A Temperature Refinement Method Using the ERA5 Reanalysis Data

Abstract

Air temperature is an important parameter in the research of meteorology, environment, and ecology. Obtaining accurate temperature values with high spatial–temporal resolution is the premise for regional climate monitoring and analysis and is also the basis for the calculation of various ecological and environmental factors. In this study, we proposed a temperature refinement method using the ERA5 reanalysis data, which constructed the correlation between the measured temperature derived from weather stations and the interpolated temperature based on the artificial neutral network (ANN) model. Experiments in a high-intensity coal mining area in China were conducted, and the root mean square error (RMSE) and compound relative error (CRE) were adopted as the statistical values in the internal and external accuracy tests. Numerical results showed that the proposed temperature refinement method outperformed the traditional interpolated method with an approximately 42% and 33% RMSE improvement in the internal and external accuracy test, respectively. Moreover, the proposed method effectively improved the geographic differences of the traditional method and obtained temperature estimates with high accuracy at arbitrary sites.

Keywords

• temperature
• ERA5
• artificial neural network