One of the big challenges in numerical weather prediction is to reduce the uncertainty in the initial conditions. At the National Meteorological Service (SMN) of Argentina, many efforts have been carried out to address this issue. In this work, the regional Local Ensemble Transform Kalman Filter coupled with the Weather Research and Forecasting model (WRF-LETKF) system is evaluated. The domain covers most of Southern South America with an horizontal resolution of 40 km and a 2 month period is tested (November and December 2012). A 40 member ensemble is used to assimilate conventional and satellite observations.

In this work a multi physics ensemble that includes different choices for the cumulus and planetary boundary layer parameterizations is evaluated. This experiment shows that, overall, the multi physics approach produce better results than a single physics configuration. The inclusion of boundary perturbations has also been explored although, it does not produce a significant impact in the current experimental settings. In addition, we explore the sensitivity to the assimilation of the Atmospheric Infrared Sounder (AIRS) temperature and moisture retrievals. The results indicate that the inclusion of these retrievals is a valuable alternative to deal with the scarcity of radiosondes observations in Southern South America. Finally, a comparison among the different WRF-LETKF ensemble mean forecasts and deterministic WRF forecasts
initialized from the GFS (Global Forecast System) without assimilation, was carried on. Generally a positive impact of the data assimilation technique was achieved, although it was found that the regional system needs to keep large scale information from the global model.