The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the ini...The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.展开更多
风云四号B星(FY-4B)是中国最新一代的静止轨道气象卫星,其资料具有更高的时空分辨率。为了进一步推进FY-4B云导风资料在数值模式中的应用,以FY-4B云导风资料为主要研究对象,结合FY-4A云导风资料进行对比,首先分析了其观测误差的垂直分...风云四号B星(FY-4B)是中国最新一代的静止轨道气象卫星,其资料具有更高的时空分辨率。为了进一步推进FY-4B云导风资料在数值模式中的应用,以FY-4B云导风资料为主要研究对象,结合FY-4A云导风资料进行对比,首先分析了其观测误差的垂直分布情况,以此更新WRFDA(Weather Research and Forecasting model Data Assimilation system)同化系统中默认观测误差,随后进一步探究了云导风资料同化对2022年台风“梅花”的预报影响。研究结果表明:FY-4B云导风资料U、V分量的观测误差整体上小于FY-4A;同化FY-4A云导风资料后,模拟的台风路径逐渐偏西;而同化FY-4B云导风资料使得台风中心动力场信息更加完善,分析场对流层中高层西风增强,预报的台风路径更接近实况;相较于同化FY-4A云导风资料,同化FY-4B云导风资料对降水预报的改善效果更好。展开更多
The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clea...The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clear-sky radiance of 48 longwave channels from the FY-4B Geostationary Interferometric Infrared Sounder(GIIRS)is assimilated into the China Meteorological Administration mesoscale model(CMA-MESO)to evaluate its impact on NCCV development and its effects on rainfall forecasting.The results show that after assimilating the GIIRS radiance data,the warm center at 200 hPa and the cold center at 850 hPa of the NCCV are strengthened,and the dry intrusion at 850 hPa becomes more pronounced.This leads to a stronger NCCV intensity in the following 24 hours and brings the precipitation intensity and area closer to the observation,resulting in significant improvements compared to the experiments that do not assimilate GIIRS radiance data.Furthermore,it is found that the enhancement of the precipitation forecast is associated with the strengthening of cold air in the middle and lower troposphere,which intensifies the uplift of the warm,moist airflow.These results highlight the potential value of GIIRS data assimilation in enhancing early warnings and forecasts of extreme weather events influenced by the NCCV.展开更多
The Northeast China Cold Vortex(NECV)is a significant atmospheric circulation system that triggers severe weather in mid-to-high latitudes of Asia.Fengyun-4B(FY-4B)satellite provides 15-min atmospheric motion vector(A...The Northeast China Cold Vortex(NECV)is a significant atmospheric circulation system that triggers severe weather in mid-to-high latitudes of Asia.Fengyun-4B(FY-4B)satellite provides 15-min atmospheric motion vector(AMV)and 2-h three-dimensional temperature profiles,enabling unprecedented high spatiotemporal resolution for real-time vortex tracking.This study evaluates the effectiveness of FY-4B AMV and temperature products in tracking 24 NECVs in 2023,among which two strong NECVs in winter and summer 2023 were carefully examined.We first assessed the accuracy of wind speed and direction of the AMVs in the NECV monitoring region by comparing them with radiosonde observations,revealing reasonable correlation coefficients(CC),mean absolute errors(MAE),and root mean square errors(RMSE).NECVs and their centers were identified by using AMV data from four channels(CH09,CH10,CH11,and CH13)within the 200–500-hPa layer,employing the“8-point method”that sets specific criteria for the wind directions at 8 surrounding points to ensure a consistent cyclonic pattern around the central point.The NECV centers identified from AMVs are found to be close(mean distance of 181.9 km)to those determined by ERA5 geopotential height.The retrieved FY-4B temperature data are also evaluated against radiosonde observations,showing a high CC of 0.996 and RMSE of 1.87 K,indicating reliable temperature retrievals for NECV tracking.Based on the FY-4B/Geostationary Interferometric Infrared Sounder(GIIRS)500-hPa temperature,the NECV cold centers are obtained and cross-validated against ERA5 reanalysis temperature at 500 hPa,revealing a mean distance deviation of 140.6 km.The real-time operational NECV monitoring based on the FY-4B AMV and temperature products on high spatiotemporal resolutions in this study provides valuable information for disaster prevention and mitigation.展开更多
Fengyun-4A(FY-4A)and Fengyun-4B(FY-4B)are the first two geostationary satellites equipped with an infrared hyperspectral sounder known as the Geostationary Interferometric Infrared Sounder(GIIRS).This paper validates ...Fengyun-4A(FY-4A)and Fengyun-4B(FY-4B)are the first two geostationary satellites equipped with an infrared hyperspectral sounder known as the Geostationary Interferometric Infrared Sounder(GIIRS).This paper validates the FY-4A/4B GIIRS atmospheric temperature profile(ATP)products by comparing them with routine radiosonde data collected from 89 stations across China between February 2023 and January 2024.To mitigate the effects of balloon drift,a collocation strategy was implemented for each pressure level to identify the nearest temperature retrieval within 1800 s and 0.2°of the actual observation time and location of the radiosonde.This strategy enhances the agreement between radiosonde observations and satellite retrievals,particularly under clear-sky condition.Utilizing the per-level collocation strategy,we conducted a comprehensive evaluation