| Popis: |
Temperature and precipitation are crucial indicators for investigating climate changes, necessitating precise measurements for rigorous scientific inquiry. While the Fifth Generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5), ERA5 of the Land Surface (ERA5-Land), and China Meteorological Forcing Dataset (CMFD) temperature and precipitation products are widely used worldwide, their suitability for the Altay region of arid and semi-arid areas has received limited attention. Here, we used the Altay region as the study area, utilizing meteorological station data and implementing the residual revision method for temperature and the coefficient revision method for precipitation to rectify inaccuracies in monthly temperature and precipitation records from ERA5-Land, ERA5, and CMFD. We evaluate the accuracy of these datasets before and after correction using bias, Taylor diagrams, and root-mean-square error (RMSE) metrics. Additionally, we employ Tropical Rainfall Measuring Mission satellite precipitation data (TRMM) as a benchmark to assess the performance of ERA5-Land, ERA5, and CMFD monthly precipitation before and after correction. The results revealed significant differences in the temperature and precipitation capture capabilities of ERA5-Land, ERA5, and CMFD in the Altay region. Overall, these data exhibit substantial errors and are not directly suitable for scientific research. However, we applied residual and coefficient revision methods. After this revision, ERA5-Land, ERA5, and CMFD showed significantly improved temperature and precipitation capture capabilities, especially for ERA5-Land. In terms of temperature, post-revision-CMFD (CMFDPR) demonstrated better temperature capture capabilities. All three datasets showed weaker performance in mountainous regions compared to plains. Notably, post-revision-ERA5 (ERA5PR) seemed unsuitable for capturing temperature in the Altay region. Concerning rain, CMFDPR, post-revision-ERA5-Land (ERA5-LandPR) and ERA5PR outperformed TRMM in capturing precipitation. CMFDPR and ERA5-LandPR both outperform ERA5PR. In summary, the revision datasets effectively compensated for the sparse distribution of meteorological stations in the Altay region, providing reliable data support for studying climate change in arid and semi-arid areas. |
