![[2602.12980] MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.12980] MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates
Summary
The paper presents MAUNet-Light, a lightweight neural network architecture designed for bias correction and downscaling of precipitation estimates, improving computational efficiency without sacrificing accuracy.
Why It Matters
As climate models often show biases in precipitation estimates, this research is significant for enhancing weather forecasting systems. The development of a more efficient model like MAUNet-Light can facilitate broader applications in climate science, potentially leading to improved environmental predictions and responses.
Key Takeaways
- MAUNet-Light offers a compact architecture for precipitation bias correction and downscaling.
- The model maintains accuracy while reducing computational and memory requirements.
- It builds on the existing MAUNet architecture, showcasing adaptability in deep learning applications.
- This research addresses the need for efficient models in operational weather forecasting.
- The findings could influence future developments in climate modeling and machine learning techniques.
Computer Science > Machine Learning arXiv:2602.12980 (cs) [Submitted on 13 Feb 2026] Title:MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates Authors:Sumanta Chandra Mishra Sharma, Adway Mitra, Auroop Ratan Ganguly View a PDF of the paper titled MAUNet-Light: A Concise MAUNet Architecture for Bias Correction and Downscaling of Precipitation Estimates, by Sumanta Chandra Mishra Sharma and 2 other authors View PDF HTML (experimental) Abstract:Satellite-derived data products and climate model simulations of geophysical variables like precipitation, often exhibit systematic biases compared to in-situ measurements. Bias correction and spatial downscaling are fundamental components to develop operational weather forecast systems, as they seek to improve the consistency between coarse-resolution climate model simulations or satellite-based estimates and ground-based observations. In recent years, deep learning-based models have been increasingly replaced traditional statistical methods to generate high-resolution, bias free projections of climate variables. For example, Max-Average U-Net (MAUNet) architecture has been demonstrated for its ability to downscale precipitation estimates. The versatility and adaptability of these neural models make them highly effective across a range of applications, though this often come at the cost of high computational and memory requirements. The aim of this research is to develop light-wei...
