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Computer Science > Machine Learning

arXiv:2305.07859 (cs)
[Submitted on 13 May 2023]

Title:HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects of Cloud Properties on Climate Patterns

Authors:Subhashis Hazarika, Haruki Hirasawa, Sookyung Kim, Kalai Ramea, Salva R. Cachay, Peetak Mitra, Dipti Hingmire, Hansi Singh, Phil J. Rasch
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Abstract:Clouds have a significant impact on the Earth's climate system. They play a vital role in modulating Earth's radiation budget and driving regional changes in temperature and precipitation. This makes clouds ideal for climate intervention techniques like Marine Cloud Brightening (MCB) which refers to modification in cloud reflectivity, thereby cooling the surrounding region. However, to avoid unintended effects of MCB, we need a better understanding of the complex cloud to climate response function. Designing and testing such interventions scenarios with conventional Earth System Models is computationally expensive. Therefore, we propose a hybrid AI-assisted visual analysis framework to drive such scientific studies and facilitate interactive what-if investigation of different MCB intervention scenarios to assess their intended and unintended impacts on climate patterns. We work with a team of climate scientists to develop a suite of hybrid AI models emulating cloud-climate response function and design a tightly coupled frontend interactive visual analysis system to perform different MCB intervention experiments.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2305.07859 [cs.LG]
  (or arXiv:2305.07859v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2305.07859

Submission history

From: Subhashis Hazarika [view email]
[v1] Sat, 13 May 2023 07:55:47 UTC (11,490 KB)
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