Characterization of daily variation of CMA-MESO background error and its application

Background error covariance (BEC) is a crucial component in the variational data assimilation frameworks. Constructing a BEC that more closely represents reality is essential for enhancing the assimilation and forecasting capabilities of numerical prediction systems. Based on the CMA-MESO kilometer-scale regional numerical prediction system, the diurnal variation of BEC parameters is analyzed, and the variational parameters are applied to actual assimilation and forecasting experiments to explore their impact. The ensemble method was used to calculate background error samples, and BEC parameters were statistically analyzed at four times of the day (00 UTC, 06 UTC, 12 UTC, and 18 UTC) to investigate their diurnal variation. The results show that the root mean square (RMS) error and spatial correlation scale of the background error for various variables exhibit clear diurnal variation features in the lower and middle layers of the troposphere. The RMS of the background error in the wind field and humidity field is generally larger at night (12 UTC, 18 UTC) than during the day (00 UTC, 06 UTC), with the maximum values occurring at 12 UTC. For the temperature field, the RMS of the background error is larger at 06 UTC and 12 UTC, with more pronounced variations below 850 hPa. Regarding horizontal correlation, larger correlation scales are observed at 18 UTC and 00 UTC, while smaller correlation scales are found at 06 UTC and 12 UTC when vertical convective mixing is stronger. As for vertical correlation coefficients, the diurnal variation is most prominent at 06 UTC, with smaller differences at other times. The idealized experiment results demonstrate that the newly estimated diurnal variation parameters can adjust the influence weights and propagation distances of observational information at different times, ensuring that the assimilation analysis matches the diurnal variation characteristics of BEC. A month-long assimilation and forecasting cycle experiment shows that using the diurnal variation BEC parameters reduces the assimilation analysis errors for wind and temperature fields, improves precipitation forecasts, especially for heavy rain and thunderstorms, and also reduces 2-meter surface temperature forecast errors.