PEAR: Equal Area Weather Forecasting on the Sphere

Artificial intelligence is rapidly reshaping the natural sciences, with weather forecasting emerging as a flagship AI4Science application where leading data-driven models now outperform traditional numerical methods across a broad range of global medium-range forecasting tasks. A common design choice in many of these models is an equiangular discretization of the sphere, which has much smaller grid cells at the poles than around the equator. In contrast, in the Hierarchical Equal Area iso-Latitude Pixelization (HEALPix) of the sphere, each pixel covers the same surface area. Motivated by the growing adoption of this grid in meteorology and climate science, we make the case for using HEALPix as the native grid for data-driven weather forecasting. To this end, we introduce Pangu Equal ARea (PEAR), an efficient volumetric transformer with equal-area inputs, intermediate representations, and outputs. We evaluate PEAR using ERA5-Lite, a reduced subset of the ERA5 atmospheric reanalysis dataset, for forecast times of one to nine days. PEAR remains competitive with the strongest evaluated baselines at short forecasting horizons and outperforms them at longer horizons. Among the evaluated models, PEAR combines the highest average forecasting accuracy with the fastest inference, while requiring less memory than its closest competitors in accuracy. Spectral analysis shows that PEAR has lower large-scale forecast errors at longer forecasting horizons compared to baselines. Furthermore, we quantify learned rotational equivariance for PEAR trained on ERA5-Lite and more densely sampled ERA5 data, and demonstrate single-step climate-model emulation, predicting surface temperature and precipitation from emission forcings.