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 at seasonal timescales. In this study, two sequential soil moisture analysis schemes are tested, both based on the comparison between observed and predicted 2-m parameters: the nudging technique used operationally at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the optimum interpolation technique proposed by J. F. Mahfouf and used operationally at Météo-France. Both techniques compute the soil moisture increments as a linear function of analysis increments of 2-m parameters (specific humidity at ECMWF, temperature and relative humidity at Météo-France). Following the preliminary study by Y. Hu et al., the optimum interpolation technique has been adapted to the four soil-level ECMWF land surface scheme. Both methods are tested in the ECMWF single column model, which has been run for 4 months in 1987 at a grid point close to the location of the First International Satellite Land-Surface Climatology Project Field Experiment. The upper-air variables are updated every 6 h using the ECMWF reanalysis. The surface downward radiation and precipitation fluxes are prescribed at each time step according to in situ observations. The soil moisture analysis is performed every 6 h, using either the nudging or the optimum interpolation. The nudging is shown to be very sensitive to model biases and sometimes produces unrealistic results. The optimum interpolation technique is more robust and reliable, due to the use of two screen-level parameters and a careful selection of the meteorological situations for which the atmosphere is expected to be informative about soil moisture. It leads to improved evaporation and soil moisture and is able to compensate for biases in both the land surface scheme and the precipitation forcing.