HAiVA: Hybrid AI-assisted Visual Analysis Framework to Study the Effects
  of Cloud Properties on Climate Patterns
Subhashis Hazarika Haruki Hirasawa Sookyung Kim Kalai Ramea Salva R. Cachay Peetak Mitra Dipti Hingmire Hansi Singh Phil J. Rasch
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.