{"ID":2842403,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10562","arxiv_id":"2511.10562","title":"Oya: Deep Learning for Accurate Global Precipitation Estimation","abstract":"Accurate precipitation estimation is critical for hydrological applications, especially in the Global South where ground-based observation networks are sparse and forecasting skill is limited. Existing satellite-based precipitation products often rely on the longwave infrared channel alone or are calibrated with data that can introduce significant errors, particularly at sub-daily timescales. This study introduces Oya, a novel real-time precipitation retrieval algorithm utilizing the full spectrum of visible and infrared (VIS-IR) observations from geostationary (GEO) satellites. Oya employs a two-stage deep learning approach, combining two U-Net models: one for precipitation detection and another for quantitative precipitation estimation (QPE), to address the inherent data imbalance between rain and no-rain events. The models are trained using high-resolution GPM Combined Radar-Radiometer Algorithm (CORRA) v07 data as ground truth and pre-trained on IMERG-Final retrievals to enhance robustness and mitigate overfitting due to the limited temporal sampling of CORRA. By leveraging multiple GEO satellites, Oya achieves quasi-global coverage and demonstrates superior performance compared to existing competitive regional and global precipitation baselines, offering a promising pathway to improved precipitation monitoring and forecasting.","short_abstract":"Accurate precipitation estimation is critical for hydrological applications, especially in the Global South where ground-based observation networks are sparse and forecasting skill is limited. Existing satellite-based precipitation products often rely on the longwave infrared channel alone or are calibrated with data t...","url_abs":"https://arxiv.org/abs/2511.10562","url_pdf":"https://arxiv.org/pdf/2511.10562v2","authors":"[\"Emmanuel Asiedu Brempong\",\"Mohammed Alewi Hassen\",\"MohamedElfatih MohamedKhair\",\"Vusumuzi Dube\",\"Santiago Hincapie Potes\",\"Olivia Graham\",\"Amanie Brik\",\"Amy McGovern\",\"George J. Huffman\",\"Jason Hickey\"]","published":"2025-11-13T18:01:08Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.ao-ph\"]","methods":"[]","has_code":false}