MetaSD: A Unified Framework for Scalable Downscaling of Meteorological Variables in Diverse Situations

arXiv:2404.17611v1 Announce Type: cross
Abstract: Addressing complex meteorological processes at a fine spatial resolution requires substantial computational resources. To accelerate meteorological simulations, researchers have utilized neural networks to downscale meteorological variables from low-resolution simulations. Despite notable advancements, contemporary cutting-edge downscaling algorithms tailored to specific variables. Addressing meteorological variables in isolation overlooks their interconnectedness, leading to an incomplete understanding of atmospheric dynamics. Additionally, the laborious processes of data collection, annotation, and computational resources required for individual variable downscaling are significant hurdles. …

abstract algorithms arxiv computational cs.ai cs.lg diverse edge framework low networks neural networks physics.ao-ph processes researchers resolution resources scalable simulations spatial type variables