Aug 2026· Journal of Meteorological Research· Vol 40, pp. 1137 - 1150· 0 citations· 39 references
TL;DR
Overall, the SAECNN provides a computationally efficient approach for downscaling and improving coarse precipitation forecasts of EPS, while enhancing probabilistic forecasting skills.
RainCast is proposed, a high-resolution framework for hourly precipitation forecasting over China up to 72 hours ahead at 0.05° resolution that incorporates a physics-guided feature extractor and a multi-head output design that supports both deterministic forecasts with a regression head and probabilistic multi-member...
Guanlong Ma, Weiqiu Chen, Yang Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
Results establish the cascade‐diffusion framework as a practically viable approach for operational extreme precipitation forecasting and improves the distributional fidelity, with P80 and P90 aggregation both achieving strong detection skill at high thresholds.
Le-Yan Dai, H. Yuan, Yiheng Liu et al.· Journal of Geophysical Resea...· 0 citations
Results indicate that cross-attention conditioning offers advantages over simple concatenation for probabilistic precipitation downscaling, and that pre-trained foundation model representations may offer benefits in data-limited settings.
Victor Nascimento Ribeiro, Jorge Guevara, J. Moraga et al.· 0 citations
The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations.
Braiton U. Mukhalela, S. Viriri, D. Ndzi et al.· Frontiers in Artificial Inte...· 0 citations
A compact radar-only nowcasting framework that combines multi-elevation reflectivity, Doppler radial velocity, and radial-velocity-gradient proxy features within an encoder-decoder U-Net that generates nowcasts within seconds for real-time use.
Akshay Sunil, Muhammed Rashid, Raja Sekhar Sivaraju et al.· arXiv.org· 0 citations
This study proposes a novel deep learning framework that uses a U-Net to generate an initial high-resolution SST estimate, which is subsequently refined using a residual corrective approach, and progressively refines initial U-Net predictions by incorporating dynamically scaled residuals at each step, enabling accurate...
Onkar Jadhav, Tim French, I. Janeković et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
