Visualizing simulation ensembles of extreme weather events

Souza, Carolina Veiga Ferreira de; Barcellos, Priscila Cunha Luz; Crissaff, Lhaylla; Cataldi, Márcio; Miranda, Fábio; Lage, Marcos

doi:10.1016/j.cag.2022.01.007

Cited by 10 publications

(4 citation statements)

References 57 publications

(29 reference statements)

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“…Trial and error experimentation can be reduced, lowering costs and increasing efficiency. While for other simulated data, e.g., adversarial weather events [17] and storm path ensembles [33], analyzing data improves timely risk mitigation planning when real data cannot be collected due to limited time. The epidemic modeling and decision support system from Afzal et al [1] is closest to our work, where a visual analytics approach enables experts to evaluate a number of potential modeled responses to the pandemic.…”

Section: Ensemble Visualizationmentioning

confidence: 99%

Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modeling

Rydow

Borgo

Fang

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Algorithm-centric, Visualization-assistedObserve Enrich c Fig. 1. An algorithmic approach to sensitivity analysis typically takes the data of ensemble runs, such as the parameter sets (a) and outputs (b), and estimates the sensitivity of each parameter (c). This algorithmic-centric approach can be assisted by visualization (d) that enriches the basis numerical measures, and be complemented by a visualization-centric and algorithm-assisted approach (e).

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Section: Ensemble Visualizationmentioning

confidence: 99%

Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modeling

Rydow

Borgo

Fang

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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“…Studying probability distributions is a central task in (visual) data analysis. They are used to model uncertain behaviors present in natural phenomena [1][2][3], to build predictive models [4], and to describe the behavior/variability present in collections of data, often associated with the processes of sampling and/or aggregation [5,6]. However, effectively visualizing probability distributions presents a big challenge for visualization designers.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Methods for the Visualization of Probability Distributions of Geographical Data

Srabanti

Veiga

Silva

et al. 2022

MTI

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Probability distributions are omnipresent in data analysis. They are often used to model the natural uncertainty present in real phenomena or to describe the properties of a data set. Designing efficient visual metaphors to convey probability distributions is, however, a difficult problem. This fact is especially true for geographical data, where conveying the spatial context constrains the design space. While many different alternatives have been proposed to solve this problem, they focus on representing data variability. However, they are not designed to support spatial analytical tasks involving probability quantification. The present work aims to adapt recent non-spatial approaches to the geographical context, in order to support probability quantification tasks. We also present a user study that compares the efficiency of these approaches in terms of both accuracy and usability.

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“…Algumas abordagens encontradas na literatura [de Souza et al 2022] propõem modelos matemáticos para realizar previsões relacionadas ao clima. Porém, esses trabalhos têm foco em eventos específicos, e em regiões pequenas (i.e., micro climas).…”

Section: Introduc ¸ãOunclassified

@WeatherNit: uma Plataforma Orientada a Dados para Monitoramento de Chuvas e Ocorrências de Eventos Climáticos

Lage

Victorino

Moreira

et al. 2022

Anais Estendidos Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD Estendido 2022)

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O número de eventos climáticos extremos têm aumentado em todo planeta, impactando fortemente grandes centros urbanos, em especial aqueles que cresceram de forma desordenada. Nessas regiões, enchentes e deslizamentos de terras são responsáveis por muitas mortes, todos os anos. Por isso, planejamentos que ajudem a se antecipar, reagir e evitar tais eventos são de fundamental importância. Neste artigo, apresentamos uma plataforma chamada @WeatherNit para o monitoramento de chuvas e eventos climáticos na cidade de Niterói. A plataforma permite a visualização interativa de dados históricos e de tempo real de volumes acumulados de chuvas e de ocorrências de enchentes e deslizamentos, integrados e armazenados usando um Data Lakehouse. A plataforma foi avaliada com um estudo de caso utilizando dados do CEMADEN e da Prefeitura de Niterói que demonstrou o potencial da abordagem no monitoramento de crises e no apoio ao desenvolvimento de políticas públicas.

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Visualizing simulation ensembles of extreme weather events

Cited by 10 publications

References 57 publications

Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modeling

Development and Evaluation of Two Approaches of Visual Sensitivity Analysis to Support Epidemiological Modeling

A Comparative Study of Methods for the Visualization of Probability Distributions of Geographical Data

@WeatherNit: uma Plataforma Orientada a Dados para Monitoramento de Chuvas e Ocorrências de Eventos Climáticos

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