J21.2
ECMWF land surface analysis

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Tuesday, 25 January 2011: 1:45 PM
ECMWF land surface analysis
611 (Washington State Convention Center)
Patricia de Rosnay, ECMWF, Reading, United Kingdom; and G. Balsamo, L. Isaksen, J. Muņoz Sabater, M. Drusch, K. Scipal, C. Albergel, J. C. Calvet, and R. Essery

Land surface processes and their initialization are of crucial importance to address the challenge of seamless Numerical Weather Prediction. In particular it is expected to be of high interest to assimilate new satellite data from low frequency active and passive microwave sensors which provide suitable information for soil moisture analysis.

This presentation addresses the combined use of conventional observations and satellite data in the ECMWF land surface analysis system. It presents major changes implemented in 2009-2010 in the operational surface analysis of the ECMWF's Integrated Forecasting System (IFS).

The operational snow analysis was revised in 2010, to use an Optimum Interpolation (OI) in which SYNOP data and high resolution (4km) snow cover information from NOAA/NESDIS (National Oceanic & Atmospheric Administration / National Environmental Satellite, Data, and Information Service) are combined. It was also improved to include SYNOP snow depth data monitoring and quality control. The new analysis is evaluated in terms of snow field representation, including point scale evaluation, and in terms of impact on screen level parameters and atmospheric parameters. It has a positive impact on both the screen level scores and atmospheric scores.

An Extended Kalman Filter (EKF) soil moisture analysis has been implemented in 2010 to replace the previous Optimum Interpolation soil moisture analysis. An extensive ECMWF soil moisture validation is presented, based on SYNOP screen level parameters data assimilation indicating improved skills of the IFS to represent soil moisture dynamics. Investigations to use satellite data for the soil moisture analysis are also presented in this paper. Monitoring of METOP/ASCAT (Advanced SCATerometer) surface soil moisture data, from active microwave remote sensing, was implemented in operations in 2009. Long term data assimilation results of ASCAT surface soil moisture in the IFS are presented. Current developments conducted in the framework of the SMOS (Soil Moisture and Ocean Salinity) mission to use passive microwave for soil moisture analysis are presented. Monitoring results and statistics are presented showing a clear improvement of the SMOS data quality during the commissioning phase. Developments concerning SMOS data assimilation in the ECMWF EKF are also presented.