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The paper applies U-Net and GAN architectures to downscale ERA5 reanalysis fields to IMERG satellite precipitation over West Africa, with ML as the core method rather than a side tool. Its most useful finding for ML readers is that deterministic networks come out too dry and lose most observed variance at small scales, which is why the authors turned to a GAN. The permutation-importance result, which ranks 500 hPa specific humidity first, also gives a physically interpretable check on what the model is learning.
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa
arXiv:2610.16816v1 Announce Type: cross Abstract: Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based architectures to downscale daily ERA5 fields (0.25{\deg}) to IMERG V07 satellite precipitation (0.1{\deg}) over West Africa for the June to October seasons of 2000 to 2024, using geopotential, temperature, humidity and wind at three pressure levels together with elevation and land fraction. Adding ERA5 precipitation as input slightly improves performance for this particular challenging region. We find that all the deterministic networks investigated for this work are too dry and lose almost all observed variance below a few hundred kilometers, which motivated our use of a GAN-based alternative. We also investigated permutation importance for the U-Net that ranks 500 hPa specific humidity first, consistent with the sensitivity of deep convection to mid-tropospheric moisture.
Abstract:Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based architectures to downscale daily ERA5 fields (0.25°) to IMERG V07 satellite precipitation (0.1°) over West Africa for the June to October seasons of 2000 to 2024, using geopotential, temperature, humidity and wind at three pressure levels together with elevation and land fraction. Adding ERA5 precipitation as input slightly improves performance for this particular challenging region. We find that all the deterministic networks investigated for this work are too dry and lose almost all observed variance below a few hundred kilometers, which motivated our use of a GAN-based alternative. We also investigated permutation importance for the U-Net that ranks 500 hPa specific humidity first, consistent with the sensitivity of deep convection to mid-tropospheric moisture.
Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
arXiv:2610.16816 [physics.ao-ph]
(or arXiv:2610.16816v1 [physics.ao-ph] for this version)
https://doi.org/10.48550/arXiv.2610.16816 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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