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.
Highlights
Lake ecosystems are strongly influenced by circulation, diffusion, and mixing processes which are dominantly induced by wind shear stress
This study presents a data-driven model for estimating local wind fields over two small lakes, Palokkajärvi and Tuomiojärvi, located in the northern part of Jyväskylä, Finland (Fig. 1) using a limited amount of data
This study aimed to develop a model for simulating a local wind field, including both wind speed and direction, over two lakes using official-station data
Summary
Lake ecosystems are strongly influenced by circulation, diffusion, and mixing processes which are dominantly induced by wind shear stress. Lake circulation models often assume wind fields to be spatially homogeneous, despite the recognized importance of spatial wind distributions (e.g., Podsetchine and Schernewski 1999). This is because high-resolution spatial data are usually unavailable for use in lake simulations, and there are no validated methods to evaluate local wind fields using only limited monitoring data.
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