A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
نویسندگان
چکیده
Abstract. Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors can only sample the effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔTsb) on daily LST (Tdm) estimated traditional method, which uses averages of clear-sky observations directly Tdm. Several methods been proposed estimation Tdm, yet they becoming less capable generating spatiotemporally seamless Tdm across globe. Based MODIS reanalysis data, here we propose an improved annual diurnal temperature cycle-based framework (termed IADTC framework) to generate products ranging 2003 2019 (named GADTC products). The validations show that reduces ΔTsb significantly. When validated in situ assessments absolute errors (MAEs) 1.4 1.1 K SURFRAD FLUXNET respectively, biases both close zero. Direct comparisons between measurements indicate MAEs 2.2 3.1 datasets, −1.6 −1.5 these two respectively. By taking references, further analysis reveals averaging method yields positive greater than 2.0 low-latitude midlatitude regions while relatively small value high-latitude regions. Although global-mean trend (2003 2019) calculated is (both 0.025 0.029 yr−1), discrepancies do occur – pixel-based MAE reaches 0.012 yr−1. We consider guide optimization globe, generated should be valuable warming freely available at https://doi.org/10.5281/zenodo.6287052 (Hong et al., 2022).
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ژورنال
عنوان ژورنال: Earth System Science Data
سال: 2022
ISSN: ['1866-3516', '1866-3508']
DOI: https://doi.org/10.5194/essd-14-3091-2022