Statistical Treatment of Convolutional Neural Network Superresolution of Inland Surface Wind for Subgrid-Scale Variability Quantification
- a Environmental Science Division, Argonne National Laboratory, Lemont, Illinois, d Department of Earth Sciences, University of Southern California, Los Angeles, California
- b Computational Science Center, National Renewable Energy Laboratory, Golden, Colorado, c Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia
- c Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia
- a Environmental Science Division, Argonne National Laboratory, Lemont, Illinois
Abstract Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge of recovering fine-scale information from coarse data. This limits their compatibility with many global climate model outputs, often available between ∼50- and 100-km resolution, to scales of interest such as cloud resolving or urban scales. This study systematically examines the capability of a type of superresolving convolutional neural network (SR-CNNs) to downscale surface wind speed data over land from different coarse resolutions (25-, 48-, and 100-km resolution) to 3 km. For each downscaling factor, we consider three convolutional neural network (CNN) configurations that generate superresolved predictions of fine-scale wind speed, which take between one and three input fields: coarse wind speed, fine-scale topography, and diurnal cycle. In addition to fine-scale wind speeds, probability density function parameters are generated through which sample wind speeds can be generated, accounting for the intrinsic stochasticity of wind speed. For assessing generalization to new data, CNN models are tested on regions with different topography and climate that are unseen during training. The evaluation of superresolved predictions focuses on subgrid-scale variability and the recovery of extremes. Models with coarse wind and fine topography as inputs exhibit the best performance when compared with other model configurations, operating across the same downscaling factor. Our diurnal cycle encoding results in lower out-of-sample generalizability when compared with other input configurations.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 2280749
- Journal Information:
- Artificial Intelligence for the Earth Systems, Journal Name: Artificial Intelligence for the Earth Systems Journal Issue: 1 Vol. 3; ISSN 2769-7525
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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