This project has ended | -
STSE (Support to Science Element) Study – EarthCARE Assimilation
The aim of the project was to provide an extensive scientific exploration of assimilation potential of the EarthCARE (Earth, Clouds, Aerosols and Radiation Explorer) observations beneficial for the NWP (Numerical Weather Prediction) community in preparation for the future pre-operational use.
The EarthCARE research satellite, selected for deployment through collaboration of ESA (European Space Agency) and JAXA (Japan Aerospace Exploration Agency), will carry, among other instruments, a lidar (ATLID) and a cloud radar (CPR), thus providing observations of the vertical structure and the horizontal distribution of cloud and aerosol together with the outgoing radiation at all climate zones. A number of studies including the ESA-funded project Quantitative Assessment of the Operational Value of Space-Borne Radar and Lidar Measurements of Cloud and Aerosol Profiles (QuARL, Janisková et al., 2010) carried out during 2009-2010 at ECMWF, have shown that such observations are useful not only to evaluate the performance of current NWP models in representing clouds, precipitation and aerosols, but also for their potential to be assimilated into these models to improve the initial atmospheric state.
The main objective of the 2-year ESA funded STSE Study - EarthCARE Assimilation has been the development of an off-line data assimilation and monitoring system to exploit the potential of space-borne radar and lidar cloud observations for NWP models. Testing of the developed systems was done using CloudSat and CALIPSO data since these are currently the only available real data of similar type with the coverage over the globe. The work performed during this project is also a preparation for the use of similar data from the future EarthCARE mission.
Initially, the project focused mainly on improving the radar observation operator, the development of a lidar forward operator as well as tools for handling observations such as data selection (i.e. quality control and data screening) and bias correction. Later, observation errors such as instrument, forward modelling and representativity (due to the narrow field of view of the space-borne lidar and radar instruments) were estimated. Finally, substantial effort has been put in the development of the off-line systems for monitoring and assimilation of both cloud radar and lidar data.
A basic framework for monitoring time series of cloud radar and lidar observations has been established based on time series of cloud observations from the CloudSat radar, the CALIPSO lidar and the corresponding reflectivity, respective backscatter first guess (FG) departures simulated from the ECMWF model. Experiments have been performed to assess the ability of the monitoring system to detect potential problems in the quality of observations. For cloud radar, the skill of a monitoring system to detect a degradation in the quality of observations is improved when the first guess departures are used compared to using CloudSat observations alone. For lidar, the study has indicated that the monitoring of differences between observations and the equivalent model quantities does not lead to earlier detection.
In order to investigate the potential that assimilation of cloud information from active sensors could have for NWP models, a technique combining one-dimensional variational (1D-Var) assimilation with four-dimensional (4D-Var) data assimilation has been used. The 1D-Var system has been built to assimilate the set of CloudSat radar reflectivity and CALIPSO lidar backscatter observations either separately or in combination. Feasibility studies have then been performed where pseudo-observations of temperature and specific humidity retrieved from 1D-Var were assimilated in the ECMWF 4D-Var system to study the impact of the new observations on analyses (i.e. initial conditions for NWP models) and subsequent forecasts. Outcomes from assimilation experiments have shown that 1D-Var analyses get closer to assimilated and also independent observations. However, impact of the cloud radar reflectivity is larger than that of the lidar backscatter. The performed 1D+4D-Var assimilation experiments have indicated a positive impact of the new observations on the subsequent forecast.