the enhancement of baroclinic forcing, the 850 hPa shear line turned to north-south distribution from quasi-east-west direction and then evolved into a low vortex shear structure. Correspondingly, the horizontal vorticity changed from weak to strong, and the overlying vertical distribution of positive vorticity gradually strengthened, featured with a weak slantwise updraft evolving into a strong vertical ascending zone and a secondary circulation sinking branch forming on the east of the system. Frontogenesis fostered this extreme snowstorm. The pseudo-equivalent potential temperature front area gradually strengthened in the three stages of snowfall, and the vertical front area developed from an inclined state into a nearly vertical structure. The snowfall area was consistent with the evolution of the front area and the snowfall intensity was proportional to the magnitude of frontogenesis function. The diagnosis of the moist potential vorticity shows that all the snowfall areas in the three stages occur in the configuration region where the barotropic (baroclinic) term is greater (less) than 0. Conditional symmetric instability is the main dynamic mechanism of this extreme snowstorm.
2024, 50(1):33-47.
DOI: 10.7519/j.issn.1000-0526.2023.062101
Abstract:
In order to reveal the deviation characteristics of severe convective storm and mesoscale convective system (MCS, in satellite infrared channel), we employ the conventional observation and the unconventional observation (radar and satellite data) to analyze the simulation performance of CMA-MESO model in simulating the extremely severe rainstorm that occurred in Henan Province from 19 to 21 July 2021 by using traditional and new spatial verified methods. Besides, we also investigate the causes for the model deviations from the perspectives of water vapor, momentum, trigger and maintenance mechanism of precipitation. The results show that, firstly, the model can well capture the shape of rain band and duration of weak echoes as well as the evolution trend of intensity and area of MCS in the early and late stages of the primary precipitation process. Secondly, the model deviations mainly lie in that the model underestimated the intensity of precipitation, failing to predict the extreme hourly precipitation at Zhengzhou Station and the evolution trend of hourly precipitation of main rain band. Besides, it seriously underestimated the durations of convective storms and severe convective storms. Furthermore, the model missed the sharply increasing of MCS area in the afternoon and the simulated MCS was displaced westward and northward relative to the observation. Thirdly, the model deviation was mainly owing to the incorrect simulation of water vapor. The vertical distribution of simulated water vapor was not reasonable, and the simulated water vapor transports from Typhoon In-Fa and Typhoon Cempaka were both weak. In fact, the weak low-level jet and insufficient ultra low-level easterly jet pulsation directly led to the insufficient transport of simulated water vapor. Moreover, the model’s failure in forecasting the stably maintaining mesoscale convergence line on the surface near Zhengzhou Station, in conjunction with the deficiency of atmospheric instability and underestimation of unstable convective available potential energy, made the development of the simulated convection not strong enough and finally caused the inadequate simulated precipitation.
In order to reveal the deviation characteristics of severe convective storm and mesoscale convective system (MCS, in satellite infrared channel), we employ the conventional observation and the unconventional observation (radar and satellite data) to analyze the simulation performance of CMA-MESO model in simulating the extremely severe rainstorm that occurred in Henan Province from 19 to 21 July 2021 by using traditional and new spatial verified methods. Besides, we also investigate the causes for the model deviations from the perspectives of water vapor, momentum, trigger and maintenance mechanism of precipitation. The results show that, firstly, the model can well capture the shape of rain band and duration of weak echoes as well as the evolution trend of intensity and area of MCS in the early and late stages of the primary precipitation process. Secondly, the model deviations mainly lie in that the model underestimated the intensity of precipitation, failing to predict the extreme hourly precipitation at Zhengzhou Station and the evolution trend of hourly precipitation of main rain band. Besides, it seriously underestimated the durations of convective storms and severe convective storms. Furthermore, the model missed the sharply increasing of MCS area in the afternoon and the simulated MCS was displaced westward and northward relative to the observation. Thirdly, the model deviation was mainly owing to the incorrect simulation of water vapor. The vertical distribution of simulated water vapor was not reasonable, and the simulated water vapor transports from Typhoon In-Fa and Typhoon Cempaka were both weak. In fact, the weak low-level jet and insufficient ultra low-level easterly jet pulsation directly led to the insufficient transport of simulated water vapor. Moreover, the model’s failure in forecasting the stably maintaining mesoscale convergence line on the surface near Zhengzhou Station, in conjunction with the deficiency of atmospheric instability and underestimation of unstable convective available potential energy, made the development of the simulated convection not strong enough and finally caused the inadequate simulated precipitation.
