arXiv AI

HaorFloodAlert: A 72-Hour Machine Learning Early Warning System for Flash Floods in Bangladesh's Haor Wetlands

arXiv:2605. 20167v2 Announce Type: replace Abstract: Every spring, flash floods strike the haor wetlands of northeast Bangladesh just before the boro rice harvest, and one flood can erase a family's entire crop in days.

arXiv Machine Learning

5d ago

Which Site, and When: A Free-Satellite-Data Test of Himalayan Glacial Lake Bursts, Landslides, and Ice Floods

arXiv:2608. 12422v1 Announce Type: new Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress.

By Matthew Kahn, Milan Arjel, Nirmala Adhikari, Mingmar Sherpa, James Pope

arXiv AI

Jul 28

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

By Maria Luisa Taccari, Kenza Tazi, Ois\'in M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger

fine-tuning benchmarks

arXiv Machine Learning

Jul 20

Physics-Based Deep Spatiotemporal Hyperlocal Radar Nowcasting with a Multi-Variable U-Net for High-Resolution Precipitation Forecasting

arXiv:2607. 16080v1 Announce Type: new Abstract: Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions.

By Akshay Sunil, Muhammed Rashid, Raja Sekhar Sivaraju, Sushma Nair, Subimal Ghosh

arXiv Machine Learning

Jun 15

Can Machine Learning Forecast Rice Yields in Data-Constrained Settings? Satellite Climate Data, National Crop Statistics, and Lessons from Sierra Leone

arXiv:2606. 13959v1 Announce Type: new Abstract: Sierra Leone's agriculture operates with almost no data-driven decision support, and no published machine learning study has examined the country's crop yields.

By Ibrahim Denis Fofanah

arXiv AI

Jun 9

Land cover and flood type govern the detection limits of satellite-based flood mapping across diverse global flood events

arXiv:2606. 07780v1 Announce Type: new Abstract: Floods are among the most destructive natural hazards, and their increasing frequency under climate change makes satellite-based inundation mapping essential for disaster response.

By Venkatesh Kolluru, Rajat Shinde, Abdelhak Marouane, Caden Helbling, Deepak Shah, Othneil Drew, Iksha Gurung, Manil Maskey, Rahul Ramachandran

arXiv Machine Learning

22h ago

MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations

arXiv:2608. 17753v1 Announce Type: new Abstract: Short-duration heavy-rainfall warning determines whether 1 h rainfall will exceed a threshold within a target-station neighborhood over the next few hours.

By Xiang Lin, Yunying Li, Chengzhi Ye, Zitong Chen, Jing Sun

arXiv Machine Learning

Jul 10

Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5

arXiv:2607. 07951v1 Announce Type: new Abstract: Wildfire smoke events produce extreme PM$_{2.

By Yongcan Huang, Li Jiang, Ze Yu Liu

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 30

Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities

arXiv:2607. 26581v1 Announce Type: new Abstract: Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences.

By Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar, Nithin Nagaraj

benchmarks

arXiv Machine Learning

Jul 13

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction

arXiv:2604. 16238v2 Announce Type: replace Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes.

By Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Zekun Ni, Jeremy Berman, Genevieve Flaspohler, Alex Lu, Jakob Schloer, Joshua Talib, Jonathan A. Weyn, Lester Mackey

safety

arXiv AI

Jul 7

AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales

arXiv:2607. 05100v1 Announce Type: cross Abstract: Data-driven models now rival numerical weather prediction in the medium range, but extending them to sub-seasonal lead times raises challenges absent at shorter horizons.

By Jakob Schloer, Steffen Tietsche, Christopher D. Roberts, Lorenzo Zampieri, Simon Lang, Gert Mertes, Gareth Jones, Matthew Chantry, Frederic Vitart

fine-tuning

arXiv Machine Learning

Jul 28

HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows

arXiv:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.

By Qingyi Yang, Siqian Qiu, Bing Li, Xu Shan, Jia Feng, Shunan Zhou, Xudong Zhou, Tiantian Xing, Jiale Guo, Xiaoyi Dong, Gaoyu Liu, Xiaohuan Liu, Haiqing Pu, Qingwen Deng, Xun Zhang, Zhongrun Xiang, Haiyang Qian, Ying Yan, Yongkang Xu, Nuo Lei, Tianlong Jia, Baoying Shan, Carlo De Michele

llms agents benchmarks

arXiv Machine Learning

Aug 12

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

arXiv:2608. 09959v1 Announce Type: cross Abstract: AI weather models are in the process of revolutionising weather forecasting.

By Anna Allen, Wessel P. Bruinsma, Michael Maier-Gerber, Harrison Cook, Matthew Chantry, Richard E. Turner

llms agents benchmarks

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