Current state of radar data assimilation at Météo-France
Olivier CAUMONT, Véronique DUCROCQ, Éric WATTRELOT, Thibaut MONTMERLE
and Francois BOUTTIER
Météo-France/CNRS, France
Abstract
Météo-France is currently designing its next operational high-resolution nonhydrostatic numerical weather prediction model (named Arome). So as to improve quantitative precipitation forecasts, ground-based radar data will be assimilated into this model among other high-resolution data. Arome assimilation algorithm is based on the three-dimensional variational (3DVar) assimilation system of the Aladin suite.
For radar reflectivities, a 1D inversion using a Bayesian method which converts reflectivities into humidity columns is first applied in order to retrieve consistent moisture profiles. The retrieved columns are then assimilated with the 3DVar as pseudo-observations.
The presentation will focus on radar data and describe the new processing chain which was set up to handle raw radar data from the French network, the observation operators for reflectivities and radial winds, the 1D+3DVar approach which is used to assimilate reflectivities, and first results of assimilation experiments.