# AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting

arXiv: https://arxiv.org/abs/2608.09959 · HTML: https://mlsanity.com/paper/2608.09959/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [Evaluation of Tropical Cyclone Track and Intensity Forecasts from Artificial Intelligence Weather Prediction (AIWP) Models](https://mlsanity.com/paper/2409.06735/) (2024)
- [TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale](https://mlsanity.com/paper/2601.23268/) (2026)
- [Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction](https://mlsanity.com/paper/2603.22314/) (2026)

## Abstract (arXiv)

AI weather models are in the process of revolutionising weather forecasting. While these models have been shown to achieve superior performance to physics-based NWP in forecasting tropical cyclone (TC) tracks, they tend to dramatically underestimate intensity. Here we present AIFS-TC, a simple correction to the AIFS-Single model that is competitive with the operational state-of-the-art for forecasting maximum wind speed and minimum central pressure at lead times of 12 h to seven days. This performance also holds for rapid intensification events. Notably, the entire system was autonomously designed and built by a large language model (Claude Fable 5) in a few hours, directed through a small number of natural-language prompts by a single domain scientist. That the operational frontier can be reached with an open-source AI forecast model (AIFS-Single) and relatively simple, cheap post-processing is significant for TC science, and points to agentic coding as a route to rapid exploration and progress in life-saving early-warning systems in other domains.