Evento Extremo de Chuva no Rio de Janeiro: Análise Sinótica, Previsão Numérica e Comparação com Eventos Anteriores

http://dx.doi.org/10.5902/2179460X16236The purpose of this study is to analyze the synoptic pattern and to verify the skill of numerical models (ETA15km, BRAMS20km and GFS) for intense rainfall event in the coastal of São Paulo and Rio de Janeiro states, Baixada Fluminense and the Mountain Region of Rio de Janeiro in march 17-18, 2013. In some places the rainfall in 24 hours exceeded 100 mm, reaching maximum of 424.5 mm in Petropolis. An objective tool was used for obtaining synoptic patterns occurred in the past similar to the extreme event studied. Heavy precipitation was caused by the presence of a cold front associated with intense low levels circulation, contributing for moisture advection from Atlantic Ocean toward the continent. The regional numerical models satisfactorily predicted the heavy rainfall episode. However, the intensity and location of heavy rainfall is still a challenge for numerical modeling.O objetivo deste trabalho é fazer uma análise sinótica e verificar a destreza de modelos numéricos operacionais (ETA15km, BRAMS20km e GFS) para um caso de chuva intensa em parte do litoral de São Paulo e do Rio de Janeiro, Baixada Fluminense e Região Serrana do Rio de Janeiro entre os dias 17 e 18/03/2013. Em algumas localidades a precipitação acumulada em 24 horas excedeu 100 mm, com máximo de 424,5 na cidade de Petrópolis. Foi usada uma ferramenta para a obtenção de padrões sinóticos ocorridos no passado semelhante ao evento extremo analisado. As fortes chuvas foram ocasionadas pela presença de uma frente fria associada aos ventos mais intensos em baixos níveis, favorecendo a advecção de umidade do oceano para o continente. Os modelos numéricos regionais representaram de forma qualitativamente adequada a chuva intensa na região atingida. Porém, a intensidade e a localização das chuvas intensas ainda é um desafio para a modelagem numérica

Data diaries : a situated approach to the study of data

This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil (Cemaden). We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest data diaries (1) are able to capture the informal dimension of data-intensive organisations; (2) enable empirical analysis of the specific ways that data intervene in the unfolding of situations; and (3) as a document, data diaries can foster interdisciplinary and inter-expert dialogue by bridging different ways of knowing data

Development of a soil moisture forecasting method for a landslide early warning system (LEWS):Pilot cases in coastal regions of Brazil

Climate change has increased the frequency of extreme weather events and, consequently, the number of occurrences of natural disasters. In Brazil, among these disasters, floods, flash floods, and landslides account for the highest number of deaths, the latter being the most lethal. Bearing in mind the importance of monitoring areas susceptible to disasters, the REMADEN/REDEGEO project of the National Center for Monitoring and Natural Disaster Alerts (Cemaden) has promoted the installation of a network of soil moisture sensors in regions with a long history of landslides. This network was used in the present paper as a base to develop a system for moisture forecasting in those critical zones. The time series of rainfall and moisture were used in an inversion algorithm to obtain the geotechnical parameters of the soil. Then the geotechnical model was used in a forward calculation with the rainfall prediction to obtain the soil moisture forecast. The landslide events of March 2020 and May 2022 in Guarujá and Recife, respectively, were used as study cases for the developed system. The obtained results indicate that the proposed methodology has the potential to be used as an important tool in the decision-making process for issuing landslide alerts.</p

The role of data in transformations to sustainability : a critical research agenda

This article investigates the role of digital technologies and data innovations, such as big data and citizen-generated data, to enable transformations to sustainability. We reviewed recent literature in this area and identified that the most prevailing assumption of work is related to the capacity of data to inform decision-making and support transformations. However, there is a lack of critical investigation on the concrete pathways for this to happen. We present a framework that identifies scales and potential pathways on how data generation, circulation and usage can enable transformations to sustainability. This framework expands the perspective on the role and functions of data, and it is used to outline a critical research agenda for future work that fully considers the socio-cultural contexts and practices through which data may effectively support transformative pathways to sustainable development

Avaliação da precipitação prevista pelo modelo MM5 em evento de chuva intensa no Estado de São Paulo

Motivado pelos desastrosos impactos sociais de eventos de chuva intensa, foi feito um estudo numérico de um evento extremo ocorrido em 01 de outubro de 2001 no Estado de São Paulo. A avaliação do modelo MM5 foi feita por meio dos índices Bias e Equitable Threat Score, além de diferenças entre observação e previsão, utilizando dados do Radar de Salesópolis. As simulações numéricas indicaram que os esquemas de parametrização de cumulus de Grell e Kain-Fritsch apresentaram melhores resultados do que o esquema de Anthes-Kuo. Resultou também que, para este caso, o aumento da resolução horizontal e vertical melhorou a previsão numérica apenas em alguns momentos e em algumas regiões. ABSTRACT: Motivated by the strong social impacts of extreme precipitation events, a numerical study of the October 1st 2001 case was conducted. The Bias Score and the Equitable Threat Score were used to evaluate the MM5 comparing the model's precipitation with the Salesópolis Radar observational data. The results suggested that the Grell and Kain-Fritsch cumulus parameterization schemes have better performances than the Anthes-Kuo one. Furthermore, increasing the horizontal and vertical resolution of the grid does not always increase the model's skill

Simulacao numerica de evento extremo de precipitacao no estado de Sao Paulo aplicando os esquemas de parametrizacao de cumulus de Anthes-Kuo e Grell no modelo MM5

