"Experimental assimilation of space-borne cloud radar and lidar observations directly in the 4D-Var system used at ECMWF"

The International Symposium on Data Assimilation Online (ISDA-Online) Feb 5, 2021
"Satellite Data Assimilation"
International Symposium on Data Assimilation - Online

Active observations from profiling instruments, such as cloud radar or lidar, contain a wealth of information on the structure of clouds and precipitation, but have never been assimilated directly in global numerical weather prediction (NWP) models. Currently there are no fully functioning space-borne radar or lidar instruments, but historical observations from CloudSat and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), part of the NASA A-train constellation, provide useful datasets for feasibility studies. In the next few years, new satellite missions with cloud radar and lidar are planned, such as EarthCARE (Earth, Clouds, Aerosol and Radiation Explorer; a joint mission between ESA and JAXA). In preparation for EarthCARE, whose data will be available in near-real time, the ECMWF 4D-Var system has been adapted to allow a direct assimilation of such type of observations.

In this presentation, several important developments required to prepare the data assimilation system for the new observations of cloud radar reflectivity and lidar backscatter will be summarized. This includes an observation operator providing realistic model equivalent to the observations. Another important aspect is the definition of the errors assigned to observations. Since the observation error of cloud observations is highly situation dependent, a flow-dependent error was designed to account for both the spatial representativity error due to the narrow field of view of these observations and the uncertainty in the microphysical assumptions. In addition, an appropriate quality control strategy and bias correction scheme are required for the proper handling of observations in the context of an assimilation system. All these components will be discussed in the presentation. Finally, results from 4D-Var experiments demonstrating the impact of cloud radar and lidar observations on the analysis and subsequent forecast will be presented, together with suggestions for increasing their impact on weather forecasts.