# EPT-2 Technical Report

arXiv: https://arxiv.org/abs/2507.09703 · HTML: https://mlsanity.com/paper/2507.09703/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [FuXi Weather: A data-to-forecast machine learning system for global weather](https://mlsanity.com/paper/2408.05472/) (2024)
- [WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models](https://mlsanity.com/paper/2409.09371/) (2024)
- [WeatherMesh-3: Fast and accurate operational global weather forecasting](https://mlsanity.com/paper/2503.22235/) (2025)
- [EPT-1.5 Technical Report](https://mlsanity.com/paper/2410.15076/) (2024)

## Built on by (most cited first, 4 of 4)

- [Universal Diffusion-Based Probabilistic Downscaling](https://mlsanity.com/paper/2602.11893/) (2026)
- [Do AI weather models miss extremes?](https://mlsanity.com/paper/2608.09972/) (2026)
- [A Station-Based Evaluation of Machine Learning-based Weather Forecasting Models in Northern Norway](https://mlsanity.com/paper/2609.10564/) (2026)
- [Aries: A Proprietary Medium-Range Weather Prediction Model for the Energy Industry](https://mlsanity.com/paper/2609.13292/) (2026)

## Abstract (arXiv)

We present EPT-2, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI models for Earth system forecasting. EPT-2 delivers substantial improvements over its predecessor, EPT-1.5, and sets a new state of the art in predicting energy-relevant variables-including 10m and 100m wind speed, 2m temperature, and surface solar radiation-across the full 0-240h forecast horizon. It consistently outperforms leading AI weather models such as Microsoft Aurora, as well as the operational numerical forecast system IFS HRES from the European Centre for Medium-Range Weather Forecasts (ECMWF). In parallel, we introduce a perturbation-based ensemble model of EPT-2 for probabilistic forecasting, called EPT-2e. Remarkably, EPT-2e significantly surpasses the ECMWF ENS mean-long considered the gold standard for medium- to longrange forecasting-while operating at a fraction of the computational cost. EPT models, as well as third-party forecasts, are accessible via the app.jua.ai platform.