Data science and climate risk analytics

Abstract

With influences from different communities, data science has evolved to provide insights in many different data-driven environments, including climate science.In this article, a brief review of data science and its connection to climate science will be presented. Additionally, two data science pipelines for quantifying risks from climate change are discussed. These pipelines focus on flooding due to tropical cyclone storm surge and changes in the distribution of temperature or precipitation or wind due to climate change via downscaling climate models. Finally, some key data science research areas in climate risk analytics are discussed.

Environmental data science: Part 2

“…Several contributions also consider the problem of spatial/spatio‐temporal interpolation or emulation: Granville, Woolford, Dean, Boychuk, and McFayden (2023) tackle the problem of interpolating spatial data for generating a fire index for wildfires in Ontario, Canada, while Cartwright, Zammit‐Mangion, and Deutscher (2023) develop a spatio‐temporal emulator based on convolutional variational autoencoders. Several contributed opinion pieces also expand on the challenges in this area: Scott (2023) discusses the ‘digital earth’ concept and the challenges of spatially or temporally sparse data; Blair and Henrys (2023) consider the idea of ‘digital twins’ for making sense of complex, heterogeneous spatio‐temporal data; and Sain (2023) discusses data science and risk quantification in a complex environment.…”

“…There is also a need for vetted and inclusive curricular materials for multidisciplinary communication and comprehension (Danyluk et al 2021; Horton, Alexander, Parkers, Piekut, & Rundel 2022). Sain (2023) reminds us that the toolbox for modeling and analysis is growing rapidly, and that there are opportunities and challenges in thoughtfully incorporating methodologies into EDS, including those concerned with the quantification of risk. Blair and Henrys (2023)'s focus on the interrelationships between process and data models, and the complexity of the ‘arrows’ that join them, is a timely reminder for practitioners in EDS to consider the connections between the models, the data, and the world they are all based on.…”