Impact of Radar Data Assimilation on a Severe Storm Study in Brazil

The west of Parana state of Brazil is a region which has a propitious environment to develop severe weather conditions with strong winds, heavy rainfall, lightning and flooding due to the influences of low level jets that bring humidity from tropical region, mainly. Every year wind gusts cause damages on many high structures such as power transmission lines and towers. To measure these severe weather conditions, the Paraná Meteorological System (SIMEPAR) operates a dual polarization S-Band weather radar in Cascavel city and since December 2015 a series of anemometers were installed in four energy towers within the weather radar range to study the behaviour of wind gusts around and in these structures. On July 13th, 2016 at 00 UTC was reported that a transmission tower was toppled by wind. A sonic anemometer near the tower at 44m high recorded a gust of 32m/s. To understanding the thermodynamics of the storm it had been run simulations with WRF numerical model at a high-resolution framework. This study used the model  Weather Research Forecasting  (WRF) with four nested grids  9, 3, 1 and 0.33 km horizontal resolution centered on the radar site. It was used the GFS external boundary conditions that run with 0.25 degree horizontal resolution.  The simulations were set with Lin et al. microphysics, RRTM for longwave radiation, Dhudia shortwave radiation scheme and surface physics of Revised Monin-Obukhov scheme. This set of physics parameterizations was chosen among four other based on a previous study on february 18th, 2016. The assimilation was performed over the 0.33km and 1km grids only. Nested feedback carried out information throughout the domain. While the model without assimilation was not able to generate the convection as observed with radar data, the high-resolution simulation with radar data assimilation yield a development of the convection cells compared to the observations, although in some areas the reflectivity simulated were overestimated. The spatial distribution of reflectivity, and the magnitude of the wind forecasted by the model were similar to those observed, although the reflectivity amplitude were again overestimated in some areas. These preliminary results encourage further investigations in radar data assimilation for short-range forecast. Quality control is a major issue that should be investigated thoroughly, and in particular when regarding radial velocity. Using polarimetric variables can improve the quality control and is already in the process of analysis of this project. The simulations show encourageous results to improve model skill in short-range forecasts (about 1 hour) by assimilation of radar reflectivity, radial velocity along with automatic surface meteorological stations.