| Summary: | Downward longwave radiation (DLR) is a critical parameter for radiation balance, energy budget, and water cycle studies at regional and global scales. Accurate estimation of the all-weather DLR with a high temporal resolution is important for the estimation of the surface net radiation and evapotranspiration. However, most DLR products involve instantaneous DLR estimates based on polar orbiting satellite data under clear-sky conditions. To obtain an in-depth understanding of the performances of different models in the estimation of DLR over the Tibetan Plateau, which is a focus area of climate change study, this study tests eight methods for clear-sky conditions and six methods for cloudy conditions based on ground-measured data. It is found that the Dilley and O’Brien model and the Lhomme model are most suitable for clear-sky conditions and cloudy conditions, respectively. For the Dilley and O’Brien model, the average root mean square error (RMSE) of DLR under clear-sky conditions is approximately 22.5 W/m<sup>2</sup> for nine ground sites; for the Lhomme model, the average RMSE is approximately 23.2 W/m<sup>2</sup>. Based on the estimated cloud fraction and meteorological data provided by the China Land Surface Data Assimilation System (CLDAS), hourly all-weather daytime DLR with a 0.0625° resolution over the Tibetan Plateau is estimated. Results demonstrate that the average RMSE of the estimated hourly all-weather DLR is approximately 26.4 W/m<sup>2</sup>. With the combined all-weather DLR model, the hourly all-weather daytime DLR dataset with a 0.0625° resolution from 2008 to 2016 over the Tibetan Plateau is generated. This dataset can contribute to studies associated with the radiation balance and energy budget, water cycle, and climate change over the Tibetan Plateau.
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