Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, and visual data such as reanalysis atmospheric images. We evaluate our models with current operational forecasts in North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time, and show our models consistently outperform statistical-dynamical models and compete with the best dynamical models, while computing forecasts in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that combining different data sources and distinct machine learning methodologies can lead to superior tropical cyclone forecasting. We hope that this work opens the door for further use of machine learning in meteorological forecasting.Comment: Under revision by the AMS' Weather and Forecasting journa

CIRA annual report FY 2017/2018

Reporting period April 1, 2017-March 31, 2018

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change

This Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) has been jointly coordinated by Working Groups I (WGI) and II (WGII) of the Intergovernmental Panel on Climate Change (IPCC). The report focuses on the relationship between climate change and extreme weather and climate events, the impacts of such events, and the strategies to manage the associated risks. The IPCC was jointly established in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP), in particular to assess in a comprehensive, objective, and transparent manner all the relevant scientific, technical, and socioeconomic information to contribute in understanding the scientific basis of risk of human-induced climate change, the potential impacts, and the adaptation and mitigation options. Beginning in 1990, the IPCC has produced a series of Assessment Reports, Special Reports, Technical Papers, methodologies, and other key documents which have since become the standard references for policymakers and scientists.This Special Report, in particular, contributes to frame the challenge of dealing with extreme weather and climate events as an issue in decisionmaking under uncertainty, analyzing response in the context of risk management. The report consists of nine chapters, covering risk management; observed and projected changes in extreme weather and climate events; exposure and vulnerability to as well as losses resulting from such events; adaptation options from the local to the international scale; the role of sustainable development in modulating risks; and insights from specific case studies

Uma abordagem multitarefa para seleção automática de limiar em distribuições de Pareto

Orientador: Fernando José Von ZubenDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O principal objetivo deste trabalho é apresentar uma abordagem multitarefa, eficiente e automática para estimar limiares de uma distribuição generalizada de Pareto, visando uma previsão de alto desempenho de extremos em várias séries temporais de precipitação. Com base na teoria dos valores extremos, as únicas informações usadas para modelar uma distribuição de cauda pesada por estimação por máxima verossimilhança são fornecidas pelas amostras da série temporal que excedem um limiar definido pelo usuário. Essa abordagem sofre de duas desvantagens fundamentais: (1) a subjetividade na definição do limiar, mesmo quando se recorre a alguma orientação gráfica; (2) a natureza esparsa inerente das amostras acima do limiar, que, por definição, pertecem à cauda da distribuição. A proposta aqui apresentada para aprendizado multitarefa cria automaticamente um relacionamento hierárquico entre as tarefas de predição e usa uma validação cruzada aninhada para automatizar a escolha dos limiares mais indicados. Dada a relação hierárquica obtida entre as tarefas de predição, o aprendizado multitarefa explora os dados de várias tarefas de predição relacionadas para uma estimativa de máxima verossimilhança dos parâmetros que caracterizam a distribuição generalizada de Pareto mais robusta. A metodologia proposta foi aplicada em séries temporais de precipitação da América do Sul e sua performance foi comparada a um método de aprendizado monotarefa e à abordagem gráfica tradicional, indicando uma melhoria consistente de desempenho. Outra vantagem da abordagem é a possibilidade de realizar uma interpretação qualitativa da relação hierárquica obtida entre as tarefas, quando associada às localizações geográficas das séries temporais de precipitaçãoAbstract: The main objective of this work is to present a multitask, efficient and automatic approach to estimate thresholds for a generalized Pareto distribution, aiming at high-performance prediction of extremes in multiple precipitation time series. Based on Extreme Value Theory, the only information used to model the heavy tail distribution by maximum likelihood estimation is given by the samples of the time series exceeding a user-defined threshold. This approach suffers from two fundamental drawbacks: (1) the subjectivity of the threshold definition, even when resorting to some graphical guidance, (2) the inherent sparse nature of the above-threshold samples, which, by definition, belong to the tail of the distribution. The proposal presented here for multitask learning automatically creates a hierarchical relationship among the prediction tasks and uses a nested cross-validation to automatize the choice of the optimal thresholds. Given the obtained hierarchical relationship among the prediction tasks, the multitask learning explores data from multiple related prediction tasks toward a more robust maximum likelihood estimation of the parameters that characterize the generalized Pareto distribution. The proposed methodology was applied to precipitation time series of South America and its performance was compared to a single-task learning method and to the traditional graphical approach, indicating a consistent performance improvement. Another advantage of the approach is the possibility of performing a qualitative interpretation of the obtained hierarchical relationship among the tasks, when associated with the geographical locations of the precipitation time seriesMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica2018/09887-1FAPES

Investigation of climate change impact on hurricane wind and freshwater flood risks using machine learning techniques

Hurricane causes severe damage along with the U.S. coastal states. With the potential increase in hurricane intensity in changing climate conditions, the impacts of hurricanes are expected to be severer. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than intended. For the development of proper hurricane risk management strategies, it is crucial to investigate the climate change impact on hurricane risk. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims at investigating the climate change impact on hurricane wind and rain-ingress risk and freshwater flood risk on residential buildings across the southeastern U.S. coastal states. To address the challenge of computational inefficiency, surrogate models are developed using machine learning techniques for evaluating wind and freshwater flood losses of simulated climate-dependent hurricane scenarios. It is found that climate change impact varies by region and has a more significant influence on wind and rain-ingress damage, while both increases in wind and flood risks are not negligible

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC