SWIRL: Statistical downscaling for Wind Pattern Reconstruction using Machine Learning

Abstract

Ports are critical infrastructures for global supply chains, crucial hubs and strategic to future trade. However, they are particularly exposed to Climate Change (CC) impacts, estimated to have broad implications on economy and human welfare. Therefore, a timely introduction of adaptation measures addressing CC impacts on ports becomes a major priority and can be proactive if based on projected climate. Yet, this challenge requires high spatial resolution timeseries for the present and the projected climate which are frequently missing. Moreover, employed downscaling procedures are not always skillful, particularly for extremely complex wind fields. The scope of this study is the development of reliable high-resolution wind speed/direction timeseries through Machine Learning (ML) techniques application. The employed ML regression schemes exploit ECMWF-ERA5 Reanalysis data as input training dataset (10931 instances) for Heraclion port area (Crete-Greece), containing 1 site of interest and 4 peripheral (period 1975-2004). Analytical simulations were conducted towards evaluating the regression accuracy on test data in terms of the Mean Absolute Error (MAE). Study outcomes revealed that ML techniques can efficiently reconstruct wind speed/direction timeseries, contributing to the wind downscaling and reconstruction problem, capable of supporting stakeholders needs on port scale regarding CC adaptation.