Estudo da dispersão de monóxido de carbono emitido por queimadas na Amazônia legal em 19 agosto de 2010 baseado em: simulações do modelo WRF-CHEM e SensoriamentoRemoto.
This study evaluates the reliability of the Weather Research and Forecasting (WRF) to simulate a snowfall event in the south of Brazil. The event in August 2013 was considered one of the most intense in recent years in the region with the highest topographic elevations between the states of Rio Grande do Sul (RS) and Santa Catarina (SC). The Snowfall in the mountain region of RS and SC was associated with the configuration involving a polar anticyclone and the intensification of an extratropical cyclone over the Atlantic Ocean. The WRF simulation results demonstrated the model's viability to predict the event, but without the magnitude representation of the phenomenon. The WRF simulation underestimated the results for the accumulated and area of the snowfall region, which may be linked to overestimations of surface and vertical air temperature and liquid water precipitation. These results were attributed to the choice of WRF Single–moment 6–class (WSM6) microphysics and in the Noah Land Surface Model scheme. Despite these limitations, WRF has proved to be an important tool for predicting the spatial and temporal distribution of snowfall and precipitation in the higher regions of southern Brazil.
Morbus Whipple is known as a systemic disease caused by bacteria and inducing the formation of mucopolysaccharides which is absorbed by macrophages. In a mucosal bioptate of a 60 years old patient suffering from this disease we could prove numerous rod-shaped bacteria (Propionibacterium) by electronmicroscopy. In the duodenal secretion aerobic and strictly anaerobic bacteria were to be found. Therapy with tetracycline was followed by a fast and distinct improvement of the clinical symptoms of the absorption disturbances but not so clear of the histological findings.
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.
The objective of this study is to observe the sensitivity of parameterizations of the WRF model to quantify the variables in surface: atmospheric pressure, air temperature, relative humidity and precipitation during the winter of 2014 in the State of Rio Grande do Sul (RS). The results were demonstrated from analysis of statistical indices, bias and Mean Squared Error root (RMSE) calculated for comparisons between the data extracted from 6 experiments of the WRF model simulations with data from the National Institute of Meteorology monitoring stations (INMET) in RS. The experiments were configuring with different physical parameterization, so that it may examine what combination performs better in the representation of the RS winter conditions. From the recognition of different physical interpretations that each set of parameterization can represent, a case study was made in order to diagnose the precipitations that occurred in the State, mainly in the municipality of Irai. The analysis came from a monitoring rain event occurred between 25 and 30 June 2014, using meteorological fields of 850hPa stream lines and rainfall. However, realizes that both temperature as pressure, the bias and the RMSE obtained no significant differences between experiments. UR, in the calculation of bias showed a big difference between the experiments, because of the manner of calculation only considers the systematic errors, which may cause cancellation of errors between underestimation and overestimation. The RMSE for the same variable showed no differences in significant amounts in the experiments, only in experiments 3 and 5, smallest error value when compared to the other experiments (~ 2%). To develop some considerations on the precipitation, the bias diagnosed underestimates the experiments for the rains during the winter of 2014; however, in the calculation of RMSE the experiments had not consent to each other, except 4 and 6, where the values of total errors were lower to 2mm. For the case study, which was accompanied rainfall occurred during the passage of an extratropical cyclone, in all experiments showed the characterization of the precipitation event. Thus, to diagnose the amount of precipitation during the event occurring on the Irai weather station with model data, combined with statistical analysis, the experiment 6 from the parameterization of combinations shown in this study had the best performance to characterize the atmospheric state during the winter period in the RS.
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