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.
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.
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.
O objetivo do presente do estudo foi observar a sensibilidade das parametrizações do modelo WRF ao quantificar as variáveis em superfície: pressão atmosférica, temperatura do ar, umidade relativa e precipitação durante o Inverno de 2014 no Estado do Rio Grande do Sul (RS). Os resultados foram demonstrados a partir de análise dos índices estatísticos, bias e Raiz do Erro Quadrático Médio (REQM), quando calculados para comparações entre os dados extraídos de 6 experimentos de simulações do modelo WRF com dados de estações de monitoramento do Instituto Nacional de Meteorologia (INMET) no RS. Os experimentos foram configurados com diferentes parametrização físicas, para assim poder verificar qual combinação apresenta melhor desempenho na representação das condições de Inverno do RS. A partir do reconhecimento das diferentes interpretações físicas que cada conjunto de parametrização pode representar, foi apresentado um estudo de caso afim de diagnosticar as precipitações ocorridas no Estado, principalmente no município de Irai-RS. As análises partiu de um acompanhamento de evento de chuvas ocorrido entre os dias 25 e 30 de junho de 2014, utilizando-se de cartas dos campos meteorológicos de Linhas de Corrente em 850hPa e Precipitação. Percebeu-se que tanto temperatura quanto pressão, o bias e o REQM obtiveram diferenças não significativas entre os experimentos. A UR, no cálculo do bias mostrou uma grande diferença entre os experimentos, devido a forma de seu cálculo considerar apenas o erros sistemáticos, podendo haver cancelamento de erros entre subestimativas e superestimativas. A REQM para a mesma variável, mostrou que os experimentos não se diferenciaram em valores significativos, obtendo apenas nos experimentos 3 e 5, menor valor de erro em comparação aos outros experimentos (~2%). Ao tecer considerações sobre a precipitação, o bias diagnosticou subestimativas nos experimentos para as chuvas durante o inverno de 2014, entretanto no cálculo da REQM os experimentos não tiveram assentimento entre si, exceto o 4 e o 6, onde os valores dos erros totais ficaram inferiores à 2mm. Para o estudo de caso, onde foi acompanhado as chuvas ocorridas durante a passagem de um fenômeno Ciclone Extratropical, em todos os experimentos mostrou a caracterização do evento de precipitação. Com isso, ao diagnosticar a quantidade de precipitação durante o evento ocorrido sobre a estação meteorológica de Irai-RS com os dados do modelo, somado as análises estatísticas, o experimento 6 dentre as combinações de parametrizações apresentadas neste estudo, obteve o melhor desempenho para caracterizar o estado atmosférico durante o período de inverno no RS. ABSTRACT 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. Keywords: Weather numerical forecast, WRF, physical parameterization, atmospheric modeling.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.