Improving Subseasonal Forecasting in the Western U.S. with Machine Learning

Hwang, Jessica P.; Orenstein, Paulo; Cohen, Judah; Pfeiffer, Karl P.; Mackey, Lester

doi:10.48550/arxiv.1809.07394

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…However such applications introduce new challenges for ML due to unique climate physics properties encountered in each problem, requiring novel research in ML. Nonetheless, there are several cross-cutting research themes in problems such as super-resolution, classification, climate downscaling, forecasting, emulating simulations, localization, detection and tracking of extreme events or anomalies, that are applicable across climate science and ML problems, which requires deep collaboration for synergistic advancements in both disciplines (Monteleoni et al, 2013;Joppa, 2017;Racah et al, 2017;Schneider et al, 2017;Gil et al, 2018;Hwang et al, 2018;Karpatne et al, 2018;Rasp et al, 2018). Furthermore, ML can help bridge the gap between numerical physics and personalized predictions by improving the accuracy of the physics models.…”

Section: Related Workmentioning

confidence: 99%

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Schmidt¹,

Luccioni²,

Mukkavilli³

et al. 2019

Preprint

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We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping that can then be applied to images of locations that have not yet experienced these events. This visual transformation is paired with climate model predictions to assess likelihood and type of climate-related events in the long term (50 years) in order to bring the future closer in the viewer's mind. The eventual goal of our project is to enable individuals to make more informed choices about their climate future by creating a more visceral understanding of the effects of climate change, while maintaining scientific credibility by drawing on climate model projections.

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Section: Related Workmentioning

confidence: 99%

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Schmidt¹,

Luccioni²,

Mukkavilli³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Rolnick et al [30] outline the wide range of machine learning applications to climate action, from enhancing efficiency in transport and infrastructure, to advancing the energy transition by improving renewable energy technologies. Other examples include using ML models for more accurate weather and climate forecasts [20] and applying deep learning to improve climate models [29] or advance earth science more broadly [24]. Meanwhile, the number of companies using AI to offer 'climate services' has surged for example through monitoring environmental risks (eg Ecometrica [16]), predicting extreme weather events (eg Jupiter [21]), or providing data to assess general climate risks (eg Acclimatise [4]).…”

Section: Ai For Climate Change and Food Securitymentioning

confidence: 99%

AI Ethics for Systemic Issues: A Structural Approach

van der Loeff,

Bassi,

Kapila

et al. 2019

Preprint

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The debate on AI ethics largely focuses on technical improvements and stronger regulation to prevent accidents or misuse of AI, with solutions relying on holding individual actors accountable for responsible AI development. While useful and necessary, we argue that this "agency" approach disregards more indirect and complex risks resulting from AI's interaction with the socio-economic and political context. This paper calls for a "structural" approach to assessing AI's effects in order to understand and prevent such systemic risks where no individual can be held accountable for the broader negative impacts. This is particularly relevant for AI applied to systemic issues such as climate change and food security which require political solutions and global cooperation. To properly address the wide range of AI risks and ensure 'AI for social good', agency-focused policies must be complemented by policies informed by a structural approach.

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Improving Subseasonal Forecasting in the Western U.S. with Machine Learning

Cited by 2 publications

References 31 publications

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

AI Ethics for Systemic Issues: A Structural Approach

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