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Abstract
This paper assesses impacts of three wind farms in northern Illinois using land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the Terra and Aqua satellites for the period 2003–2013. Changes in LST between two periods (before and after construction of the wind turbines) and between wind farm pixels and nearby non-wind-farm pixels are quantified. An areal mean increase in LST by 0.18–0.39 °C is observed at nighttime over the wind farms, with the geographic distribution of this warming effect generally spatially coupled with the layout of the wind turbines (referred to as the spatial coupling), while there is no apparent impact on daytime LST. The nighttime LST warming effect varies with seasons, with the strongest warming in winter months of December-February, and the tightest spatial coupling in summer months of June-August. Analysis of seasonal variations in wind speed and direction from weather balloon sounding data and Automated Surface Observing System hourly observations from nearby stations suggest stronger winds correspond to seasons with greater warming and larger downwind impacts. The early morning soundings in Illinois are representative of the nighttime boundary layer and exhibit strong temperature inversions across all seasons. The strong and relatively shallow inversion in summer leaves warm air readily available to be mixed down and spatially well coupled with the turbine. Although the warming effect is strongest in winter, the spatial coupling is more erratic and spread out than in summer. These results suggest that the observed warming signal at nighttime is likely due to the net downward transport of heat from warmer air aloft to the surface, caused by the turbulent mixing in the wakes of the spinning turbine rotor blades.
Highlights
In 2008 the U.S Department of Energy outlined an initiative to increase the contribution of wind energy to the US electricity supply to 20% by the year 2030 [1]
The present study builds upon the research of Zhou et al [17,18] over a region in northern Illinois, which has more variable weather systems than Texas, especially in winter, and very distinct land surface properties, to explore likely physical mechanisms that determine the magnitude and variability of wind farm-induced temperature changes seasonally and diurnally using meteorological data
The wind speeds at hub-height given above are in the cut-in and cut-out speed range for all seasons except for JJA, indicating on average, the wind turbines are operating across the year except for some days in summer, generating electricity, and mixing the atmosphere, bringing down warm air, and creating turbulence during the night
Summary
Remote sensing based studies [17,18,19,20,21] can effectively detect and quantify the wind farm impacts with spatial details from various perspectives (e.g., data quality, topography, land cover/use change), while the physical mechanisms responsible for the wind farm-induced LST changes are mostly speculative because meteorological data were not analyzed To overcome this limitation, the present study builds upon the research of Zhou et al [17,18] over a region in northern Illinois, which has more variable weather systems than Texas, especially in winter, and very distinct land surface properties, to explore likely physical mechanisms that determine the magnitude and variability of wind farm-induced temperature changes seasonally and diurnally using meteorological data. Besides analyzing remote sensed data as done in previous studies [17,18,19,20,21], the roles of atmospheric stability, wind speed and direction in determining the wind farm impacts are primarily examined
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