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

Takayuki Shuku, Janne Ropponen, Janne Juntunen, Hiroshi Suito

Research output: Contribution to journalArticlepeer-review

Abstract

This 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.

Original languageEnglish
Article number18
JournalMeteorology and Atmospheric Physics
Volume134
Issue number1
DOIs
Publication statusPublished - Feb 2022

ASJC Scopus subject areas

  • Atmospheric Science

Fingerprint

Dive into the research topics of 'Data-driven model of the local wind field over two small lakes in Jyväskylä, Finland'. Together they form a unique fingerprint.

Cite this