2024, 50(1):48-58.
DOI: 10.7519/j.issn.1000-0526.2023.073001
Abstract:
In order to improve the accuracy of short-time severe rainfall forecasts, the LightGBM algorithm is applied to build the hourly precipitation forecasting model based on the precipitation observation data and CMA-GD model forecast products of Fujian Province from April to September in 2019 and 2020. Correction models are optimized by the feature processing, Bagging (bootstrap aggregating) and hyperparameter searching. Combined with AUC, AUPR and traditional classification indices, a series of experiments are designed to evaluate different modeling schemes and verify the applicability in short-time severe rainfall forecasting. The results show that all modeling schemes can improve the original numerical model forecast representing the high POD and FAR in varying degrees. Bagging can enhance the stability of model prediction, and the slightly unbalanced sub-training set contributes to the higher TS scores by reducing the FAR with the best TS score of validation set being about 17.5%. The largest contribution of feature variable to the classification information gain is K index, followed by 500 hPa dew point and time parameters. The ranking of experiment indices in good to bad order is random cross-validation, random hourly cross-validation and operational simulation test which indicates that the validity of correction models mainly result from the sample information at the same or adjacent moments. The dynamic fusion scheme of heterogeneous models based on logistic regression increases indices of static homogeneous models, which decreases at least 520 〖KG-*5〗000 false alarm samples with approximately 50% POD.
In order to improve the accuracy of short-time severe rainfall forecasts, the LightGBM algorithm is applied to build the hourly precipitation forecasting model based on the precipitation observation data and CMA-GD model forecast products of Fujian Province from April to September in 2019 and 2020. Correction models are optimized by the feature processing, Bagging (bootstrap aggregating) and hyperparameter searching. Combined with AUC, AUPR and traditional classification indices, a series of experiments are designed to evaluate different modeling schemes and verify the applicability in short-time severe rainfall forecasting. The results show that all modeling schemes can improve the original numerical model forecast representing the high POD and FAR in varying degrees. Bagging can enhance the stability of model prediction, and the slightly unbalanced sub-training set contributes to the higher TS scores by reducing the FAR with the best TS score of validation set being about 17.5%. The largest contribution of feature variable to the classification information gain is K index, followed by 500 hPa dew point and time parameters. The ranking of experiment indices in good to bad order is random cross-validation, random hourly cross-validation and operational simulation test which indicates that the validity of correction models mainly result from the sample information at the same or adjacent moments. The dynamic fusion scheme of heterogeneous models based on logistic regression increases indices of static homogeneous models, which decreases at least 520 〖KG-*5〗000 false alarm samples with approximately 50% POD.
2024, 50(1):59-70.
DOI: 10.7519/j.issn.1000-0526.2023.092101
Abstract:
By using the L-band radiosonde data of Shanghai Baoshan Station during the winter from 2019 to 2021, the FY-4A cloud top temperature (CTT) product is evaluated. The results indicate that the CTT of the single-layer cloud can be reflected well by the FY-4A CTT product, while the CTT of multi-layer clouds is generally overestimated with the mean bias larger than 14℃. And the FY-4A CTT product has a small mean bias of approximately 3℃ compared to the sounding observation when the observed cloud top height is below 6 km or the observed cloud top temperature is higher than -20℃. Moreover, based on the surface observation in the Yangtze River Delta Region in the winter of 2021 and 2022, the relationship between CTT and precipitation types in winter seasons is statistically analyzed and further verified by typical cases. The results reveal that a low CTT is one of the main necessary conditions for snowfall, as most snowfall events occur when the CTT is below -12℃. As the FY-4A CTT product has a certain accuracy with a high spatial and temporal resolution, this product could have great application potential in iden-tifying precipitation types.
