Characteristics of Covariances Between Land and Low-Atmosphere States and Their Influences on Coupled Land-Atmospheric Data Assimilation

Coupled land-atmosphere data assimilation faces some difficulties, including differences in the time scales of land and atmospheric variables, large uncertainties in the simulated cross-component covariances (and error covariances), and the potential for transmission of biases between components. In this study, we examine the observed and simulated covariances between time series of land and low-level atmosphere states, and use the results to guide the design of an assimilation strategy for coupled land/atmosphere data assimilation. Specifically, we conduct comprehensive studies to examine the linkage and error covariances between soil moisture and near-surface atmospheric variables (e.g., 2-m temperature and humidly and 10-m winds). First, we use in-situ data over 8-year to investigate the correlations between the soil moisture and near-surface variables. Second, a single column model was used to evaluate the influences of the changes in soil moisture on numerical forecasts of these near-surface variables. Finally, characteristics of error covariances between soil moisture and atmospheric variables were further investigated, and the soil moisture data assimilation was performed with Noah land-surface model. The influence of soil moisture data assimilation on short-range weather forecasts in the low-level atmosphere using the mesoscale community Weather Research and Forecasting (WRF) model, as well as the impact of error covariances between land and atmospheric variables are investigated. Different coupling methods are also examined with WRF model and Noah land-surface model.