A Heuristic Approach for Precipitation Data Assimilation: Characterization Using OSSEs

Abstract

Abstract We introduce a new technique for the assimilation of precipitation observations, the localized ensemble mosaic assimilation (LEMA). The method constructs an analysis by selecting, for each vertical column in the model, the ensemble member with precipitation at the ground that is locally closest to the observed values. The proximity between the modeled and observed precipitation is determined by the mean absolute difference of precipitation intensity, converted to reflectivity and measured over a spatiotemporal window centered at each grid point of the model. The underlying hypothesis of the approach is that the ensemble members that are locally closer to the observed precipitation are more probable to be closer to the “truth” in the state variables than the other members. The initial conditions for the new forecast are obtained by nudging the background states toward the mosaic of the closest ensemble members (analysis) over a 30 min time interval, reducing the impacts of the imbalances at the boundaries between the different selected members. The potential of the method is studied using observing system simulation experiments (OSSEs) employing a small ensemble of 20 members. The ensemble is produced by the WRF Model, run at a horizontal grid spacing of 20 km. The experiments lend support to the validity of the hypothesis and allow the determination of the optimal parameters for the approach. In the context of OSSE, this new data assimilation technique is able to produce forecasts with considerable and long-lived error reductions in the fields of precipitation, temperature, humidity, and wind.