The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(N...The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(New_FY3D)was constructed by the two global MLSE daily product datasets,clear-sky(FY-3D1)and clear/cloudy(FY-3D2),which were retrieved from the same FY-3D MicroWave Radiation Imager(MWRI)Level-1 brightness temperature(BT)data from 2021 to 2022,respectively.Then,a set of global MLSE label samples based on the New_FY3D,including 14 surface geophysical parameters,was obtained for an instantaneous global MLSE simulation at a 0.10°spatial resolution by adopting the extreme gradient boosting(XGBoost)machine learning method.Finally,the FengYun-3F(FY-3F)MWRI-II BT simulations using the Advanced Radiative Transfer Modeling System(ARMS)based on the above different MLSE products were evaluated.The results show that the New_FY3D atlas performs well,and the BT simulation at the top of atmosphere is better than that of FY-3D1,FY-3D2,and the international mainstream TELSEM2(Version 2.0 for a Tool to Estimate Land Surface Emissivities in the Microwaves)atlas.Surface roughness,vegetation coverage,land cover type,and snow cover are vital parameters for MLSE simulation.The XGBoost model can accurately simulate all-sky/all-surface MLSE instantaneously over the frequency range 10.65–89.0 GHz.The average simulation determination coefficients(R^(2))under clear-sky and cloud-sky conditions are 0.925 and 0.901,respectively,and the average root-mean-square errors(RMSEs)are 0.018 and 0.021,respectively.Large simulation errors occur in permanent wetland,ice and snow,and urban and built-up areas.With a standard deviation of 6.6 K,the BT simulation based on an XGBoost simulated MLSE is better than those based on New_FY3D and TELSEM2.展开更多
The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation i...The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation inaccurate on the TP.This paper introduces an indirect estimation scheme for the LWC profile obtained using a monochromatic radiative transfer model(MonoRTM)and microwave radiometers(MWRs)on the TP.The LWC estimation method was improved using an optimization of the difference between the simulated and observed brightness temperature(TB)at specific microwave channels that are sensitive to liquid water.The accuracy of the LWC estimation method depends heavily on the value of the cloud-base environment humidity criterion(CBEHC).Our experiment confirmed that the default CBEHC value of 95%is unsuitable for the TP.For the rainfall scenarios,the optimization method suggested the use of CBEHC values of 81%,76%,and 83%for Mangya,Nagqu,and Qamdo stations,respectively.The new CBEHC values produced a 30 K improvement in the TB simulation when compared to that of 95%CBEHC under rainfall conditions.This demonstrates the robustness of the LWC estimation scheme and its significant improvement in LWC estimation on the TP.For no-rainfall scenarios,the original Karstens model remained suitable for Nagqu station.An adjustment of the CBEHC to 94%for Mangya station resulted in a 1 K improvement of its TB simulation.Qamdo station had a 2.5 K improvement when the CBEHC was adjusted to 98%.The relationship between the simulated TB simulation error and the maximum relative humidity of the radiosonde profiles weakened after CBEHC optimization.Thus,the innovative method proposed in this article provides a practical estimation method for LWC in the TP region.This LWC estimation method has a higher potential for rainfall days than no-rainfall days.Under no-rainfall conditions,the accuracy of the proposed LWC estimation method is sensitive to TB errors included in its measurement and simulation.An accurate estimation of LWC for no-rainfall conditions relies more on the equipment and radiation model.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.U2242211)the Hunan Provincial Natural Science Foundation Major Project(Grant No.2021JC0009).
文摘The Microwave Land Surface Emissivity(MLSE)atlas and instantaneous simulation of all-sky/all-surface MLSE are important prerequisites for satellite data assimilation.A ten-day/month synthesized FengYun-3D MLSE atlas(New_FY3D)was constructed by the two global MLSE daily product datasets,clear-sky(FY-3D1)and clear/cloudy(FY-3D2),which were retrieved from the same FY-3D MicroWave Radiation Imager(MWRI)Level-1 brightness temperature(BT)data from 2021 to 2022,respectively.Then,a set of global MLSE label samples based on the New_FY3D,including 14 surface geophysical parameters,was obtained for an instantaneous global MLSE simulation at a 0.10°spatial resolution by adopting the extreme gradient boosting(XGBoost)machine learning method.Finally,the FengYun-3F(FY-3F)MWRI-II BT simulations using the Advanced Radiative Transfer Modeling System(ARMS)based on the above different MLSE products were evaluated.The results show that the New_FY3D atlas performs well,and the BT simulation at the top of atmosphere is better than that of FY-3D1,FY-3D2,and the international mainstream TELSEM2(Version 2.0 for a Tool to Estimate Land Surface Emissivities in the Microwaves)atlas.Surface roughness,vegetation coverage,land cover type,and snow cover are vital parameters for MLSE simulation.The XGBoost model can accurately simulate all-sky/all-surface MLSE instantaneously over the frequency range 10.65–89.0 GHz.The average simulation determination coefficients(R^(2))under clear-sky and cloud-sky conditions are 0.925 and 0.901,respectively,and the average root-mean-square errors(RMSEs)are 0.018 and 0.021,respectively.Large simulation errors occur in permanent wetland,ice and snow,and urban and built-up areas.With a standard deviation of 6.6 K,the BT simulation based on an XGBoost simulated MLSE is better than those based on New_FY3D and TELSEM2.
基金supported by the National Natural Science Foundation of China(Grant Nos.41975009 and U2442213).
文摘The cloud liquid water content(LWC)of the Tibetan Plateau(TP)is crucial for cloud water conversion.There are very few accurate observations of the LWC on the TP.This makes our estimation of the LWC and precipitation inaccurate on the TP.This paper introduces an indirect estimation scheme for the LWC profile obtained using a monochromatic radiative transfer model(MonoRTM)and microwave radiometers(MWRs)on the TP.The LWC estimation method was improved using an optimization of the difference between the simulated and observed brightness temperature(TB)at specific microwave channels that are sensitive to liquid water.The accuracy of the LWC estimation method depends heavily on the value of the cloud-base environment humidity criterion(CBEHC).Our experiment confirmed that the default CBEHC value of 95%is unsuitable for the TP.For the rainfall scenarios,the optimization method suggested the use of CBEHC values of 81%,76%,and 83%for Mangya,Nagqu,and Qamdo stations,respectively.The new CBEHC values produced a 30 K improvement in the TB simulation when compared to that of 95%CBEHC under rainfall conditions.This demonstrates the robustness of the LWC estimation scheme and its significant improvement in LWC estimation on the TP.For no-rainfall scenarios,the original Karstens model remained suitable for Nagqu station.An adjustment of the CBEHC to 94%for Mangya station resulted in a 1 K improvement of its TB simulation.Qamdo station had a 2.5 K improvement when the CBEHC was adjusted to 98%.The relationship between the simulated TB simulation error and the maximum relative humidity of the radiosonde profiles weakened after CBEHC optimization.Thus,the innovative method proposed in this article provides a practical estimation method for LWC in the TP region.This LWC estimation method has a higher potential for rainfall days than no-rainfall days.Under no-rainfall conditions,the accuracy of the proposed LWC estimation method is sensitive to TB errors included in its measurement and simulation.An accurate estimation of LWC for no-rainfall conditions relies more on the equipment and radiation model.
