Construction and characteristic analysis of background error covariance coupled with land surface temperature

Land surface temperature (LST) is the key variable in land–atmosphere interaction, having an important impact on weather and climate forecasting. However, achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging. This is because there is limited knowledge about the cross-component background error covariance (BEC) between LST and atmospheric state variables. This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables, and whether this relationship varies spatially and temporally. To this end, the BEC coupled with atmospheric variables and LST was constructed (LST-BEC), and its characteristics were analyzed based on the 2023 mei-yu season. The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height, and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature. The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background, and also have obvious diurnal variations. These results provide valuable information for strongly coupled land–atmosphere assimilation.