The forecasting of convective precipitation is an important and challenging problem. In complex situations, intense convective cells generate gust fronts that propagate, interact and produce new convective elements. These gust fronts and convergence lines are driven primarily by the buoyancy field in the boundary layer. A very important process occurring in the boundary layer during these phenomena is the evaporation of rain in subsaturated air.

In a first step towards the use of a deep convection model with full microphysics, a parameterization of the precipitation evaporative cooling is added to a dry boundary layer model to improve the temperature field for 4D-Var radar data assimilation and forecasting. The evaporative cooling is parameterized using the precipitation deduced from the observed reflectivity and the air subsaturation. A first guess of temperature and humidity is obtained from a nearby sounding or from a mesoscale model analysis and surface data. The 3D structure of the humidity field is retrieved during the data assimilation and is time independent. During the forecast, the evaporative cooling is applied assuming steady state for humidity and the precipitation field is advected by the mid-level horizontal wind field.

The implementation of the evaporative cooling is tested first with simulated data from a cloud resolving model to measure the effect on analyzed temperature and humidity. Then, a real data case is studied. When the evaporative cooling is applied during the assimilation, colder pools and stronger divergence appear at the surface where there is precipitation, in better agreement with the surface observations and our knowledge of the phenomenon. The evaporative cooling seems to be critical for the forecast of the cold pool spreading and the associated leading convergence line. Indeed, a crude estimation of the precipitation and humidity fields is sufficient to improve dramatically the convergence line forecast.