Surface upward longwave radiation(SULR)is one of the four components of the surface radiation budget,which is defined as the total surface upward radiative flux in the spectral domain of 4-100μm.The SULR is an indica...Surface upward longwave radiation(SULR)is one of the four components of the surface radiation budget,which is defined as the total surface upward radiative flux in the spectral domain of 4-100μm.The SULR is an indicator of surface thermal conditions and greatly impacts weather,climate,and phenology.Big Earth data derived from satellite remote sensing have been an important tool for studying earth science.The Advanced Baseline Imager(ABI)onboard the Geostationary Operational Environmental Satellite(GOES-16)has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR.In this study,based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset,we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020.The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites.Compared with the SULR dataset of the Global LAnd Surface Satellite(GLASS)longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer(MODIS)onboard the polar-orbiting Terra and Aqua satellites,the ABI/GOES-16 SULR dataset has commensurate accuracy(an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of−4.4 W/m2 vs−2.57 W/m2),coarser spatial resolution(2 km at nadir vs 1 km resolution),less spatial coverage(most of the Americas vs global),fewer weather conditions(clear-sky vs all-weather conditions)and a greatly improved temporal resolution(48 vs 4 observations a day).The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.展开更多
The presence of fog in offshore regions poses significant hazards to navigation and aviation,making fog nowcasting indispensable for various industries,including oil and gas.This study presented a novel approach utili...The presence of fog in offshore regions poses significant hazards to navigation and aviation,making fog nowcasting indispensable for various industries,including oil and gas.This study presented a novel approach utilizing Recurrent Neural Networks(RNN)within a deep learning framework to address this need.Leveraging geos-tationary GOES-16 satellite data from the summers of 2018 and 2019,fog maps were generated as input.The model incorporated Convolutional Long Short-Term Memory(ConvLSTM)layers and was trained with a unique loss function combining Minimum Squared Error(MSE)and structural DISSIMilarity(DSSIM)metrics.Validation results demonstrated an approximate 60%accuracy for both two-hour and three-hour nowcasting.Furthermore,evalua-tion against in-situ data from an offshore platform revealed a Probability of Detection(PoD)of 0.75 and False Alarm Rate(FAR)of 0.14 for two-hour nowcasting,PoD of 0.75 and FAR of 0.20 for three-hour nowcasting,and PoD of 0.70 and FAR of 0.20 for six-hour nowcasting.These findings suggested the operational viability of the proposed method for short-term fog forecasting in offshore environments.展开更多
基金This work was supported in part by The National Key Research and Development Program of China[2018YFA0605503]National Natural Science of Foundation of China[41871258,41930111,41901287 and 42071317]+1 种基金The Youth Innovation Promotion Association CAS[2020127]The“Future Star”Talent Plan of the Aerospace Information Research Institute of Chinese Academy of Sciences[Y920570Z1F].
文摘Surface upward longwave radiation(SULR)is one of the four components of the surface radiation budget,which is defined as the total surface upward radiative flux in the spectral domain of 4-100μm.The SULR is an indicator of surface thermal conditions and greatly impacts weather,climate,and phenology.Big Earth data derived from satellite remote sensing have been an important tool for studying earth science.The Advanced Baseline Imager(ABI)onboard the Geostationary Operational Environmental Satellite(GOES-16)has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR.In this study,based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset,we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020.The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites.Compared with the SULR dataset of the Global LAnd Surface Satellite(GLASS)longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer(MODIS)onboard the polar-orbiting Terra and Aqua satellites,the ABI/GOES-16 SULR dataset has commensurate accuracy(an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of−4.4 W/m2 vs−2.57 W/m2),coarser spatial resolution(2 km at nadir vs 1 km resolution),less spatial coverage(most of the Americas vs global),fewer weather conditions(clear-sky vs all-weather conditions)and a greatly improved temporal resolution(48 vs 4 observations a day).The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.
文摘The presence of fog in offshore regions poses significant hazards to navigation and aviation,making fog nowcasting indispensable for various industries,including oil and gas.This study presented a novel approach utilizing Recurrent Neural Networks(RNN)within a deep learning framework to address this need.Leveraging geos-tationary GOES-16 satellite data from the summers of 2018 and 2019,fog maps were generated as input.The model incorporated Convolutional Long Short-Term Memory(ConvLSTM)layers and was trained with a unique loss function combining Minimum Squared Error(MSE)and structural DISSIMilarity(DSSIM)metrics.Validation results demonstrated an approximate 60%accuracy for both two-hour and three-hour nowcasting.Furthermore,evalua-tion against in-situ data from an offshore platform revealed a Probability of Detection(PoD)of 0.75 and False Alarm Rate(FAR)of 0.14 for two-hour nowcasting,PoD of 0.75 and FAR of 0.20 for three-hour nowcasting,and PoD of 0.70 and FAR of 0.20 for six-hour nowcasting.These findings suggested the operational viability of the proposed method for short-term fog forecasting in offshore environments.
