# YanTian: An Application Platform for AI Global Weather Forecasting Models

arXiv: https://arxiv.org/abs/2410.04539 · HTML: https://mlsanity.com/paper/2410.04539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [Deep Learning for Day Forecasts from Sparse Observations](https://mlsanity.com/paper/2306.06079/) (2023)
- [FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model](https://mlsanity.com/paper/2310.19822/) (2023)
- [Towards an end-to-end artificial intelligence driven global weather forecasting system](https://mlsanity.com/paper/2312.12462/) (2023)
- [Creating synthetic night-time visible-light meteorological satellite images using the GAN method](https://mlsanity.com/paper/2108.04330/) (2021)
- [The Compatibility between the Pangu Weather Forecasting Model and Meteorological Operational Data](https://mlsanity.com/paper/2308.04460/) (2023)

## Built on by (most cited first, 1 of 1)

- [Searth Transformer: A Transformer Architecture Incorporating Earth's Geospheric Physical Priors for Global Mid-Range Weather Forecasting](https://mlsanity.com/paper/2601.09467/) (2026)

## Abstract (arXiv)

To promote the practical application of AI Global Weather Forecasting Models (AIGWFM), we have developed an adaptable application platform named 'YanTian'. This platform enhances existing open-source AIGWFM with a suite of capability-enhancing modules and is constructed by a "loosely coupled" plug-in architecture. The goal of 'YanTian' is to address the limitations of current open-source AIGWFM in operational application, including improving local forecast accuracy, providing spatial high-resolution forecasts, increasing density of forecast intervals, and generating diverse products with the provision of AIGC capabilities. 'YianTian' also provides a simple, visualized user interface, allowing meteorologists easily access both basic and extended capabilities of the platform by simply configuring the platform UI. Users do not need to possess the complex artificial intelligence knowledge and the coding techniques. Additionally, 'YianTian' can be deployed on a PC with GPUs. We hope 'YianTian' can facilitate the operational widespread adoption of AIGWFMs.