This work shows two simulations of an extreme event of precipitation occurred in October 1 SI, 2001 in the State of São Paulo, Brazil. The MM5 regional model was run with two schemes of cumulus parametrization: Anthes-Kuo and Grell. There were three nested grids, with 90, 30 and 10 Km of resolution. The Anthes-Kuo scheme was only applied for the mother domain, while for the other grids the Grell scheme was used. Accumulated precipitation fields every 3 hours were compared with satellite images and with 3 Km CAPPI accumulated for the same period. The qualitative comparison showed that the Anthes- Kuo scheme has a precipitation field closer to the observed than the Grell scheme. These conclusions are consistent with the literature. It was algo detected that both schemes underestimated the precipitation over the region of Grande São Paulo

Um estudo de caso de invasao de ar polar em latitutes medias associado a uma cilcogenesis intensa no rio da Prata

This work intends to improve extratropical cyclones forecasts. We studied the case of September 27th 1998 when were observed record values of precipitation in Argentina. Satellite data from September 27th to October 2nd were analyzed and CPTEC model analyses were studied to describe the processes involved into the formation of the system and its contribution for the polar outbreak. It was also calculated the terms of Sutcliffe equation. The results were compared with the observation and a good agreement was found. It is concluded that intense gradients of humidity and temperature in low levels and gradients of vorticity in high levels favored the cyclone formation and development

The Master Super Model Ensemble System (MSMES)

A statistical model of weather forecast has been implemented at the weather laboratory at the University of São Paulo (MASTER/IAG/USP Laboratory - www.master.iag.usp.br). This statistical forecast is obtained on a routine daily basis from an ensemble of six global models and fourteen regional models of numerical weather prediction (NWP). The optimal combination of the several individual forecasts is obtained by the weighted mean of the forecasts after bias removal. The weights are provided by the inverse of the mean square error (MSE) of each forecast. The evaluation metric is based on the fit of the forecast to the surface data. METAR, SYNOP and the Center for Weather Forecasting and Climate Studies CPTEC automatic weather stations. . The predicted variables are: a) temperature; b) dew point temperature; c) zonal wind; d) meridional wind; e) sea level pressure; f) precipitation. Precipitation estimates provided by TRMM, NAVY and CPTEC are treated separately. To evaluate the statistical model, the MSE and bias averaged in 15 days period are calculated for each station. The choice of the 15 days period is based on the fact that the forecast errors are somewhat influences by the intraseasonal oscillation. Real time forecasts are available at the MASTER homepage. The statistical models evaluation indicates that the products are very robust and competitive. Separate evaluation is provided for different regions of Brazil and the statistical combination is better than any individual forecast in the mean sense. Concerning precipitation, the results are not statistically sound for the 6 hour accumulated precipitation (TRMM and NAVY), but for 24 hours accumulated precipitation the results are very robust. To appraise the accuracy of this forecast an uncertainness index is calculated. Low values of this index indicate that there isn't large dispersion between forecasts and also indicate that these forecasts are similar to statistical model forecast, thus increasing the confidence in the statistical forecasts.Pages: 1751-175

Analisis meteorologico para El Refugio de Montan J.J. Neumeyer, en San Carlos de Barilloche (Argentina), durante los inviernos de 1996-1999 y otono de 2000

EL presente trabajo muestra un resumen de los datos meteorológicos colectados en el refugio de montaña J. J. Neumeyer (41o16’S, 71o17’W, 1300 m), situado a cerca de 15 Km del centro de la ciudad turística de San Carlos de Bariloche, en Argentina, para los inviernos de 1996 al 2000. Fueron hechas observaciones de temperatura, presión, precipitación, humedad y otros elementos meteorológicos. Se compararon los datos medidos en el refugio con los datos del aeropuerto de Bariloche (41o09’S, 71o 10’W, 840 m) provistos por el Servicio Meteorológico Argentino (SMN). Los resultados indican un elevado coeficiente de correlación entre los datos de temperatura máxima en el refugio y en el aeropuerto (+0.97) y también entre los datos de presión (+0.99) en las dos localidades, con un intervalo de confianza de 99%, de acuerdo con el test T-student. La temperatura máxima promedio obtenida para el refugio quedó, en promedio, 4.4oC inferior a la temperatura máxima del aeropuerto, mientras el comportamiento de las temperaturas mínimas presentó una mayor variabilidad asociada a efectos topográficos locales. Un análisis inicial para la serie histórica de precipitación en el aeropuerto no apuntó ninguna tendencia de alteración climática en los últimos 50 años, y comparaciones con anomalías de TSM indicaron una tendencia a precipitaciones más elevadas en el periodo de invierno durante años de El Niño o cuando las aguas en la costa centro/sur de Chile se encuentran cálidas

Extreme rainfall and its impacts in the Brazilian Minas Gerais state in January 2020: Can we blame climate change?

In January 2020, an extreme precipitation event occurred over southeast Brazil, with the epicentre in Minas Gerais state. Although extreme rainfall frequently occurs in this region during the wet season, this event led to the death of 56 people, drove thousands of residents into homelessness, and incurred millions of Brazilian Reais (BRL) in financial loss through the cascading effects of flooding and landslides. The main question that arises is: To what extent can we blame climate change? With this question in mind, our aim was to assess the socioeconomic impacts of this event and whether and how much of it can be attributed to human-induced climate change. Our findings suggest that human-induced climate change made this event >70% more likely to occur. We estimate that >90,000 people became temporarily homeless, and at least BRL 1.3 billion (USD 240 million) was lost in public and private sectors, of which 41% can be attributed to human-induced climate change. This assessment brings new insights about the necessity and urgency of taking action on climate change, because it is already effectively impacting our society in the southeast Brazil region. Despite its dreadful impacts on society, an event with this magnitude was assessed to be quite common (return period of ∼ 4 years). This calls for immediate improvements on strategic planning focused on mitigation and adaptation. Public management and policies must evolve from the disaster response modus operandi in order to prevent future disasters