In this study, a rainstorm of the type experienced on 20 July 2021 over central East China was simulated using the first-generation Chinese Reanalysis datasets and Global Land Data Assimilation System datasets, and the Noah land surface model coupled with the advanced weather research and forecasting model. Based on this, the gridded planetary boundary layer (PBL) profiles and ensemble states within soil perturbations were collected to investigate the typical land–atmosphere coupling chain during this modeled rainstorm by using various local coupling metrics and introduced ensemble statistical metrics. The results show that (1) except for the stratospheric thermodynamics and the surface temperature over mountain areas, the main characteristics of the mid-low atmospheric layers and the surface have been well captured in this modeled rainstorm; (2) the typical coupling intensity is characterized by the dominant morning moistening, an early afternoon weak PBL warming factor of around 2, a noontime buoyant mixing temperature deficit around 274 K, daytime PBL and surface latent flux contributions of around 100 and 280 W/m2, respectively, and significant afternoon soil-surface latent flux coupling; and (3) an overall negative soil–rainfall relationship can be identified from the ensemble metrics in which the moist static energy is more significant than PBL height, and this is consistent with the significance of daytime surface moistening indicated by local coupling metrics. Taking the multi-process chain in chronological order, the wet soil contributes greatly to daytime moisture evaporation, which then increases the early noon PBL warming and enhances the noon period buoyant mixing within weak moist heating; however, this is suppressed by large-scale forcing such as the upper southwestern inflows of rainstorms, which further significantly shapes the spatial distribution of the statistical metrics. These quantitatively described local daytime couplings highlight the potential local application of promoting public weather forecasting efforts, while the high spatial differences in the coupling indicate the more applicable threshold diagnoses within finer-scale spatial investigations.
Read full abstract