The Northeast of Brazil (NEB) is the region with the highest number of municipal decrees of emergency situation declaration caused by weather events in the period from 2013 to 2022 and with the highest rate of natural disasters per risk area. In the NEB, the city of Recife and its metropolitan region are the biggest localities with populations in risk areas. Focusing on this region, five events of extreme precipitation were chosen for simulations using the WRF model and diagnostics analyses. First, a set of configurations of the model was tested, including 11 microphysics (MPH) schemes, 9 planetary boundary layer (PBL) schemes, 5 cumulus (CUM), and 7 surface layer (SFC) schemes. Then, through diagnostic analysis, the conditional instability, the moisture supply at low levels, and the support of the medium and high levels in storm formation were verified. The model’s configurations were verified by 298 rain gauges with hourly registrations through statistical metrics such as bias, MSE, standard deviation, and Pearson’s correlation, and demonstrated that the MPH schemes of Thompson Aerosol-Aware and NSSL + CCM, ACM2, MYJ for the PBL, KFCuP for CUM, and RUC for SFC were considered the best. All the cases were better with CUM parametrizations turned on. In all cases, diagnostics analyses highlighted the strong moisture flux convergence at the low levels, the presence of wind shear on the middle layer, weak cyclonic vorticity advection at high levels, and CAPE values around 1500 J/kg, in addition to an inverse relationship between wind shear action and CAPE values. This work is part of the national strategy for monitoring, diagnosis, and modeling of information that can minimize or even prevent damage caused by severe precipitation events.

The state of Santa Catarina is often hit by extreme events such as heavy rain, windstorms, hail and eventually tornadoes. Thus, the goal of the present study was to verify how the inclusion of a larger number of observations could improve the prediction of such events. Thus, through a campaign carried out in the west of Santa Catarina, surface and radiosonde data were collected and assimilated through the GSI system. This process produced an updated initial condition to the weather prediction model WRF. The surface data assimilation had 84 new pressure observations. The radiosonde experiment had 162 observations of temperature, wind, and humidity assimilated. It was observed that the improvement of the initial condition through the insertion of the local surface and upper air data obtained during the campaign significantly improved the forecast in the conduced experiments.