Implementation of 1D+4D-Var assimilation of precipitation-affected microwave radiances at ECMWF. I: 1 D-Var

This paper presents the operational implementation of a 1D+4D-Var assimilation system of rain-affected satellite observations at the European Centre for Medium-Range Weather Forecasts. The first part describes the methodology and performance analysis of the 1D-Var retrieval scheme in clouds and precipitation that uses Special Sensor Microwave/Imager microwave radiance observations for the estimation of total-column water vapour. The second part shows the global and long-term impact of these observations on both model 4D-Var analyses and medium-range forecasts. The 1D-Var scheme employs a complex observation operator that consists of linearized moist physics parametrization schemes and a multiple-scattering radiative-transfer model. The observation operator shows rather linear behaviour in most situations except in the presence of very intense precipitation suggesting a possible use even for a direct assimilation of radiances in 4D-Var. A bias correction and observation-error estimation method were implemented and indicate stable error behaviour. The 1D-Var algorithm quality control shows the largest failure number in areas with mostly frozen precipitation where the Special Sensor Microwave/Imager channels have little sensitivity to changes in hydrometeor contents. From test analyses on a global scale, a small moisture increase was computed that was greatest in dry subtropical areas. Large-scale and convective precipitation were increased similarly but showed a significantly different geographical distribution. The large-scale precipitation scheme has a stronger sensitivity to moisture changes and therefore moisture increments mainly affect stratiform precipitation distributions. While the global mean moisture fields are only weakly affected by the assimilation of rain-affected observations, the impact on local systems may be quite large. The forecast of synoptic system development through the 4D-Var analysis can be significant.