# Apeliotes: A Diffusion-Based Modeling Framework for km-scale Multi-Level Atmospheric Fields

arXiv: https://arxiv.org/abs/2607.17037 · HTML: https://mlsanity.com/paper/2607.17037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 19 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling](https://mlsanity.com/paper/2309.15214/) (2023)
- [Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales](https://mlsanity.com/paper/2406.16947/) (2024)
- [Machine learning emulation of a local-scale UK climate model](https://mlsanity.com/paper/2211.16116/) (2022)
- [Dynamical-generative downscaling of climate model ensembles](https://mlsanity.com/paper/2410.01776/) (2024)
- [Diffusion Models for High-Resolution Solar Forecasts](https://mlsanity.com/paper/2302.00170/) (2023)
- [Conditional diffusion models for downscaling and bias correction of Earth system model precipitation](https://mlsanity.com/paper/2404.14416/) (2024)
- [Deep Learning and Foundation Models for Weather Prediction: A Survey](https://mlsanity.com/paper/2501.06907/) (2025)
- [Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution](https://mlsanity.com/paper/2502.07814/) (2025)

## Abstract (arXiv)

High-resolution atmospheric data are required to resolve mesoscale and localized meteorological structures, however such datasets remain limited in many regions of the world. Existing high-resolution weather products are typically produced through dynamical downscaling, which is computationally expensive and difficult to scale across locations, variables, and forecast scenarios. These limitations motivate machine-learning-based downscaling systems that can generate multiple weather variables stochastically while producing new high-resolution fields directly. In this paper we present Apeliotes, a framework for high-resolution weather forecasting. Built on the global re-analysis atmospheric data, a pre-trained global weather foundation model, and a regionally trained generative diffusion model, Apeliotes not only provides accurate kilometer-scale weather variables, but also multi-level atmospheric fields which are not directly available in the existing global atmospheric data. Our comprehensive evaluation demonstrates that Apeliotes achieves highly competitive performance. The model predicts vertical wind profile with less than 3% error between truth and predicted fields, achieving correlations of 0.91 for 10-m wind speed and 0.99 for 2-m temperature, with NRMSE values of 0.42 and 0.17, respectively.