Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa
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Abstract (arXiv)
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 (0.25deg) to IMERG V07 satellite precipitation (0.1deg) 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.
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References · 14
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2018CBAM: Convolutional Block Attention Modulecited by 2,477
- 2017Improved Training of Wasserstein GANscited by 2,235
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- 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,198
- 2023Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscalingcited by 110
- 2019Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importancecited by 87
- 2017DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolutioncited by 82
- 2020Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Networkcited by 70
- 2022A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecastscited by 66
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