By using the L-band radiosonde data of Shanghai Baoshan Station during the winter from 2019 to 2021, the FY-4A cloud top temperature (CTT) product is evaluated. The results indicate that the CTT of the single-layer cloud can be reflected well by the FY-4A CTT product, while the CTT of multi-layer clouds is generally overestimated with the mean bias larger than 14℃. And the FY-4A CTT product has a small mean bias of approximately 3℃ compared to the sounding observation when the observed cloud top height is below 6 km or the observed cloud top temperature is higher than -20℃. Moreover, based on the surface observation in the Yangtze River Delta Region in the winter of 2021 and 2022, the relationship between CTT and precipitation types in winter seasons is statistically analyzed and further verified by typical cases. The results reveal that a low CTT is one of the main necessary conditions for snowfall, as most snowfall events occur when the CTT is below -12℃. As the FY-4A CTT product has a certain accuracy with a high spatial and temporal resolution, this product could have great application potential in iden-tifying precipitation types.
2024, 50(1):71-83.
DOI: 10.7519/j.issn.1000-0526.2023.091201
Abstract:
Using the conventional sounding observation data, minutely automatic weather station (AWS) data, wind profile radar data, and Doppler weather radar, this paper analyzes the weather characteristics of a regional extreme gale in the northern Zhejiang and Hangzhou Bay coastal areas from the evening to the night of 30 April 2021, focusing on the evolution and mesoscale features of the severe squall line system after its moving into Hangzhou Bay. The analysis results show that this severe squall line system developed under a typical multi-scale interaction background of anomalously deep northeast cold vortex at relatively high altitude. The combination of the mid-level northwest jet and surface warm low pressure induced an enhancement of the local convection storms behind the squall line, resulting in thunderstorm and gale. After the convection cell passed through the Hangzhou Bay, its intensity was enhanced significantly. The warm-moist southwest air flew into the front of the gust front, the cold pool developed stronger in the rear of the gust front, barometric pressure surged with the land surface 〖JP2〗environmental wind field and the thermo-〖JP〗dynamic conditions of water surface of Hangzhou Bay. All these factors triggered unstable energy and enhanced the development of cells. The intensity of subsidence divergence outflow developed strongly after the cell’s crossing the Hangzhou Bay, and it conveyed the momentum in the mid-high layers to the ground rapidly, which generated the enhancement effect of extreme winds in the south of Hangzhou Bay significantly. The lower friction on the water surface of Hangzhou Bay and the special topography of bellmouth were the important causes for the occurrence of extreme gale. At the same time, minute-by-minute temperature change was about 7-10 minutes earlier than the occurrence time of extreme gale. This finding has certain directive significance for local extreme wind monitoring and warning.
Using the conventional sounding observation data, minutely automatic weather station (AWS) data, wind profile radar data, and Doppler weather radar, this paper analyzes the weather characteristics of a regional extreme gale in the northern Zhejiang and Hangzhou Bay coastal areas from the evening to the night of 30 April 2021, focusing on the evolution and mesoscale features of the severe squall line system after its moving into Hangzhou Bay. The analysis results show that this severe squall line system developed under a typical multi-scale interaction background of anomalously deep northeast cold vortex at relatively high altitude. The combination of the mid-level northwest jet and surface warm low pressure induced an enhancement of the local convection storms behind the squall line, resulting in thunderstorm and gale. After the convection cell passed through the Hangzhou Bay, its intensity was enhanced significantly. The warm-moist southwest air flew into the front of the gust front, the cold pool developed stronger in the rear of the gust front, barometric pressure surged with the land surface 〖JP2〗environmental wind field and the thermo-〖JP〗dynamic conditions of water surface of Hangzhou Bay. All these factors triggered unstable energy and enhanced the development of cells. The intensity of subsidence divergence outflow developed strongly after the cell’s crossing the Hangzhou Bay, and it conveyed the momentum in the mid-high layers to the ground rapidly, which generated the enhancement effect of extreme winds in the south of Hangzhou Bay significantly. The lower friction on the water surface of Hangzhou Bay and the special topography of bellmouth were the important causes for the occurrence of extreme gale. At the same time, minute-by-minute temperature change was about 7-10 minutes earlier than the occurrence time of extreme gale. This finding has certain directive significance for local extreme wind monitoring and warning.
2024, 50(1):84-94.
