首页|Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES-16 satellite

Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES-16 satellite

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Surface upward longwave radiation (SULR) is one of the four com-ponents of the surface radiation budget,which is defined as the total surface upward radiative flux in the spectral domain of 4-100 μm.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) Iongwave 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.

Surface upward longwave radiationAdvanced Baseline ImagerGOES-16hybrid methodkernel-driven model

Boxiong Qin、Biao Cao、Zunjian Bian、Ruibo Li、Hua Li、Xueting Ran、Yongming Du、Qing Xiao、Qinhuo Liu

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State Key Laboratory of Remote Sensing Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,China

College of Resources and Environment,University of Chinese Academy 15f Sciences,Beijing,China

College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao,China

School of Resources and Environment,University of Electronic Science and Technology of China,Chengdu,China

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This work was supported in part by The National Key Research and Development Program of ChinaNational Natural Science of Foundation of ChinaNational Natural Science of Foundation of ChinaNational Natural Science of Foundation of ChinaNational Natural Science of Foundation of ChinaYouth Innovation Promotion Association CASand The"Future Star"Talent Plan of the Aerospace Information Research Institute of Chinese Academy of Sciences

2018YFA0605503418712584193011141901287420713172020127Y920570Z1F

2021

地球大数据(英文版)

地球大数据(英文版)

ISSN：
年,卷(期)：2021.5(2)
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