首次将MSG-2(Meteosat Second Generation-2)卫星上的旋转增强可见光及红外成像仪(Spinning Enhanced Visible and Infrared Imager,SEVIRI)的观测资料同化到美国国家环境预报中心(National Centers for Environmental Prediction,NCEP...首次将MSG-2(Meteosat Second Generation-2)卫星上的旋转增强可见光及红外成像仪(Spinning Enhanced Visible and Infrared Imager,SEVIRI)的观测资料同化到美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)全球资料同化系统(global data assimilation system,GDAS)中。对当前的地球静止业务环境卫星(Geostationary Operational Environmental Satellite,GOES)成像仪资料的同化问题也进行了进一步探讨。利用CRTM(The Community Radiative Transfer Model)模式,对SEVIRI辐射率观测资料进行了模拟。为了对红外辐射率资料进行模拟,CRTM模式中的几个关键部分得到改进,例如:动态更新地面发射率资料以及采用了快速精确的气体吸收模块。为了改进对SEVIRI和GOES成像仪辐射率资料的模拟效果,采用了GSICS(The Global Space-Based Inter-Calibration System)标定订正。初步研究结果表明,包含对SEVIRI辐射率资料的水汽通道(6.25和7.35μm)和二氧化碳通道(13.40μm)的同化对GFS(Global Forecast System)6d预报具有显著的正影响;而对其他5个SEVIRI红外窗口通道资料的同化则减小了这种正影响。通过应用GSICS标定算法,订正了SEVIRI和GOES-12成像仪观测资料的偏差,提高了对GFS预报的影响。此外,还需作进一步研究来提高对SEVIRI红外窗口通道辐射率资料同化的有效性。展开更多
Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required t...Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required to develop workflows,executable on cloud-based platforms,for ingesting the satellite data,performing the targeted processes,and generating the desired products.In this study,an operational workflow is proposed,based on monthly Evaporative Stress Index(ESI)anomaly,and implemented in cloud-based online Virtual Earth Laboratory(VLab)platform,as a demonstration,to monitor European agricultural water stress.To this end,daily time-series of actual and reference evapotranspiration(ETa and ET0),from the Spinning Enhanced Visible and Infrared Imager(SEVIRI)sensor,were used to execute the proposed workflow successfully on VLab.The execution of the workflow resulted in obtaining one decade(2011–2020)of European monthly agricultural water stress maps at 0.04˚spatial resolution and corresponding stress reports for each country.To support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through MapStore.This enables creating a dashboard for better visualization of the results for end-users.The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.展开更多
The simplest way of building a look-up table (LUT) for the retrieval of cloud microphysical properties is to use a standard atmospheric profile and vertically uniform cloud microphysics. Such an assumption has been de...The simplest way of building a look-up table (LUT) for the retrieval of cloud microphysical properties is to use a standard atmospheric profile and vertically uniform cloud microphysics. Such an assumption has been demonstrated to be incoherent with in-cloud observations. This paper aims to show the effect of some atmospheric conditions associated with fog as well as its macro-and microstructure on brightness temperature (BT) for the MSG/ SEVIRI satellite using libRadtran. The sensitivity tests were performed by gradually changing some features from the initial data, such as cloud cover, total water vapor column, thermal inversion intensity, fog depth, fog microstructure, and others. The results revealed that some variables can cause significant variations on BT and, consequently, discrepancies in the retrieval of fog microphysical properties. Also, a variation as high as 0.5<span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:-apple-system, "font-size:16px;white-space:normal;background-color:#FFFFFF;">°</span>C</span><span style="font-family:Verdana;"> was found on BT just by switching uniform to the non-uniform profile of fog microphysics depending on the channel, the droplet size, and optical thickness.</span></span></span></span>展开更多
