Direct 4D‐Var assimilation of space‐borne cloud radar and lidar observations. Part II: Impact on analysis and subsequent forecast. (15th September 2020)

• Record Type: Journal Article
• Authors: Janisková, Marta; Fielding, Mark D.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 146:Number 733(2020)
• Page Start: 3900
• Page End: 3916
• Publication Date: 2020-09-15
• Subjects: cloud radar reflectivity -- lidar backscatter -- variational technique; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3879
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : 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. Abstract : This paper presents the impact of the assimilation of CloudSat radar and CALIPSO lidar observations into a numerical weather prediction model using the ECMWF Integrated Forecast System. The example here shows the impact of including the new observations on 120‐hr forecast skill of temperature (left panel) and vector wind (right panel) for the period from 1 August to 31 October 2007. Results are shown as zonal mean of the normalised difference in the rms of the forecast error between the experimental run assimilating cloud radar and lidar observations and the reference. Each experiment has been verified against its own analysis, and negative numbers indicate a reduction in the forecast errors from adding the radar and lidar observations. Hatching indicates statistical significance at the 95% level. A reduction (resp. increase) of rms errors for the experimental run is indicated by blue (resp. red) shading.