Abstract
Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.
Highlights
Climate change poses a substantial challenge to agriculture and food security, water availability and quality [1,2,3,4]
The mean air temperatures of both HRCLDAS and ERA5 capture the broad features of spatial distribution
The daily mean temperatures of HRCLDAS and ERA5 are basically consistent with the observations in spatial distribution and seasonal variation characteristics
Summary
Climate change poses a substantial challenge to agriculture and food security, water availability and quality [1,2,3,4]. High-quality meteorological forcing datasets are the essential basis for understanding the characteristics and trends in extreme weather events and can effectively improve the accuracy of crop models [13,14]. The development of data assimilation and fusion techniques provides an effective way to assimilate ground station observation data, remote sensing information and numerical weather forecast data [21,22]. Based on these techniques, different sources of meteorological data are fused to produce spatial–temporal and long time series of gridded fusion datasets, which can make up for the shortcomings of different sources of data [13]. Many gridded fusion forcing datasets are available, including the Global Land Data Assimilation System (GLDAS) [23], the European
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