4-DIMENSIONAL VARIATIONAL ASSIMILATION OF SSM I PRECIPITABLE WATER-CONTENT DATA/

Satellite microwave radiometers provide measurements of precipitable w ater content (PWC) over the oceans, with a horizontal resolution of a few tens of kilometres. These data represent the water vapour content integrated over the atmospheric column. We assess the value of this so urce of information for numerical weather prediction systems. We used the European Centre for Medium-Range Weather Forecasts Integrated Fore casting System with a four-dimensional variational method to assimilat e the Special Sensor Microwave/Imager (SSM/I) PWC data over a 24-hour period. Our experiments use an incremental variational method. Compari son with observations is made using a multi-level global primitive-equ ation T106 spectral model with physical parametrizations. Minimization is performed using a T63 adiabatic dynamics model which includes only simplified physics (horizontal diffusion, a simple surface drag and a vertical diffusion scheme). Comparing the control and assimilation ex periments with aircraft and other data shows that the use of PWC data from SSM/I improves the analysis. We also obtain a slight improvement in short-range forecasts of almost all parameters when SSM/I-PWC data are used in the assimilation.