DOI: 10.7519/j.issn.1000-0526.2023.050802
Abstract:
Temporal and spatial variations of haze days in four seasons and dominant factors are analyzed based on meteorological data from 51 surface weather stations in Liaoning Province between 1961 and 2020. The results show that the annual haze days averaged in Liaoning Province have a significant increasing trend of 2.1 d per decade from 1961 to 2020 and a decreasing from 2015 to 2020. In terms of spatial distribution, there is a highvalue center (Shenyang) and two subhighvalue centers (Jinzhou and Beipiao), where the annual haze days are respectively 139 d, 52 d and 46 d. Comparatively, the annual haze days in the mountain areas of western and eastern in Liaoning Province are the least, less than 20 days on average. Wind direction and wind speed are important meteorological factors for the formation of haze. The warm and humid air brought by the increase of southsouthwesterly winds has a greater contribution to haze formation in spring, summer and autumn, while the decrease of northerly winds helps the formation of haze days in winter. The frequencies of southsouthwest winds in spring, summer and autumn have increased from 11.4%, 12.1% and 8.0% to 15.8%, 19.8% and 13.5%, respectively. When haze occurs, the wind speeds in all four seasons are lower than the average, indicating that light wind is conducive to the formation of haze. The longterm evolution of haze in Liaoning Province is affected by pollutant emissions, wind factors and environmentmanage policies. The growth of haze days from 1980 to 2003 was caused by the increased pollutants and decreased wind speeds. The reduction of haze days since 2015 is very likely related to the reduction of PM2.5, which is achieved by air pollution control. At the same time, fewer light wind days also provide a favorable meteorological condition for the reduction of haze days.
Temporal and spatial variations of haze days in four seasons and dominant factors are analyzed based on meteorological data from 51 surface weather stations in Liaoning Province between 1961 and 2020. The results show that the annual haze days averaged in Liaoning Province have a significant increasing trend of 2.1 d per decade from 1961 to 2020 and a decreasing from 2015 to 2020. In terms of spatial distribution, there is a highvalue center (Shenyang) and two subhighvalue centers (Jinzhou and Beipiao), where the annual haze days are respectively 139 d, 52 d and 46 d. Comparatively, the annual haze days in the mountain areas of western and eastern in Liaoning Province are the least, less than 20 days on average. Wind direction and wind speed are important meteorological factors for the formation of haze. The warm and humid air brought by the increase of southsouthwesterly winds has a greater contribution to haze formation in spring, summer and autumn, while the decrease of northerly winds helps the formation of haze days in winter. The frequencies of southsouthwest winds in spring, summer and autumn have increased from 11.4%, 12.1% and 8.0% to 15.8%, 19.8% and 13.5%, respectively. When haze occurs, the wind speeds in all four seasons are lower than the average, indicating that light wind is conducive to the formation of haze. The longterm evolution of haze in Liaoning Province is affected by pollutant emissions, wind factors and environmentmanage policies. The growth of haze days from 1980 to 2003 was caused by the increased pollutants and decreased wind speeds. The reduction of haze days since 2015 is very likely related to the reduction of PM2.5, which is achieved by air pollution control. At the same time, fewer light wind days also provide a favorable meteorological condition for the reduction of haze days.
2024, 50(1):95-102.
DOI: 10.7519/j.issn.1000-0526.2023.050603
Abstract:
Based on the 15-min power load of Shijiazhuang City from 2017 to 2020 and the meteorological data of the same period, we calculate the effective temperature and the temperature-humidity index of human comfort index. Considering the periodicity and growth of the benchmark load, we adopt the grey model GM (1, 1), and at the sametime, combining filtering method and correlation analysis method, we establish a piecewise regression model of daily peak cooling load and human body comfort index. The results show that the power load in Shijiazhuang has an obvious year-on-year growth trend. The stripped-off daily cooling load curve shows a “W”-shaped distribution. The models are fitted with primary, secondary and piecewise functions respectively. The three prediction models are tested， and the fixdings suggest that the prediction accuracy of the piecewise function is relatively high, the average relative error is between 4.8% and 5.2%, and the piecewise function error between the effective temperature and the temperature-humidity index is between -10% and 10%, and the proportions are 88.1% and 90.5%, respectively. The effective temperature ratio considering temperature, humidity and wind speed has a higher forecast accuracy than the summer daily peak cooling load forecasting model which is based on the temperature-humidity index. The regression model piecewise point is 26.2℃, which has better reference value for power dispatch during the “peak summer” period.
