HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects of Cloud Properties on Climate Patterns
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 45 references that are in the map. First posted on arXiv 13 May 2023.
Builds on
6 of 45 references.
References · 6
- 2016Layer Normalizationcited by 5,669
- 2016Gaussian Error Linear Units (GELUs)cited by 3,502
- 2020DeepSphere: a graph-based spherical CNNcited by 66
- 2022GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulationscited by 32
- 2018The State of the Art in Integrating Machine Learning into Visual Analyticscited by 24
- 2019NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulationcited by 12
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.