Impact of SSM/I Observations Related to Moisture, Clouds, and Precipitation on Global NWP Forecast Skill

Abstract

This paper presents the results from the Observing System Experiments (OSEs) with the current ECMWF data assimilation and modeling system for quantifying the impact on both analysis and forecast quality of Special Sensor Microwave Imager (SSM/I) observations sensitive to moisture and clouds as well as precipitation. SSM/I radiances have been assimilated operationally in clear-sky areas for 8 yr and in cloud- and rain-affected areas since June 2005. This paper examines experiments set up such that clear-sky and rain-affected observations were either added to a baseline with a restricted observing system configuration or withdrawn from the full system. The experiment duration was 10 weeks of which the first 14 days were excluded from the evaluation to allow the system to lose the memory of the initial conditions at day −1. It is shown that both clear-sky and rain-affected observations account for the bulk correction of moisture in the ECMWF analysis. SSM/I data adds 1 day of forecast skill over the first 48 h when evaluated in addition to a baseline-observing system. In the tropics, the rain-affected data contributes more skill to the moisture forecast than the clear-sky data at 700 hPa and above. In the Northern and Southern Hemispheres, the effect is generally weaker and slightly in favor of clear-sky observations. A similar performance can be seen with respect to the wind vector forecast skill, which reflects the connection between the analysis of moisture and dynamics.