This study presents an AI-based dynamical downscaling system for Tropical Cyclones (TCs). The system incorporates an AI-based limited-area model that downscales 3-hourly low-resolution boundary forcings into hourly high-resolution fields autoregressively, and a diffusion model that converts the outputs into ensembles of hazard-relevant variables. The system is trained on the regridded CONUS404 data with ERA5 forcings, and is evaluated on 20 TCs in 2020--2024. Verification shows stable downscaling performance across Atlantic hurricane seasons, with energy spectra closely matching the CONUS404 reference. The system is also verified to produce skillful TC-relevant weather extremes, largely improved over a deterministic AI baseline. The system performs well with forcing data from other models (GDAS/FNL) and can produce detailed eyewall, rainband, and landfall structures in TC case studies. The study provides a good example of how AI-based dynamical downscaling systems can be designed to resolve small-scale extreme weather events.
This study employs a Hybrid Attention Transformer‐based super‐resolution model to reconstruct tropical cyclone (TC) surface wind fields. The model downscales coarse‐resolution ERA5 reanalysis data to high‐resolution HWind analyses (1998–2013), increasing the horizontal resolution by a factor of five. First, this st...
Chun-Hua Wang, Da-Zhi Xi, Nai-Jie He et al.· Journal of Geophysical Resea...· 0 citations
This paper reports on two sets of 22-year convection-permitting regional climate simulations conducted with the Weather Research and Forecasting model over the entirety of South America at 4-km grid spacing. The first simulation downscales the fifth-generation European Centre for Medium-Range Weather Forecasts atmosphe...
Chang-Hai Liu, Kyoko Ikeda, F. Dominguez et al.· Climate Dynamics· 2 citations
Coarse spatial resolution limits the ability of subseasonal prediction models to resolve extreme precipitation. Downscaling with either dynamical or deep generative models can overcome this issue, but the comparative performance of these models for extremes across different atmospheric regimes remains poorly understood...
Mauricio Lima, M. Koukoula, Romain Pilon et al.· 0 citations
High-resolution atmospheric thermodynamic information is essential for resolving localized weather processes in complex urbanized regions such as the Yangtze River Delta (YRD). However, existing global reanalysis products are limited by coarse spatial resolution, constraining their ability to represent fine-scale the...
Yan-An Liu, Zheng-Yan Zhang, Tian-Yue Zeng et al.· Advances in Atmospheric Scie...· 0 citations
This paper describes an ensemble of global atmosphere reference simulations covering the period 1980 to 2023 produced with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The resulting dataset consists of 6-hourly three-dimensional global outputs from higher- and lowe...
M. Aengenheyster, Christopher D. Roberts, Razvan Aguridan et al.· Scientific Data· 1 citation
At ICSOB 2026 in Gothenburg, we presented TenderIntell, our work on multi-agent support for tendering. This post looks back at the conference, the idea behind a panel of three AI reviewers who score each proposal independently, and why evaluating agents reliably is the question we keep coming back to. The post TenderIntell at ICSOB 2026: Three Days in Gothenburg appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduNov 13, 2026
Founded by Anthony Jules ’92, SM ’92 and Professor Emeritus Rodney Brooks, Robust AI’s robots are designed to collaborate with humans in factories and warehouses.
MIT News · Artificial Intelligence· news.mit.eduNov 13, 2026