of the GIIRS ATP products and analyzed how the evaluation results vary in relation to sensors(FY-4A/4B GIIRS),sky conditions(cloudy/clear),months,times(morning/evening),pressure levels,and regions.The results indicate that the FY-4A/4B GIIRS ATP products demonstrate a strong consistency with radiosonde observations,achieving high coefficients of determination(0.990–0.997),favorable biases(-0.92 to 0.06 K),and low RMSE(root-mean-square error)values(1.58–2.57 K).In comparison to cloudy conditions,the evaluation results are significantly improved and exhibit more pronounced variations across months and pressure levels under clear-sky conditions.The summer months demonstrate better performance than the winter months,and the retrievals in the evening are slightly better than those in the morning.Near the top of the atmospheric boundary layer(850 h Pa)and the tropopause(200 h Pa),the RMSE shows higher values and increased spatial heterogeneity compared to the mid-tropospheric level(500 h Pa).FY-4B demonstrates significant superiority over FY-4A under clear-sky conditions;however,this trend does not persist under cloudy conditions due to the large negative biases associated with FY-4B,highlighting the need for further improvement in the retrieval method.展开更多
Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostation...Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.展开更多
基金National Key Research and Development Program of China (2022YFC3004004)National Natural Science Foundation of China (42075155,12241104)National Natural Science Foundation of China Joint Fund (U2342213)。
文摘The infrared channels of the FY-4B advanced geosynchronous radiation imagers(AGRI) play a crucial role in temperature and humidity analyses for mesoscale numerical weather prediction, particularly in enhancing the initial field quality and the forecasting accuracy of the model. This study assimilated FY-4B AGRI data into the CMA-MESO model and analyzed the bias characteristics and correction methods. Analysis of the AGRI data revealed a clear diurnal variation in the bias, which was positively correlated with the solar elevation angle. However, the diurnal variation in the bias lagged behind the solar elevation angle, likely owing to temperature changes and delayed instrument responses resulting from solar radiation. To address this issue, we propose a correction method that utilizes the solar elevation angle after an optimal time shift. Using the time-shifted solar elevation angle as a predictor effectively reduces the diurnal variation in bias and significantly improves the correction effect. This approach provides theoretical support for the assimilation of FY-4B AGRI data into mesoscale numerical weather predictions, thereby enhancing the reliability of the assimilation results.
文摘风云四号B星(FY-4B)是中国最新一代的静止轨道气象卫星,其资料具有更高的时空分辨率。为了进一步推进FY-4B云导风资料在数值模式中的应用,以FY-4B云导风资料为主要研究对象,结合FY-4A云导风资料进行对比,首先分析了其观测误差的垂直分布情况,以此更新WRFDA(Weather Research and Forecasting model Data Assimilation system)同化系统中默认观测误差,随后进一步探究了云导风资料同化对2022年台风“梅花”的预报影响。研究结果表明:FY-4B云导风资料U、V分量的观测误差整体上小于FY-4A;同化FY-4A云导风资料后,模拟的台风路径逐渐偏西;而同化FY-4B云导风资料使得台风中心动力场信息更加完善,分析场对流层中高层西风增强,预报的台风路径更接近实况;相较于同化FY-4A云导风资料,同化FY-4B云导风资料对降水预报的改善效果更好。
基金sponsored by the National Natural Science Foundation of China(Grant No.42275171)the Basic Research Operating Expenses of the Institute of Meteorological Sciences,CMA(Grant No.2023Z019)+3 种基金the National Key Research and Development Program of China(Grant No.2022YFF0801304)the China Meteorological Administration Youth Innovation Team Fund(Grant No.CMA2024QN05)a Liaoning Provincial Meteorological Bureau Project(Grant No.D202201)Shenyang Institute of Atmospheric Environment Projects(Grant Nos.2022SYIAEJY13 and 2018SYIAEZD5).
文摘The Northeast China Cold Vortex(NCCV)is a common cut-off low-pressure system in Northeast China,frequently causing localized heavy rainfall,strong winds,and thunderstorms during the early summer.In this study,the clear-sky radiance of 48 longwave channels from the FY-4B Geostationary Interferometric Infrared Sounder(GIIRS)is assimilated into the China Meteorological Administration mesoscale model(CMA-MESO)to evaluate its impact on NCCV development and its effects on rainfall forecasting.The results show that after assimilating the GIIRS radiance data,the warm center at 200 hPa and the cold center at 850 hPa of the NCCV are strengthened,and the dry intrusion at 850 hPa becomes more pronounced.This leads to a stronger NCCV intensity in the following 24 hours and brings the precipitation intensity and area closer to the observation,resulting in significant improvements compared to the experiments that do not assimilate GIIRS radiance data.Furthermore,it is found that the enhancement of the precipitation forecast is associated with the strengthening of cold air in the middle and lower troposphere,which intensifies the uplift of the warm,moist airflow.These results highlight the potential value of GIIRS data assimilation in enhancing early warnings and forecasts of extreme weather events influenced by the NCCV.
