Operational assimilation of space-borne radar and lidar cloud profile observations for numerical weather prediction. Feasibility demonstration of 4D-Var assimilation system using CloudSat and CALIPSO observations

TitleOperational assimilation of space-borne radar and lidar cloud profile observations for numerical weather prediction. Feasibility demonstration of 4D-Var assimilation system using CloudSat and CALIPSO observations
Publication TypeReport
Date Published2018
Series/CollectionESA Contract Report
Document NumberWP-5000
AuthorsJaniskova, M, Fielding, M
Event Series/CollectionESA Contract Report
Abstract

This report describes the data assimilation system at European Centre for Medium Range Weather Forecasts (ECMWF) prepared for feasibility demonstration of direct inclusion of cloud radar and lidar observations into the Four-Dimensional Variational (4D-Var) system. It provides information on observations, their errors and the used observation operators. The necessary data handling for observations in the data assimilation system, such as quality control and bias correction is also briefly summarized.

4D-Var assimilation experiments have been performed using CloudSat (NASA's cloud radar mission) cloud radar reflectivity and CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) lidar backscatter observations, either separately or in combination. Obtained results indicate that 4D-Var
analyses get closer to assimilated observations. However, impact of the cloud radar reflectivity is larger than that of the lidar backscatter. Impact on the first-guess (FG) and analysis (AN) departure statistics when verified against other observation types assimilated in 4D-Var is most pronounced and
positive for conventional observations, especially for wind. For all other observations the observed impact is small. An impact of the new observations on the subsequent forecast is largest for zonal wind, relative to both operational analysis and radiosonde observations. Results also indicate an improved forecast of rain rates in the tropics by assimilating cloud radar and lidar observations.

URLhttps://www.ecmwf.int/node/18499
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