A Comparison between Analog Ensemble and Convolutional Neural Network Empirical-Statistical Downscaling Techniques for Reconstructing High-Resolution Near-Surface Wind

Christopher M Rozoff,Stefano Alessandrini

doi:10.3390/en15051718

Christopher M Rozoff, Stefano Alessandrini

Open Access

https://doi.org/10.3390/en15051718

Copy DOI

Abstract

Empirical-statistical downscaling (ESD) can be a computationally advantageous alternative to dynamical downscaling in representing a high-resolution regional climate. Two distinct strategies of ESD are employed here to reconstruct near-surface winds in a region of rugged terrain. ESD is used to reconstruct the innermost grid of a multiply nested mesoscale model framework for regional climate downscaling. An analog ensemble (AnEn) and a convolutional neural network (CNN) are compared in their ability to represent near-surface winds in the innermost grid in lieu of dynamical downscaling. Downscaling for a 30 year climatology of 10 m April winds is performed for southern MO, USA. Five years of training suffices for producing low mean absolute error and bias for both ESD techniques. However, root-mean-squared error is not significantly reduced by either scheme. In the case of the AnEn, this is due to a minority of cases not producing a satisfactory representation of high-resolution wind, accentuating the root-mean-squared error in spite of a small mean absolute error. Homogeneous comparison shows that the AnEn produces smaller errors than the CNN. Though further tuning may improve results, the ESD techniques considered here show that they can produce a reliable, computationally inexpensive method for reconstructing high-resolution 10 m winds over complex terrain.

Highlights

In climate modeling, empirical-statistical downscaling (ESD) is a valuable technique with a prime objective of reliably depicting a region’s climate details not resolved in a relatively coarse-resolution global climate model or in reanalysis data
To ensure a reduced computational cost in using the analog ensemble (AnEn) or convolutional neural network (CNN) for a 30 year regional climatology of high-resolution winds, this study considered the scenario where 5 years of training data were generated by the high-resolution configuration of Weather Research and Forecasting (WRF) and where 25 years of data were generated using the coarserresolution configuration of WRF
20 analog ensemble members were averaged at each grid point on grid 4 to reconstruct u and v from grid 3 with the AnEn ESD method

Summary

Introduction

Empirical-statistical downscaling (ESD) is a valuable technique with a prime objective of reliably depicting a region’s climate details not resolved in a relatively coarse-resolution global climate model or in reanalysis data These regional climate details simultaneously depend on the large-scale weather and climate patterns and on the local topography and other surface details. In contrast with ESD, dynamical downscaling employs a high-resolution numerical model furnished with initial and boundary conditions from a relatively coarse-resolution prediction or reanalysis to directly simulate smaller-scale details of a region’s climate. The latter approach is usually preferred when seeking the most accurate representation of the fine-scale atmospheric state of a region. Regression-based techniques and weather classification methods are the primary emphasis of this study, so the reader is referred to [1] for further information on the weather generator technique

Objectives

Methods

Results

Conclusion