LGAIDec 3, 2024

WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks

arXiv:2412.02780v14 citationsh-index: 15Has Code
Originality Synthesis-oriented
AI Analysis

This provides a new benchmark for researchers in weather and climate AI, though it is incremental as it focuses on dataset creation rather than novel model development.

The authors tackled the scarcity of curated, machine learning-ready datasets for weather and climate analysis by introducing WxC-Bench, a multi-modal dataset that supports downstream tasks like aviation turbulence and hurricane monitoring, and they made it publicly available on Hugging Face.

High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific applications such as weather and climate analysis. Unfortunately, despite the growing development of new deep learning models for weather and climate, there is a scarcity of curated, pre-processed machine learning (ML)-ready datasets. Curating such high-quality datasets for developing new models is challenging particularly because the modality of the input data varies significantly for different downstream tasks addressing different atmospheric scales (spatial and temporal). Here we introduce WxC-Bench (Weather and Climate Bench), a multi-modal dataset designed to support the development of generalizable AI models for downstream use-cases in weather and climate research. WxC-Bench is designed as a dataset of datasets for developing ML-models for a complex weather and climate system, addressing selected downstream tasks as machine learning phenomenon. WxC-Bench encompasses several atmospheric processes from meso-$β$ (20 - 200 km) scale to synoptic scales (2500 km), such as aviation turbulence, hurricane intensity and track monitoring, weather analog search, gravity wave parameterization, and natural language report generation. We provide a comprehensive description of the dataset and also present a technical validation for baseline analysis. The dataset and code to prepare the ML-ready data have been made publicly available on Hugging Face -- https://huggingface.co/datasets/nasa-impact/WxC-Bench

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