Abstract

A new ensemble Kalman filter based land data assimilation system is developed, which can be used to assimilate satellite-borne passive microwave brightness temperature data to update soil moisture and temperature. Fully driven by a regional atmospheric model, the assimilation system can provide new surface fluxes to the atmosphere and gets back its updated forcing. The system uses a new land surface model based on the Simple Biosphere Model, which is currently under development at the Japan Meteorological Agency. The observation operator is a radiative transfer model, which can estimate the land surface brightness temperature. Currently the Q/h model is used to estimate the soil brightness temperature. The data assimilation method employed is the ensemble Kalman filter technique, which is a Monte Carlo based sequential filter method. The atmospheric driver is the Advanced Regional Prediction System which is a 3-dimensional non-hydrostatic atmospheric model. The system was tested on a regional scale over North-East Asia (0 to 60 deg. North, 45 to165 deg. East) for a two weeks period during the 2003 Monsoon season using NCEP/GFS-FNL global data as boundary and initial conditions. Forcing from the atmospheric model drives the land surface model. Satellite brightness temperature observations from the Advanced Microwave Scanning Radiometer are compared to the simulated ones through the assimilation process to update the surface state and feed back the new simulated fluxes into the atmospheric model. A control run using only ARPS nested from the global model data without land surface assimilation is used for comparison. Furthermore the new land surface conditions and fluxes are compared to in-situ observation collected during the Coordinated Enhanced Observation Period. The results showed significant differences compared with standard regional atmospheric model outputs, and it is shown that the system can estimate land surface states more reasonable than uncontrolled modeling by merging the brightness temperature observations into land surface dynamics