The data assimilation technique, known as 3DVAR, of the WRF mesoscale modeling system has been used in order to perform the impact analysis of meteorological data assimilation in the weather forecasts over the Rio Gra...The data assimilation technique, known as 3DVAR, of the WRF mesoscale modeling system has been used in order to perform the impact analysis of meteorological data assimilation in the weather forecasts over the Rio Grande do Sul State in Brazil. The consistency of the data assimilation has been analyzed by investigating and evaluating the model forecast results processed with and without data assimilations. Two different procedures of data assimilation have been conducted to perform the study. The forecasts of the accumulated rainfall model variable, spatially plotted over the model integration domains, have been compared and validated against the Tropical Rain Measuring Mission (TRMM) satellite based data, as well as with the Canguçu city meteorological radar reflectivity data. The comparison has been made considering the total amount of the accumulated rainfall predicted by the model against the automatic weather station data and most of the conducted processing presented compatible results. It has also been observed that, the inclusion of assimilated data enabled an improvement in the intensity as well as in the location of the main convective cell. The radar reflectivity field showed a significant performance in all processed experiments with data assimilation. However, for some regions, more significant obtained results have been shown to be the case in which the spectral radiances were assimilated, as compared with the case in which the spectral radiances were not included. The evaluation of the vertical atmospheric profiles of temperature and dew point temperature showed only a small impact of data assimilation. However, both simulations coherently presented the two vertical profiles, when compared with the observed profiles. In short, the study shows that, although the forecasts presented some inconsistencies in the evaluated results, the 3DVAR assimilation improves significantly the forecasting of the Weather WRF model.展开更多
文摘The data assimilation technique, known as 3DVAR, of the WRF mesoscale modeling system has been used in order to perform the impact analysis of meteorological data assimilation in the weather forecasts over the Rio Grande do Sul State in Brazil. The consistency of the data assimilation has been analyzed by investigating and evaluating the model forecast results processed with and without data assimilations. Two different procedures of data assimilation have been conducted to perform the study. The forecasts of the accumulated rainfall model variable, spatially plotted over the model integration domains, have been compared and validated against the Tropical Rain Measuring Mission (TRMM) satellite based data, as well as with the Canguçu city meteorological radar reflectivity data. The comparison has been made considering the total amount of the accumulated rainfall predicted by the model against the automatic weather station data and most of the conducted processing presented compatible results. It has also been observed that, the inclusion of assimilated data enabled an improvement in the intensity as well as in the location of the main convective cell. The radar reflectivity field showed a significant performance in all processed experiments with data assimilation. However, for some regions, more significant obtained results have been shown to be the case in which the spectral radiances were assimilated, as compared with the case in which the spectral radiances were not included. The evaluation of the vertical atmospheric profiles of temperature and dew point temperature showed only a small impact of data assimilation. However, both simulations coherently presented the two vertical profiles, when compared with the observed profiles. In short, the study shows that, although the forecasts presented some inconsistencies in the evaluated results, the 3DVAR assimilation improves significantly the forecasting of the Weather WRF model.
