Oya: Deep Learning for Accurate Global Precipitation Estimation

• URL: http://arxiv.org/abs/2511.10562v1
• Date: Fri, 14 Nov 2025 01:57:48 GMT
• Title: Oya: Deep Learning for Accurate Global Precipitation Estimation
• Authors: Emmanuel Asiedu Brempong, Mohammed Alewi Hassen, MohamedElfatih MohamedKhair, Vusumuzi Dube, Santiago Hincapie Potes, Olivia Graham, Amanie Brik, Amy McGovern, George Huffman, Jason Hickey,
• Abstract summary: 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.<n>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.
• Score: 0.2621533844622817
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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