Grid box-level evaluation of IMERG over Brazil at various space and time scales

This study evaluates the performance of the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run product over Brazil by means of multi-temporal and -spatial analyses. The assessment of the IMERG Final Run product is based on six statistics obtained for the period between January-December 2016 (daily, monthly, and annual basis). The analysis consisted of comparing the satellite-based estimates against a ground-based gridded rainfall product created using daily records from 4,911 rain gauges distributed throughout Brazil. Overall, the results show that the IMERG product can effectively capture the spatial patterns of rainfall across Brazil. However, the IMERG product presents a slight tendency in overestimating the ground-based rainfall at all timescales. Furthermore, the performance of the satellite product varies throughout the region. The higher errors and biases are found in the North and Central-West regions, but the low density of rain gauges in those regions can be a source of large deviations between IMERG estimates and observations. A large underestimation of the IMERG data is evident along the coastal zone of the Northeast region, probably due to the inability of the passive microwave and infrared sensors to detect warm-rain processes over land. This study shows that the IMERG product can be a good source of rainfall data to complement the ground precipitation measurements in most of Brazil, although some uncertainties are found and need to be further studied

Mineração de dados meteorológicos para previsão de eventos severos Meteorological data mining for the prediction of severe convective events

O objetivo do trabalho proposto é detectar antecipadamente possíveis ocorrências de eventos convectivos severos, por meio do monitoramento das saídas do modelo de previsão numérica de tempo Eta, para cada intervalo de previsão e para um conjunto de variáveis selecionadas. O período de estudo estende-se de janeiro a fevereiro de 2007. Classificadores foram desenvolvidos pela abordagem de similaridade de vetores e de conjuntos aproximativos, de forma a identificar saídas do modelo Eta que possam ser associados a esses eventos. Assumiu-se como premissa que os eventos convectivos severos possam ser correlacionados com grande número de ocorrências de descargas elétricas atmosféricas. Os classificadores agruparam as saídas do modelo Eta, compostas por essas variáveis, com base na densidade de ocorrência de descargas elétricas atmosféricas nuvem-solo. Ambos os classificadores apresentaram bom desempenho para os testes realizados para um período de dois meses escolhido para três mini-regiões selecionadas do território brasileiro.<br>This work aims the early detection of possible occurrences of severe convective events in Central and Southeast Brazil by means of monitoring the output of the Eta numerical weather prediction model for each forecasted time interval and for a selected set of variables. The studied period ranges from January to February 2007. Classifiers were developed by two approaches, vector similarity and rough sets, in order to identify Eta outputs that can be associated to such events. It was assumed that severe convective events can be correlated to a large number of atmospheric electric discharges. The classifiers grouped the Eta meteorological model outputs for these selected variables based on the density of occurrences of cloud-to-ground atmospheric electrical discharges. Both classifiers show good performance for the chosen 2-month period at the three selected mini-regions of the Brazilian territory

Avaliação do modelo regional eta utilizando as análises do CPTEC e NCEP Evaluation of the eta regional model using the analysis of CPTEC and NCEP

Os modelos numéricos de tempo são ferramentas essenciais para a previsão de curto e longo prazo, permitindo realizar a previsão com vários dias de antecedência. O conhecimento do desempenho dos modelos e dos erros sistemáticos a eles associados, é de suma importância, pois permite avaliar a capacidade dos mesmos em captar os processos físicos da atmosfera. Com intuito de melhorar a qualidade da previsão de tempo na América do Sul, disponibilizada no Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), este trabalho avaliou as previsões de precipitação e pressão ao nível médio do mar para o prazo de até 120 horas, utilizando o erro médio (EM) e a raiz do erro médio quadrático (REMQ) no período de dezembro de 2007 a fevereiro de 2008. O modelo utilizado foi o ETA (40 km), com duas entradas distintas de dados, as análises do Physical-space Statistical Analysis System (PSAS) (ETA-I) e do National Centers for Environmental Predictions (NCEP) (ETA-II). Os resultados mostraram, para ambas as análises, uma tendência de superestimativa (valores positivos do erro médio) da precipitação sobre a Região Norte do Brasil, principalmente para 24 horas de previsão. Em relação à pressão ao nível médio do mar (PNMM) foi possível verificar claramente que o ETA-I apresenta melhores resultados em comparação com o ETA-II, cujos valores de pressão se aproximaram bastante do observado, principalmente nas primeiras horas de integração.<br>The numerical weather models are essential tools for predicting short and long term, allowing the prediction of weather conditions several days in advance. The knowledge of models performance and the systematic errors associated with them is extremely important because it allows to evaluate the ability to capture the physical processes of the atmosphere. In order to improve the quality of weather forecast in South America, available at Center for Weather Forecast and Climate Studies (CPTEC), this study evaluated the forecasts of precipitation and mean sea level pressure for the period up to 120 hours, using the mean error (ME) and the root mean squared error (RMSE) from December 2007 to February 2008. The used model was the ETA (40 km), with two separate entries of data, the analysis of the Physical-Space Statistical Analysis System (PSAS) (ETA-I) and the National Centers for Environmental Predictions (NCEP) (ETA-II). The results showed, for both tests, a trend of overestimation (positive values of average error) of precipitation on the Northern Region of Brazil, mainly for the 24 hours forecast. Considering the mean sea level pressure (MSLP), it was clearly seen that the ETA-I, whose values of pressure are very close to observed, provides better results compared to the ETA-II, especially during the first hours of integration

Two models solutions for the Douro Estuary: flood risk assessment and breakwater effects

Estuarine floods are one of the most harmful and complex extreme events occurring in coastal environments. To predict the associated effects, characterize areas of risk and promote population safety, numerical modelling is essential. This work performs a comparison and a combination of two 2-dimensional depth averaged estuarine models (based on openTELEMAC-MASCARET and Delft3D hydrodynamic software packages), to develop a two-model ensemble approach that will improve forecast robustness when compared to a one-model approach. The ensemble was applied to one of the main Portuguese estuaries, the Douro river estuary, to predict the expected water levels associated with extreme river discharges in the present-day configuration with the new breakwaters. This is a region that is periodically under heavy flooding, which entails economic losses and damage to protected landscape areas and hydraulic structures. Both models accurately simulated water levels and currents for tidal- and flood-dominated validation simulations, with correlation values close to 1, "RMSE" below 15%, small "Bias" and "Skill" coefficient close to 1. The two-model ensemble results revealed that the present-day estuarine mouth configuration will produce harsher effects for the riverine populations in case identical historical river floods take place. This is mainly due to the increase in the area and volume of the estuary?s sand spit related to the construction of the new breakwaters.This research was supported by the Research Line ECOSERVICES, integrated in the Structured Program of R&D&I INNOVMAR: Innovation and Sustainability in the Management and Exploitation of Marine Resources (NORTE-01-0145-FEDER-000035), funded by the Northern Regional Operational Programme (NORTE2020) through the European Regional Development Fund (ERDF), and by the Brazilian National Council for Scientific and Technological Development (CNPq) through a scholarship granted to the 2nd author (Process 200016 / 2014-8).info:eu-repo/semantics/publishedVersio

Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks

Reduced rainfall increases the risk of forest dieback, while in return forest loss might intensify regional droughts. The consequences of this vegetation–atmosphere feedback for the stability of the Amazon forest are still unclear. Here we show that the risk of self-amplified Amazon forest loss increases nonlinearly with dry-season intensification. We apply a novel complexnetwork approach, in which Amazon forest patches are linked by observation-based atmospheric water fluxes. Our results suggest that the risk of self-amplified forest loss is reduced with increasing heterogeneity in the response of forest patches to reduced rainfall. Under dry-season Amazonian rainfall reductions, comparable to Last Glacial Maximum conditions, additional forest loss due to self-amplified effects occurs in 10–13% of the Amazon basin. Although our findings do not indicate that the projected rainfall changes for the end of the twenty-first century will lead to complete Amazon dieback, they suggest that frequent extreme drought events have the potential to destabilize large parts of the Amazon forest.peerReviewe