基金Supported by the China Meteorological Administration Northeast China Cold Vortex Research Key Laboratory(2023SYIAEKFZD04)Research Project of China Meteorological Administration Training Centre(2024CMATCQN03 and2024CMATCPY01)。
文摘The Northeast China Cold Vortex(NECV)is a significant atmospheric circulation system that triggers severe weather in mid-to-high latitudes of Asia.Fengyun-4B(FY-4B)satellite provides 15-min atmospheric motion vector(AMV)and 2-h three-dimensional temperature profiles,enabling unprecedented high spatiotemporal resolution for real-time vortex tracking.This study evaluates the effectiveness of FY-4B AMV and temperature products in tracking 24 NECVs in 2023,among which two strong NECVs in winter and summer 2023 were carefully examined.We first assessed the accuracy of wind speed and direction of the AMVs in the NECV monitoring region by comparing them with radiosonde observations,revealing reasonable correlation coefficients(CC),mean absolute errors(MAE),and root mean square errors(RMSE).NECVs and their centers were identified by using AMV data from four channels(CH09,CH10,CH11,and CH13)within the 200–500-hPa layer,employing the“8-point method”that sets specific criteria for the wind directions at 8 surrounding points to ensure a consistent cyclonic pattern around the central point.The NECV centers identified from AMVs are found to be close(mean distance of 181.9 km)to those determined by ERA5 geopotential height.The retrieved FY-4B temperature data are also evaluated against radiosonde observations,showing a high CC of 0.996 and RMSE of 1.87 K,indicating reliable temperature retrievals for NECV tracking.Based on the FY-4B/Geostationary Interferometric Infrared Sounder(GIIRS)500-hPa temperature,the NECV cold centers are obtained and cross-validated against ERA5 reanalysis temperature at 500 hPa,revealing a mean distance deviation of 140.6 km.The real-time operational NECV monitoring based on the FY-4B AMV and temperature products on high spatiotemporal resolutions in this study provides valuable information for disaster prevention and mitigation.
基金Supported by the National Key Research and Development Program of China(2023YFB3905802)。
文摘Fengyun-4A(FY-4A)and Fengyun-4B(FY-4B)are the first two geostationary satellites equipped with an infrared hyperspectral sounder known as the Geostationary Interferometric Infrared Sounder(GIIRS).This paper validates the FY-4A/4B GIIRS atmospheric temperature profile(ATP)products by comparing them with routine radiosonde data collected from 89 stations across China between February 2023 and January 2024.To mitigate the effects of balloon drift,a collocation strategy was implemented for each pressure level to identify the nearest temperature retrieval within 1800 s and 0.2°of the actual observation time and location of the radiosonde.This strategy enhances the agreement between radiosonde observations and satellite retrievals,particularly under clear-sky condition.Utilizing the per-level collocation strategy,we conducted a comprehensive evaluation of the GIIRS ATP products and analyzed how the evaluation results vary in relation to sensors(FY-4A/4B GIIRS),sky conditions(cloudy/clear),months,times(morning/evening),pressure levels,and regions.The results indicate that the FY-4A/4B GIIRS ATP products demonstrate a strong consistency with radiosonde observations,achieving high coefficients of determination(0.990–0.997),favorable biases(-0.92 to 0.06 K),and low RMSE(root-mean-square error)values(1.58–2.57 K).In comparison to cloudy conditions,the evaluation results are significantly improved and exhibit more pronounced variations across months and pressure levels under clear-sky conditions.The summer months demonstrate better performance than the winter months,and the retrievals in the evening are slightly better than those in the morning.Near the top of the atmospheric boundary layer(850 h Pa)and the tropopause(200 h Pa),the RMSE shows higher values and increased spatial heterogeneity compared to the mid-tropospheric level(500 h Pa).FY-4B demonstrates significant superiority over FY-4A under clear-sky conditions;however,this trend does not persist under cloudy conditions due to the large negative biases associated with FY-4B,highlighting the need for further improvement in the retrieval method.
基金supported by the National Natural Science Foundation of China (NSFC) (Grant No.42205044)Feng Yun Application Pioneering Project (FY-APP) Innovation Center for Feng Yun Meteorological Satellite (FYSIC) Special Project (FY-APP-XC-2023.04)the Wuxi University Research Start-up Fund for Recruited Talent。
文摘Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.
