Development of strategies for radar and lidar data assimilation

Title
Development of strategies for radar and lidar data assimilation
Report
Date Published
03/2010
Series/Collection
WP-3100 Report for ESA contract 1-5576/07/NL/CB: Project QuARL - Quantitative Assessment of the Operational Value of Space-Borne Radar and Lidar Measurements of Cloud and Aerosol Profiles
Document Number
2010:WP-3100 contract 1-5576/07/NL/CB
Author
Marta Janiskova
O. Stiller
Event Series/Collection
ESA Contract Report
Abstract Profiting from the enormous wealth of highly vertically resolved data as they are obtained from spaceborne radar and lidar poses a great challenge for the NWP community which, if successful, could lead to great improvements of our forecast accuracy and modelling capabilities. Particularly the large uncertainty of the vertical distribution of clouds is a longstanding problem of current NWP systems which affects both data assimilation and parametrisation developments. Also the vertical structure of aerosols is notoriously underconstrained by current data assimilation systems which contrasts the great interest which these quantities have recently obtained in the context of air quality monitoring and forecasting. This report explores possibilities to exploit these new data types in the context of an NWP system by including them into the 1D-Var system. This approach gives a good indication to which extent variational data assimilation methods can deal with the new data type and also lays the technical foundations for a future inclusion into the full 4D-Var system. More specifically, 1D-Var retrievals of clouds from CloudSat and aerosols from CALIPSO have been performed. Both assimilations were very successful in producing assimilated states whose model-equivalent observations are substantially closer to the real observations. Cloud retrievals substantially incremented both the humidity and the temperature field while aerosol observations updated the aerosol field considerably. Independent observations confirmed that the cloud retrievals bring the model state closer towards that of the true atmosphere.
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