Evaluation of the optimum interpolation and nudging techniques for soil moisture analysis using FIFE data

Initialization of land surface prognostic variables is a crucial issue for short- and medium-range forecasting as well as ar seasonal timescales. in t his study, two sequential soil moisture analysis schemes are tested. both b ased on the comparison between observed and predicted 2-m parameters: the n udging technique used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the optimum interpolation technique proposed by J. E Mahfouf and used operationally at Meteo-France. Both techniques co mpute the soil moisture increments as a linear function of analysis increme nts of 2-m parameters (specific humidity at ECMWF temperature and relative humidity at Meteo-France). Following the preliminary study by Y. Hu et ai., the optimum interpolation technique has been adapted to the four soil-leve l ECMWF land surface scheme. Both methods are tested in the ECMWF single co lumn model, which has been run for 4 months in 1987 at a grid point close t o the location of the First International Satellite Land-Surface Climatolog y Project Field Experiment. The upper-air variables are updated every 6 h u sing the ECMWF reanalysis. The surface downward radiation and precipitation fluxes are prescribed at each rime step according to in situ observations. The soil moisture analysis is performed every 6 h, using either the nudgin g or the optimum interpolation. The nudging is shown to be very sensitive t o model biases and sometimes produces unrealistic results. The optimum inte rpolation technique is more robust and reliable, due to the use of two scre en-level parameters and a careful selection of the meteorological situation s for which the atmosphere is expected to be informative about soil moistur e. It leads to improved evaporation and soil moisture and is able to compen sate for biases in both the land surface scheme and the precipitation forci ng.