On the impact of Doppler radar derived wind fields in a mesoscale non-hydrostatic model

Abstract

Fine-scale three-dimensional wind fields retrieved from airborne Doppler radar observations within an oceanic tropical mesoscale convective system (MCS), during the Tropical Ocean/Global Atmosphere Coupled Ocean-Atmosphere Response Experiment, are used to enhance the initial conditions of a n on-hydrostatic mesoscale model. They are incorporated into the French CANARI-ALADIN analysis system, based on the optimal interpolation technique. The fine-mesh (4 km) horizontal wind components are first averaged over subdomains of 20 km × 20 km in order to provide a series soscale wind profiles. Because of the absence of dense sounding data, the synthesized vertical-velocity profiles are transformed into humidity profiles by considering that updraughts contain nearly saturated or saturated air, while downdraughts are associated with unsaturated air. Comparison of the initial state using conventional data with the Doppler-enhanced initial state clearly identifies the benefits of the addition of the observed mesoscale circulation features. In terms of the precipitation forecast, the control run that uses the conventional initial State dramatically fails to predict the existence of the MCS precipitation core. On the other hand, the reference run with the data-enhanced initial state succeeds in forecasting it up to 12 hours in a way that is well consistent with the satellite imagery. In terms of the wind forecast, the mesovortex signature that could be identified from the radar observations becomes a persistent feature with the radar-enhanced initial conditions. Sensitivity tests reveal the specific roles of wind and humidity data. As in previous studies, the initial state of humidity is fundamental for the forecast of the MCS precipitation by sustaining the convective activity, and also by forming and maintaining a vortex-like circulation. These results suggest that moist convective processes play a major role. The inclusion of wind data is necessary for improving the system propagation by advective processes.