Data-driven model of the local wind field over two small lakes in Jyväskylä, Finland

Abstract

AbstractThis study presents a data-driven model of the local wind field over two small lakes in Jyväskylä, Finland. Five temporary monitoring stations installed during the summers of 2015 and 2016 observed wind speed/direction around the two lakes. In addition, an official meteorological station located 15 km north of the lakes is permanently available. Our goal was to develop a model that could evaluate wind speed and direction over the two lakes using only data from the permanent station. Statistical analysis for the spatio-temporal wind data revealed that (1) local wind speed is correlated with the elevation and its cyclic pattern is identical to that of the official-station data, and (2) the local wind direction field is spatially homogeneous and is strongly correlated with the official-station data. Based on these results, we built two regression models for estimating spatial distribution of local wind speed and directions based on the digital elevation model (DEM) and official-station data. We compared the predicted wind speeds/directions by the proposed model with the corresponding observation data and a numerical result for model validation. We found that the proposed model could effectively simulate heterogeneous local wind fields and considers uncertainty of estimates.