Accurate three-dimensional(3D)wind products are crucial for weather and climate research.Besides analysis/reanalysis datasets,measurements from the geostationary hyperspectral infrared sounder provide another independ...Accurate three-dimensional(3D)wind products are crucial for weather and climate research.Besides analysis/reanalysis datasets,measurements from the geostationary hyperspectral infrared sounder provide another independent source for obtaining 3D wind profiles at high temporal resolution.In this study,an assessment of the 3D wind product retrieved from Geostationary Interferometric Infrared Sounder(GIIRS)observations,along with several typical reanalysis/analysis datasets from representative centers(e.g.,ERA5,CRA-40,MERRA-2,JRA-55 and ART)is conducted,using the independent wind profile observations from the radiosonde network as the truth.It is found that selection of spatial matching thresholds is crucial for objective and reliable evaluation,and homogeneity scene selection aids in reducing inconsistencies between assessed wind data and radiosonde measurements.Reanalysis/analysis datasets show good accuracy between 300 and 900 hPa,but they reveal larger errors near the planetary boundary layer and above 300 hPa.Moreover,wind speed and direction errors are dependent on wind speed,with positive biases dominating at lower wind speeds and shifting towards negative biases as wind speed increases.The ART analysis and ERA5 reanalysis datasets demonstrate the best overall quality,while JRA-55,CRA-40,and MERRA-2 exhibit inferior performance.GIIRS-derived winds perform comparably to other datasets at low wind speeds but show degraded accuracy at higher wind speeds.ART and ERA5 exhibit relatively stable quality across varying weather stability conditions compared to other wind products.展开更多
Measurements from a hyperspectral infrared(HIR) sounder onboard a satellite in geostationary orbit not only provide atmospheric thermodynamic information,but also can be used to infer dynamic information with high tem...Measurements from a hyperspectral infrared(HIR) sounder onboard a satellite in geostationary orbit not only provide atmospheric thermodynamic information,but also can be used to infer dynamic information with high temporal resolution.Radiance measurements from the Geostationary Interferometric Infrared Sounder(GIIRS),obtained with 15-min temporal resolution during Typhoon Maria(2018) and 30-min temporal resolution during Typhoon Lekima(2019),were used to derive three-dimensional(3D) horizontal winds by tracking the motion of atmospheric moisture.This work focused on the impact of assimilation of 3D winds on typhoon analyses and forecasts using the operational NWP model of the China Meteorological Administration(CMA-MESO),and improved understanding of the potential benefits of assimilating dynamic information from geostationary sounder data with higher temporal resolution.The standard deviation of the observations minus simulations revealed that the accuracy of the derived 3D winds with 15-min resolution was higher than that of derived winds with 30-min resolution.Experiments showed that the assimilation system can effectively absorb the information of the derived 3D winds,and that dynamic information from clear-sky areas can be transferred to typhoon areas.In typhoon prediction,assimilation of the derived 3D winds had greatest influence on the typhoon track,and less influence on the maximum wind speed.Assimilation of the derived 3D winds reduced the average track error by 17.4% for Typhoon Maria(2018) and by 3.5% for Typhoon Lekima(2019) during their entire 36-h forecasts initiated at different times.Assimilation of GIIRS dynamic information can substantially improve forecasts of heavy precipitation by CMAMESO.Results indicate that the assimilation of dynamic information from high-temporal-resolution geostationary HIR sounder data adds value for improved numerical weather prediction.展开更多
A physical retrieval approach based on the one-dimensional variational(1 D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and part...A physical retrieval approach based on the one-dimensional variational(1 D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4 A GIIRS(geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center(NSMC)during the periods from December 2019 to January 2020(winter) and from July 2020 to August 2020(summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1 D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error(RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there.Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg^(–1). Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 h Pa and 300 h Pa both in summer and winter in terms of RMSE.展开更多
Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-re...Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels.This study introduces a fast and accurate RT model for the hyperspectral infrared(HIR)sounder based on principal component analysis(PCA)or machine learning(i.e.,neural network,NN).The Geosynchronous Interferometric Infrared Sounder(GIIRS),the first HIR sounder onboard the geostationary Fengyun-4 satellites,is considered to be a candidate example for model development and validation.Our method uses either PCA or NN(PCA/NN)twice for the atmospheric transmittance and radiance,respectively,to reduce the number of independent but similar simulations to accelerate RT simulations;thereby,it is referred to as a multi-domain compression model.The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently.The second PCA/NN is performed in the traditional spectral radiance domain.Meanwhile,a new method is introduced to choose representative variables for the PCA/NN scheme developments.The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference(BTD)less than 0.1 K,and the compressions based on PCA or NN methods result in comparable efficiency and accuracy.Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.展开更多
Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferome...Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferometric infrared sounder(GIIRS)of FengYun-4 A(FY-4 A)observation and simulated brightness temperature based on background field,the brightness temperature bias correction of GIIRS channel is carried out based on random forest(RF)and extreme gradient boosting(XGBoost)machine learning.Based on the case data of Typhoon"Haishen",the correction effect of machine learning method is compared with Harris and Kelly’s"off-line"method,and the importance of different predictors to the bias correction is further discussed.The experimental results show that the systematic bias is effectively corrected,and the following conclusions are obtained:the correction effect is improved by adding geographic information(longitude and latitude)into the predictors;under the given combination of predictors,the correction effect of XGBoost is the best,followed by random forest,and finally offline method,but the three methods can correct the bias effectively;compared with long wave data of FY-4 A/GIIRS,machine learning may be more feasible for medium wave data bias correction.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
基金supported by the Chinese National Natural Science Foundation(Grant Nos.U2142201 and 42105126).
文摘Accurate three-dimensional(3D)wind products are crucial for weather and climate research.Besides analysis/reanalysis datasets,measurements from the geostationary hyperspectral infrared sounder provide another independent source for obtaining 3D wind profiles at high temporal resolution.In this study,an assessment of the 3D wind product retrieved from Geostationary Interferometric Infrared Sounder(GIIRS)observations,along with several typical reanalysis/analysis datasets from representative centers(e.g.,ERA5,CRA-40,MERRA-2,JRA-55 and ART)is conducted,using the independent wind profile observations from the radiosonde network as the truth.It is found that selection of spatial matching thresholds is crucial for objective and reliable evaluation,and homogeneity scene selection aids in reducing inconsistencies between assessed wind data and radiosonde measurements.Reanalysis/analysis datasets show good accuracy between 300 and 900 hPa,but they reveal larger errors near the planetary boundary layer and above 300 hPa.Moreover,wind speed and direction errors are dependent on wind speed,with positive biases dominating at lower wind speeds and shifting towards negative biases as wind speed increases.The ART analysis and ERA5 reanalysis datasets demonstrate the best overall quality,while JRA-55,CRA-40,and MERRA-2 exhibit inferior performance.GIIRS-derived winds perform comparably to other datasets at low wind speeds but show degraded accuracy at higher wind speeds.ART and ERA5 exhibit relatively stable quality across varying weather stability conditions compared to other wind products.
基金supported by the National Natural Science Foundation of China(Grant No.U2142201)the Fengyun Application Pion eering Project(Grant No.FY-APP-ZX-2022.01)。
文摘Measurements from a hyperspectral infrared(HIR) sounder onboard a satellite in geostationary orbit not only provide atmospheric thermodynamic information,but also can be used to infer dynamic information with high temporal resolution.Radiance measurements from the Geostationary Interferometric Infrared Sounder(GIIRS),obtained with 15-min temporal resolution during Typhoon Maria(2018) and 30-min temporal resolution during Typhoon Lekima(2019),were used to derive three-dimensional(3D) horizontal winds by tracking the motion of atmospheric moisture.This work focused on the impact of assimilation of 3D winds on typhoon analyses and forecasts using the operational NWP model of the China Meteorological Administration(CMA-MESO),and improved understanding of the potential benefits of assimilating dynamic information from geostationary sounder data with higher temporal resolution.The standard deviation of the observations minus simulations revealed that the accuracy of the derived 3D winds with 15-min resolution was higher than that of derived winds with 30-min resolution.Experiments showed that the assimilation system can effectively absorb the information of the derived 3D winds,and that dynamic information from clear-sky areas can be transferred to typhoon areas.In typhoon prediction,assimilation of the derived 3D winds had greatest influence on the typhoon track,and less influence on the maximum wind speed.Assimilation of the derived 3D winds reduced the average track error by 17.4% for Typhoon Maria(2018) and by 3.5% for Typhoon Lekima(2019) during their entire 36-h forecasts initiated at different times.Assimilation of GIIRS dynamic information can substantially improve forecasts of heavy precipitation by CMAMESO.Results indicate that the assimilation of dynamic information from high-temporal-resolution geostationary HIR sounder data adds value for improved numerical weather prediction.
