arXiv (All)
open access
GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting
High-resolution precipitation nowcasting is critical for reducing the impacts of severe weather but remains difficult because of rapid storm evolution. Deep learning models have shown great promise for this task, but their predictive skill often deteriorates over longer forecast horizons. This leads
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Assessing trends and forecasting meteorological drought in South Africa using Savitzky-Golay enhanced hybrid deep learning.#258450
- An Input-Frugal Deep Learning Framework for Weather-Driven National Crop-Yield Forecasting: A Case Study of Brazilian Soybean#1143048
- Climate Change temperature Prediction Using Just Neural Network#105780
- MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation#1129450
- FRAME PREDICTION WITH HYBRID CONVLSTM AND TRANSFORMER MODELS: A GENERATOR-DISCRIMINATOR FRAMEWORK ON HKO-7 RADAR DATA#823820
Record · ID 819983
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive