PROWIS: A Visual Approach for Building, Managing, and Analyzing Weather Simulation Ensembles at Runtime.

Weather forecasting is essential for decision-making and is usually performed using numerical modeling. Numerical weather models, in turn, are complex tools that require specialized training and laborious setup and are challenging even for weather experts. Moreover, weather simulations are data-intensive computations and may take hours to days to complete. When the simulation is finished, the experts face challenges analyzing its outputs, a large mass of spatiotemporal and multivariate data. From the simulation setup to the analysis of results, working with weather simulations involves several manual and error-prone steps. The complexity of the problem increases exponentially when the experts must deal with ensembles of simulations, a frequent task in their daily duties. To tackle these challenges, we propose ProWis: an interactive and provenance-oriented system to help weather experts build, manage, and analyze simulation ensembles at runtime. Our system follows a human-in-the-loop approach to enable the exploration of multiple atmospheric variables and weather scenarios. ProWis was built in close collaboration with weather experts, and we demonstrate its effectiveness by presenting two case studies of rainfall events in Brazil.

Development of multi-scale indices of human mobility restriction during the COVID-19 based on air quality from local and global NO2 concentration.

This study investigated whether variability in air quality, especially related to vehicular emissions, during the COVID-19 pandemic could indicate social distancing. Data from in situ measurements and satellite estimates were used. The study areas were São Paulo, Brazil, and Bologna, Italy. We focused our analysis on NO2, a combustion-derived pollutant, because of its availability in surface stations and satellite tracking, and because it has a short atmospheric lifetime. The analyses included graphical, statistical, and wavelet transform-based approaches to understand NO2 concentrations before and during the pandemic. After confirming the reduction in vehicular emissions during the pandemic, we created normalized indices to assess the social remoteness in 2020 in different locations, with a focus on São Paulo and Bologna. These indices were compared to existing indices based on cell phone mobility. The indices proposed in this study suffered high sensitivity to social distance compared to existing ones and helped to understand the actual application of social distance and contamination rates, considering the various dimensions of the problem.

Simulação dos impactos climáticos da desertificação do Nordeste brasileiro / Simulation of climatic impacts of desertification in Northeast Brazil

RESUMO O comportamento climático do Nordeste brasileiro (NEB) foi simulado considerando um cenário onde toda a sua área de Caatinga foi substituída por Deserto. A precipitação sofreu uma redução na região em quase todos os meses do ano, principalmente sobre o setor da Caatinga. A evapotranspiração apresentou decréscimos de grande magnitude ao longo de todo o ano, principalmente durante a estação chuvosa. O escoamento superficial apresentou acréscimos de forma geral, indicando uma diminuição da água extraída pelas raízes das plantas. A temperatura do ar foi a variável mais afetada, com elevações de até 6°C em algumas regiões e um aumento médio de aproximadamente 3°C para a área da Caatinga. Além disso, verificou-se a existência de um mecanismo restaurador associado à convergência de umidade que atuou no favorecimento da precipitação, embora insuficiente para evitar sua redução no NEB.

ABSTRACT The climatic behavior of the Brazilian Northeast (BNE) was simulated considering a scenario where all its caatinga area was replaced by desert. Precipitation decreased in the region in almost every month of the year, mainly on the caatinga sector. Evapotranspiration showed decreases of great magnitude throughout the year, especially during the rainy season. Runoff presented overall increases, indicating a decrease of the water extracted by plant roots. Air temperature was the most affected variable, with increases of up to 6°C in some regions and an average increase of approximately 3°C in the caatinga area. In addition, there was a restorative mechanism associated with convergence of humidity that worked favoring precipitation although insufficient to prevent its reduction in the BNE.

Aplicação de técnicas de redes neurais e modelagem atmosférica para elaboração de previsões de vazão na Bacia do Rio Grande (MG) / Application of neural networks techniques and atmospheric modeling to elaborate inflow forecasts in the Rio Grande Basin (MG), Brazil

RESUMO Neste estudo foi proposta a elaboração de um modelo de previsão de vazões no horizonte de dez dias para a Usina Hidrelétrica de Furnas, localizada na Bacia do Rio Grande, Minas Gerais, a partir da aplicação de redes neurais artificiais (RNA), informações de vazão natural e precipitação observada e prevista. O modelo foi desenvolvido utilizando o software Matlab(r) Neural Network Toolbox. Escolheu-se uma rede neural do tipo perceptron multicamadas (MLP), treinada com algoritmo supervisionado de retropropagação Levenberg-Marquardt. As previsões de precipitação foram obtidas a partir do modelo ETA/Centro de Previsão do Tempo e Estudos Climáticos (CPTEC), e utilizadas com e sem tratamento matemático. Foram realizados três experimentos, dividindo-se o histórico de dados em três períodos, sendo o primeiro para a calibração do modelo, o segundo para a validação e o terceiro para os testes. Em cada experimento foi variado o conjunto de dados de entrada, sendo utilizada, no primeiro experimento, somente a vazão passada para prever os dez dias de vazão futura. No segundo foi adicionada a precipitação observada e, no terceiro, a previsão de precipitação. Os resultados da modelagem chuva-vazão obtidos com a previsão de precipitaçãodo modelo ETA não apresentaram melhorias estatísticas em comparação com os experimentos que só utilizaram informações passadas. No entanto, quando se utilizou a previsão de precipitação corrigida matematicamente, observou-se uma melhora sensível tanto nos índices estatísticos quanto na representação da previsão simulada no hidrograma, ficando o desempenho da modelagem proposta neste estudo semelhante à encontrada em modelos conceituais do tipo chuva-vazão.

ABSTRACT The purpose of this study was to elaborate a ten-year runoff forecast model for the Furnas hydroelectric plant. The facility is located in the Rio Grande Basin in the state of Minas Gerais, Brazil. Artificial neural networks were used to determine natural flow as well as observed and predicted precipitation. The model was created using the Matlab(r) Neural Network Toolbox software, and the multi-layers perceptron (MLP) was trained with supervised learning algorithm Levenberg-Marquardt. Precipitation forecasts derived from ETA/Centro de Previsão do Tempo e Estudos Climáticos (CPTEC) model, and both raw and mathematical adjusted data were used. Historical data was separated in three different periods in order to calibrate, validate and test the model. The first share was used for calibration, the second portion was used for validation and the third one to test the model. In each experiment the input data was modified; thus, in the first experiment, to forecast the ten day runoff, only the past runoff data was considered. In the second experiment, observed precipitation was added; and in the third one, the forecast precipitation was added. The rainfall-runoff modeling results did not show any significant improvement in the statistics when ETA input data is compared with the experiments that only used past information as input. Nevertheless, when forecast precipitation was used with mathematical adjustment, a mild improvement was shown for the statistics index and for the forecast hydrogram simulation. As a result, the modeling performance proposed in this study is similar to that found in conceptual models of rainfall-runoff type.