基金supported in part by the National Key Research and Development Program of China under Grant No.2018YFC1507302in part by the National Natural Science Foundation of China under Grant No.41975028。
文摘A physical retrieval approach based on the one-dimensional variational(1 D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4 A GIIRS(geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center(NSMC)during the periods from December 2019 to January 2020(winter) and from July 2020 to August 2020(summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1 D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error(RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there.Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg^(–1). Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 h Pa and 300 h Pa both in summer and winter in terms of RMSE.
基金supported by the National Natural Science Foundation of China(Grant No.42122038)。
文摘Forward radiative transfer(RT)models are essential for atmospheric applications such as remote sensing and weather and climate models,where computational efficiency becomes equally as important as accuracy for high-resolution hyperspectral measurements that need rigorous RT simulations for thousands of channels.This study introduces a fast and accurate RT model for the hyperspectral infrared(HIR)sounder based on principal component analysis(PCA)or machine learning(i.e.,neural network,NN).The Geosynchronous Interferometric Infrared Sounder(GIIRS),the first HIR sounder onboard the geostationary Fengyun-4 satellites,is considered to be a candidate example for model development and validation.Our method uses either PCA or NN(PCA/NN)twice for the atmospheric transmittance and radiance,respectively,to reduce the number of independent but similar simulations to accelerate RT simulations;thereby,it is referred to as a multi-domain compression model.The first PCA/NN gives monochromatic gas transmittance in both spectral and atmospheric pressure domains for each gas independently.The second PCA/NN is performed in the traditional spectral radiance domain.Meanwhile,a new method is introduced to choose representative variables for the PCA/NN scheme developments.The model is three orders of magnitude faster than the standard line-by-line-based simulations with averaged brightness temperature difference(BTD)less than 0.1 K,and the compressions based on PCA or NN methods result in comparable efficiency and accuracy.Our fast model not only avoids an excessively complicated transmittance scheme by using PCA/NN but is also highly flexible for hyperspectral instruments with similar spectral ranges simply by updating the corresponding spectral response functions.
基金Supported by the National Natural Science Foundation of China(41805080)Special Project for Innovation and Development of Anhui Meteorological Bureau(CXB202101)Central Asian Fund for Atmospheric Science Research(CAAS202003)。
文摘Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferometric infrared sounder(GIIRS)of FengYun-4 A(FY-4 A)observation and simulated brightness temperature based on background field,the brightness temperature bias correction of GIIRS channel is carried out based on random forest(RF)and extreme gradient boosting(XGBoost)machine learning.Based on the case data of Typhoon"Haishen",the correction effect of machine learning method is compared with Harris and Kelly’s"off-line"method,and the importance of different predictors to the bias correction is further discussed.The experimental results show that the systematic bias is effectively corrected,and the following conclusions are obtained:the correction effect is improved by adding geographic information(longitude and latitude)into the predictors;under the given combination of predictors,the correction effect of XGBoost is the best,followed by random forest,and finally offline method,but the three methods can correct the bias effectively;compared with long wave data of FY-4 A/GIIRS,machine learning may be more feasible for medium wave data bias correction.
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.
