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

TL;DR

MetaSD introduces a unified, meta-learning-based framework for scalable downscaling of various meteorological variables, capturing their interconnections and outperforming existing methods across diverse datasets and scales.

Contribution

The paper presents the first generalized downscaling model that leverages meta-learning to handle multiple meteorological variables and their interrelations across different datasets and scales.

Findings

Outperforms existing top-downscaling methods in accuracy.

Effectively captures inter-variable relationships.

Demonstrates versatility across multiple datasets and scales.

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. Given the limited versatility of existing models across different meteorological variables and their failure to account for inter-variable relationships, this paper proposes a unified downscaling approach leveraging meta-learning. This framework aims to facilitate the downscaling of diverse meteorological variables derived from various numerical models and spatiotemporal scales. Trained at variables consisted of temperature, wind, surface pressure and total precipitation from ERA5 and GFS, the proposed method can be extended to downscale convective precipitation, potential energy, height, humidity and ozone from CFS, S2S and CMIP6 at different spatiotemporal scales, which demonstrating its capability to capture the interconnections among diverse variables. Our approach represents the initial effort to create a generalized downscaling model. Experimental evidence demonstrates that the proposed model outperforms existing top downscaling methods in both quantitative and qualitative assessments.