Direct 4d-Var Assimilation Of Space-Borne Cloud Radar And Lidar Observations. Part Ii: Impact On Analysis And Subsequent Forecast

Observations related to cloud, such as radiances from microwave imagers, have been at the forefront of recent developments in data assimilation for numerical weather prediction (NWP). While they offer unrivalled spatial coverage, they contain limited information on the vertical structure of clouds. In contrast, active observations from profiling instruments such as cloud radar and lidar contain a wealth of information on the structure of clouds and precipitation, providing the much-needed vertical context of clouds, but have never been assimilated directly in global NWP models. To explore the potential benefits of these profiling observations, the European Centre for Medium-Range Weather Forecasts (ECMWF) Four-Dimensional Variational (4D-Var) data assimilation system has been recently adapted to allow direct assimilation of cloud profile observations from space-borne radar and lidar instruments. In this paper, in conjunction with its companion paper, the first-time direct assimilation of cloud radar and lidar observations into a global NWP model is demonstrated. Using CloudSat radar reflectivity and CALIPSO attenuated backscatter shows that the assimilation brings the analysis closer to these observations and has a mainly neutral affect on other assimilated observations. Some improvements in the forecast skill are also observed when verified against the experiment's own analysis, with the largest positive impact noticed for temperature at the lowest model levels and for vector wind above 500 hPa, but longer experiments are required to reach 95% statistical significance of the results. The potential improvements in the model radiation budget is explored by verifying with Clouds and the Earth's Radiation Energy System (CERES) observations. Sensitivity of the results to observation error and to the observation reduction by increased averaging is also discussed. The demonstration of statistically significant improvements to forecast skill in some metrics without any significant degredation in others shows great promise for the future use of cloud radar and lidar observations in NWP.