Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification

by   Daniel Getter, et al.

Machine learning models are frequently employed to perform either purely physics-free or hybrid downscaling of climate data. However, the majority of these implementations operate over relatively small downscaling factors of about 4–6x. This study examines the ability of convolutional neural networks (CNN) to downscale surface wind speed data from three different coarse resolutions (25km, 48km, and 100km side-length grid cells) to 3km and additionally focuses on the ability to recover subgrid-scale variability. Within each downscaling factor, namely 8x, 16x, and 32x, we consider models that produce fine-scale wind speed predictions as functions of different input features: coarse wind fields only; coarse wind and fine-scale topography; and coarse wind, topography, and temporal information in the form of a timestamp. Furthermore, we train one model at 25km to 3km resolution whose fine-scale outputs are probability density function parameters through which sample wind speeds can be generated. All CNN predictions performed on one out-of-sample data outperform classical interpolation. Models with coarse wind and fine topography are shown to exhibit the best performance compared to other models operating across the same downscaling factor. Our timestamp encoding results in lower out-of-sample generalizability compared to other input configurations. Overall, the downscaling factor plays the largest role in model performance.


page 5

page 6

page 10

page 12

page 13

page 18


Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts

To forecast wind power generation in the scale of years to decades, outp...

Application of ERA5 and MENA simulations to predict offshore wind energy potential

This study explores wind energy resources in different locations through...

Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network

Wind energy resource quantification, air pollution monitoring, and weath...

Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks

Synthetic Aperture Radar is known to be able to provide high-resolution ...

Multi-Resolution Spatio-Temporal Prediction with Application to Wind Power Generation

This paper proposes a spatio-temporal model for wind speed prediction wh...

Station-wise statistical joint assessment of wind speed and direction under future climates across the United States

This study develops a statistical conditional approach to evaluate clima...

Enhanced Simulation of the Indian Summer Monsoon Rainfall Using Regional Climate Modeling and Continuous Data Assimilation

This study assesses a Continuous Data Assimilation (CDA) dynamical-downs...

Please sign up or login with your details

Forgot password? Click here to reset