Based on the 15-min power load of Shijiazhuang City from 2017 to 2020 and the meteorological data of the same period, we calculate the effective temperature and the temperature-humidity index of human comfort index. Considering the periodicity and growth of the benchmark load, we adopt the grey model GM (1, 1), and at the sametime, combining filtering method and correlation analysis method, we establish a piecewise regression model of daily peak cooling load and human body comfort index. The results show that the power load in Shijiazhuang has an obvious year-on-year growth trend. The stripped-off daily cooling load curve shows a “W”-shaped distribution. The models are fitted with primary, secondary and piecewise functions respectively. The three prediction models are tested， and the fixdings suggest that the prediction accuracy of the piecewise function is relatively high, the average relative error is between 4.8% and 5.2%, and the piecewise function error between the effective temperature and the temperature-humidity index is between -10% and 10%, and the proportions are 88.1% and 90.5%, respectively. The effective temperature ratio considering temperature, humidity and wind speed has a higher forecast accuracy than the summer daily peak cooling load forecasting model which is based on the temperature-humidity index. The regression model piecewise point is 26.2℃, which has better reference value for power dispatch during the “peak summer” period.
2024, 50(1):103-114.
DOI: 10.7519/j.issn.1000-0526.2023.111701
Abstract:
Using the smart grid reality analysis product (CLDAS-V2.0) and the European Centre for 〖JP〗Medium-Range Weather Forecast Global Model (EC) product, which were operated at the national level from September 2017 to March 2021, six regions are constructed according to the geographical distribution characteristics of Hubei Province. The temperature forecast model established by LightGBM machine learning algorithm is used to generate 0.05°×0.05° gridded temperature forecast products in Hubei Province, and the forecast products are verified by the forecast products and grid data from April to September 2021. The results show that the temperature prediction method (MLT) based on machine learning has achieved a good forecast effect, and MLT is superior to SCMOC and EC model products in 0-72 h lead time. The error of MLT in mountain area is larger than that in plain area, but the correction amplitude of MLT in mountain area is larger than that in plain area, and the correction amplitude of daily maximum temperature is larger than that of daily minimum temperature. The diurnal variations of MAE in MLT, SCMOC and EC model products from April to September present the characteristics of higher in daytime, lower over night, and convex single-peak in afternoon. The MAE value of MLT is lower than that of SCMOC and EC model products, and still has an advantage in changeable weather. The results of site test and grid test are consistent, and the temperature forecast product based on grid modeling is also corrected. Machine learning can be used as an effective means to correct the pattern of gridded temperature.
Using the smart grid reality analysis product (CLDAS-V2.0) and the European Centre for 〖JP〗Medium-Range Weather Forecast Global Model (EC) product, which were operated at the national level from September 2017 to March 2021, six regions are constructed according to the geographical distribution characteristics of Hubei Province. The temperature forecast model established by LightGBM machine learning algorithm is used to generate 0.05°×0.05° gridded temperature forecast products in Hubei Province, and the forecast products are verified by the forecast products and grid data from April to September 2021. The results show that the temperature prediction method (MLT) based on machine learning has achieved a good forecast effect, and MLT is superior to SCMOC and EC model products in 0-72 h lead time. The error of MLT in mountain area is larger than that in plain area, but the correction amplitude of MLT in mountain area is larger than that in plain area, and the correction amplitude of daily maximum temperature is larger than that of daily minimum temperature. The diurnal variations of MAE in MLT, SCMOC and EC model products from April to September present the characteristics of higher in daytime, lower over night, and convex single-peak in afternoon. The MAE value of MLT is lower than that of SCMOC and EC model products, and still has an advantage in changeable weather. The results of site test and grid test are consistent, and the temperature forecast product based on grid modeling is also corrected. Machine learning can be used as an effective means to correct the pattern of gridded temperature.
2024, 50(1):115-125.
