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RSS FeedsRemote Sensing, Vol. 14, Pages 2480: Development and Evaluation of a Real-Time Hourly One-Kilometre Gridded Multisource Fusion Air Temperature Dataset in China Based on Remote Sensing DEM (Remote Sensing)

 
 

22 may 2022 09:58:10

 
Remote Sensing, Vol. 14, Pages 2480: Development and Evaluation of a Real-Time Hourly One-Kilometre Gridded Multisource Fusion Air Temperature Dataset in China Based on Remote Sensing DEM (Remote Sensing)
 


High-resolution gridded 2 m air temperature datasets are important input data for global and regional climate change studies, agrohydrologic model simulations and numerical weather predictions, etc. In this study, the digital elevation model (DEM) is used to correct temperature forecasts produced by ECMWF. The multi-grid variation formulation method is then used to fuse the data from corrected temperature forecasts and ground automatic station observations. The fused dataset covers the area over (0–60°N, 70–140°S), where different underlying surfaces exist, such as plains, basins, plateaus, and mountains. The spatial and temporal resolutions are 1 km and 1 h, respectively. The comparison of the fusion data with the verification observations, including 2400 weather stations, indicates that the accuracy of the gridded temperature is superior to European Centre for Medium-Range Weather Forecasts (ECMWF) data. This is because a more significant number of stations and high-resolution terrain data are used to generate the fusion data than are utilized in the ECMWF. The obtained dataset can describe the temperature feature of peaks and valleys more precisely. Due to its continuous temporal coverage and consistent quality, the fusion dataset is one of China’s most widely used temperature datasets. However, data uncertainty will increase for areas with sparse observations and high mountains, and we must be cautious when using data from these areas.


 
73 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 2482: Evapotranspiration Seasonality over Tropical Ecosystems in Mato Grosso, Brazil (Remote Sensing)
Remote Sensing, Vol. 14, Pages 2483: Pair-Wise Similarity Knowledge Distillation for RSI Scene Classification (Remote Sensing)
 
 
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