Impact of WRF-3DVAR data assimilation on the prediction of rainfall over Southern Brazil

The procedure to combine mathematical models with noise data, in order to improve numerical weather forecasting by statistical methods, is an important and challenging meteorology research field, known as data assimilation. The 3DVAR approach, state of the art in data assimilation technique, is applied in this study. The aim of present development is to evaluate the results of the data assimilation from INMET automatic weather stations and atmospheric soundings in the weather forecast produced by WRF model. The region of interest is the South of Brazil. The specific aim is to evaluate the assimilation procedure of two precipitation events occurred in 2012. This study is especially important, because the INMET automatic weather stations data are not transmitted by GTS. Therefore, these data were not assimilated by prediction systems generated by global models, such as GFS, which provides initial and boundary conditions for regional models, such as WRF. The results showed that the WRF with data assimilation procedure, reproduces satisfactorily the true synoptic scenario given by GFS model in the two cases evaluated, and produces better forecasts then WRF without data assimilation. The thermodynamic analysis showed that the WRF with data assimilation producing vertical profiles of air temperature and dew point temperature very close to the observed profiles. Additional experiments indicate that data assimilated from other sources, in addition to the INMET automatic weather stations and atmospheric soundings, as well as the increases of horizontal resolution in the integration of the WRF with inclusion of subset, provide significant improvements in weather forecasting fields.