AI for Science (AI4Science) is an emerging field that explores the intersection of artificial intelligence (AI) and scientific research. It leverages the power of AI techniques and algorithms to analyze vast amounts of scientific data, accelerate discovery, and enhance our understanding of complex scientific phenomena.
The paper introduces CRA5, a project that uses the Variational Autoencoder Transformer (VAEformer) to compress the ERA5 climate dataset from 226TB to just 0.7TB, achieving a compression ratio of over 300 times.
The paper presents the WEATHER-5K dataset, a comprehensive global weather station dataset designed to advance time-series weather forecasting benchmarks. WEATHER-5K includes data from 5,672 weather stations worldwide, covering a 10-year period with hourly intervals.
FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting The first data-driven global weather forecasting model running at the 0.09◦ horizontal resolution. FengWu-GHR introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a pretrained low-resolution model.