DOI: 10.7519/j.issn.1000-0526.2023.102701
Abstract:
In the summer of 2023, the overall climate of China was characterized by high temperature with less rainfall, and the regional and periodic high temperature, droughts, floods and other meteorological disasters occurred frequently. The precipitation displayed remarkable spatial differences across China, with the major rain belt mainly located in northern China. Both the Songhua River Basin and the Haihe River Basin experienced severe flash flooding in middle summer. Both the generated typhoons and the landfall typhoons were less than in the same period in years, but the northward typhoons incurred extremely serious flood disasters in the Beijing-Tianjin-Hebei Region. The national summer temperature ranked the second highest since 1961, especially in northern China. Both North China and Northwest China experienced the most intense heat waves. The anomalous excessive precipitation in North China and Northeast China was caused by different circulation systems. For the former, it was mainly caused by a rarely-seen extreme precipitation process from the end of July to early August, during which the strong moisture transport by Typhoon Doksuri and Typhoon Khanun was blocked by a much more westward and northward western Pacific subtropical high combined with the topographic effect of the Taihang Mountains; for the latter, the exceptional southerly water vapor transport in the whole troposphere along the east of Northeast China can be regarded as the direct cause. This abnormal circulation was possibly associated with the decrease in the sea ice concentration in the Barents Sea in early summer and the abnormal warmer sea surface temperature in the Northwest Pacific in middle summer.
In the summer of 2023, the overall climate of China was characterized by high temperature with less rainfall, and the regional and periodic high temperature, droughts, floods and other meteorological disasters occurred frequently. The precipitation displayed remarkable spatial differences across China, with the major rain belt mainly located in northern China. Both the Songhua River Basin and the Haihe River Basin experienced severe flash flooding in middle summer. Both the generated typhoons and the landfall typhoons were less than in the same period in years, but the northward typhoons incurred extremely serious flood disasters in the Beijing-Tianjin-Hebei Region. The national summer temperature ranked the second highest since 1961, especially in northern China. Both North China and Northwest China experienced the most intense heat waves. The anomalous excessive precipitation in North China and Northeast China was caused by different circulation systems. For the former, it was mainly caused by a rarely-seen extreme precipitation process from the end of July to early August, during which the strong moisture transport by Typhoon Doksuri and Typhoon Khanun was blocked by a much more westward and northward western Pacific subtropical high combined with the topographic effect of the Taihang Mountains; for the latter, the exceptional southerly water vapor transport in the whole troposphere along the east of Northeast China can be regarded as the direct cause. This abnormal circulation was possibly associated with the decrease in the sea ice concentration in the Barents Sea in early summer and the abnormal warmer sea surface temperature in the Northwest Pacific in middle summer.
2024, 50(1):126-132.
DOI: 10.7519/j.issn.1000-0526.2023.120601
Abstract:
The main characteristics of the general atmospheric circulation in October 2023 are that the polar vortex was distributed in a multipolar pattern, the mid- and high-latitude regions in Eurasia were mainly controlled by high-pressure ridges, the western Pacific subtropical high was obviously strong, located to more northward, showing a zonal distribution, and there were tropical depressions in eastern Asia. In October, the monthly mean temperature was 12.0℃, 1.4℃ higher than that of the same period in normal years (10.6℃). The monthly average precipitation was 35.4 mm, which was the same as the normal value (35.6 mm). During this month, the autumn rainfall in West China was excessive and more prolonged, bringing torrential rains and floods to some areas. The number of generated typhoons was less than normal but above-normal typhoons made landfall, with heavier impacts to the affected areas. Among them, Typhoon Koinu triggered severe precipitation in South China, and Typhoon Samba made landfall in China three times, causing disasters in South China. In addition, there were two cold air processes and one fog-haze weather event during this month.
The main characteristics of the general atmospheric circulation in October 2023 are that the polar vortex was distributed in a multipolar pattern, the mid- and high-latitude regions in Eurasia were mainly controlled by high-pressure ridges, the western Pacific subtropical high was obviously strong, located to more northward, showing a zonal distribution, and there were tropical depressions in eastern Asia. In October, the monthly mean temperature was 12.0℃, 1.4℃ higher than that of the same period in normal years (10.6℃). The monthly average precipitation was 35.4 mm, which was the same as the normal value (35.6 mm). During this month, the autumn rainfall in West China was excessive and more prolonged, bringing torrential rains and floods to some areas. The number of generated typhoons was less than normal but above-normal typhoons made landfall, with heavier impacts to the affected areas. Among them, Typhoon Koinu triggered severe precipitation in South China, and Typhoon Samba made landfall in China three times, causing disasters in South China. In addition, there were two cold air processes and one fog-haze weather event during this month.
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ISSN:1000-0526 CN:11-2282/P