Potential Use of Surface-Sensitive Microwave Observations Over Land in Numerical Weather Prediction

This paper describes several sensitivity studies carried out with the French global 4-D-Var system to check its ability to assimilate surface-sensitive observations over land from the Special Sensor Microwave Imager (SSM/I). As well as a sound knowledge of land-surface parameters, the assimilation of SSM/I observations requires effective rain-detection and bias-correction algorithms. Three sensitivity components are hence analyzed with a special emphasis on the land-surface emissivity at SSM/I frequencies estimated from satellite observations. Several rain algorithms were tested to reject cloudy/rainy observations over land, and the bias-correction scheme was adapted to improve its performance over land and sea surfaces. Once these problems have been outlined, a global 4-D-Var assimilation experiment which assimilates SSM/I observations over land surfaces was run and compared with a control experiment. The impact on forecast scores has been found to be globally positive. Nevertheless, the very high sensitivity of SSM/I to each of the three components presented in this study is characterized by opposite effects that, once clustered together, lead to some residual biases over land due to their combined effects.