基金美国NOAA和NASA GOES-R Algorithm Working Group和GOES-R Risk Reduction关于地球静止卫星资料模拟和同化项目
文摘首次将MSG-2(Meteosat Second Generation-2)卫星上的旋转增强可见光及红外成像仪(Spinning Enhanced Visible and Infrared Imager,SEVIRI)的观测资料同化到美国国家环境预报中心(National Centers for Environmental Prediction,NCEP)全球资料同化系统(global data assimilation system,GDAS)中。对当前的地球静止业务环境卫星(Geostationary Operational Environmental Satellite,GOES)成像仪资料的同化问题也进行了进一步探讨。利用CRTM(The Community Radiative Transfer Model)模式,对SEVIRI辐射率观测资料进行了模拟。为了对红外辐射率资料进行模拟,CRTM模式中的几个关键部分得到改进,例如:动态更新地面发射率资料以及采用了快速精确的气体吸收模块。为了改进对SEVIRI和GOES成像仪辐射率资料的模拟效果,采用了GSICS(The Global Space-Based Inter-Calibration System)标定订正。初步研究结果表明,包含对SEVIRI辐射率资料的水汽通道(6.25和7.35μm)和二氧化碳通道(13.40μm)的同化对GFS(Global Forecast System)6d预报具有显著的正影响;而对其他5个SEVIRI红外窗口通道资料的同化则减小了这种正影响。通过应用GSICS标定算法,订正了SEVIRI和GOES-12成像仪观测资料的偏差,提高了对GFS预报的影响。此外,还需作进一步研究来提高对SEVIRI红外窗口通道辐射率资料同化的有效性。
基金supported by The European Commission HORIZON 2020 Program ERA-PLANET/GEOEssential project[grant number 689443].
文摘Cloud computing facilities can provide crucial computing support for processing the time series of satellite data and exploiting their spatio-temporal information content.However,dedicated efforts are still required to develop workflows,executable on cloud-based platforms,for ingesting the satellite data,performing the targeted processes,and generating the desired products.In this study,an operational workflow is proposed,based on monthly Evaporative Stress Index(ESI)anomaly,and implemented in cloud-based online Virtual Earth Laboratory(VLab)platform,as a demonstration,to monitor European agricultural water stress.To this end,daily time-series of actual and reference evapotranspiration(ETa and ET0),from the Spinning Enhanced Visible and Infrared Imager(SEVIRI)sensor,were used to execute the proposed workflow successfully on VLab.The execution of the workflow resulted in obtaining one decade(2011–2020)of European monthly agricultural water stress maps at 0.04˚spatial resolution and corresponding stress reports for each country.To support open science,all the workflow outputs are stored in GeoServer,documented in GeoNetwork,and made available through MapStore.This enables creating a dashboard for better visualization of the results for end-users.The results from this study demonstrate the capability of VLab platform for water stress detection from time series of SEVIRI-ET data.
文摘The simplest way of building a look-up table (LUT) for the retrieval of cloud microphysical properties is to use a standard atmospheric profile and vertically uniform cloud microphysics. Such an assumption has been demonstrated to be incoherent with in-cloud observations. This paper aims to show the effect of some atmospheric conditions associated with fog as well as its macro-and microstructure on brightness temperature (BT) for the MSG/ SEVIRI satellite using libRadtran. The sensitivity tests were performed by gradually changing some features from the initial data, such as cloud cover, total water vapor column, thermal inversion intensity, fog depth, fog microstructure, and others. The results revealed that some variables can cause significant variations on BT and, consequently, discrepancies in the retrieval of fog microphysical properties. Also, a variation as high as 0.5<span><span><span style="font-family:;" "=""><span style="font-family:Verdana;"><span style="color:#4F4F4F;font-family:-apple-system, "font-size:16px;white-space:normal;background-color:#FFFFFF;">°</span>C</span><span style="font-family:Verdana;"> was found on BT just by switching uniform to the non-uniform profile of fog microphysics depending on the channel, the droplet size, and optical thickness.</span></span></span></span>
