Assessing Wind Data from Reanalyses for the Upper Midwest

Abstract

Wind is an important atmospheric variable that is receiving increased attention as the world seeks to shift from carbon-based fuels in order to mitigate climate change. This has resulted in increased need for more temporally and spatially continuous wind information, which is often met through the use of reanalysis data. However, limited work has been done to assess the long-term accuracy of the wind data against observations in the context of specific applications. This study focuses on the representation of daily and monthly average 10-m wind speed data in the upper Midwest by six global reanalysis datasets. The accuracy of the datasets was assessed using several measures of skill, as well as the associated wind speed distributions and long-term trends. While it was found that higher resolution and complexity in more recent generations of reanalyses produced more accurate simulations of wind in the region, important biases remained. High variability in the observed data resulted in lower correlations at the monthly time scale. As with previous research, linear trends calculated from the reanalyzed wind speeds were significantly underestimated compared to observed trends. While it is expected that future improvements in model resolution, physics, and data assimilation will further improve wind representation in reanalyses, accounting for the differences between the available datasets and their associated biases will be important for potential applications of the output, particularly wind resource assessment.

Evaluation of Global Reanalysis Land Surface Wind Speed Trends to Support Wind Energy Development Using In Situ Observations

“…The piecewise trends show wind speed trends calculated by reanalysis data have been significantly underestimated compared to the observed piecewise trends in most regions. This is consistent with the results obtained by Coburn (2019). JRA-55 can capture the phenomenon of global terrestrial stilling well, especially the stilling in Europe, North America, and Africa.…”

Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset

“…These results are in accordance with other studies (Decker et al ., 2012; Cannon et al ., 2015; Kaiser‐Weiss et al ., 2015; Rose and Apt, 2016; Minola et al ., 2020), where wind speeds from different reanalyses products are compared against flux tower or meteorological data in different areas of the globe. But in contrast with others (Toledo et al ., 2015; Coburn, 2019), probably mainly related to the studied area. Coburn (2019) in the upper Midwest of the United States, showed that correlations between reanalyses and observations improve with higher temporal reanalysis resolution (monthly correlations are smaller than daily correlations).…”

“…But in contrast with others (Toledo et al ., 2015; Coburn, 2019), probably mainly related to the studied area. Coburn (2019) in the upper Midwest of the United States, showed that correlations between reanalyses and observations improve with higher temporal reanalysis resolution (monthly correlations are smaller than daily correlations). Note that all these previous studies have a more limited studied area compared to the work presented here, in which the whole European area is analysed and so it would include all those different and mixed regional behaviours.…”

Can synoptic drivers explain rotor‐area wind conditions and predict energy output?

“…We characterized daily synoptic patterns over the Eolos site using a series of experiments to find the optimal synoptic classification for the site. We tested two clustering algorithms (K‐means, a nonheirarchical clustering method 22 and self‐organizing maps [SOM], a neural network‐based classification method 23,24 ), two reanalysis datasets as input for the clustering algorithm (ERA5 25 and MERRA‐2, 26 based in part on research by Coburn 27 ), and different numbers of clusters (i.e., synoptic types) for grouping the data (5, 10, 15, 20, and 25). We also tested different clustering variables (sea level pressure [SLP], which is commonly used for synoptic classification, 2‐m air temperature [T2m], which was used to potentially capture thermodynamic effects, or both in conjunction) and the spatial range over which to perform the classification (1000 1000, 2000 2000, and 3300 3300 km 2 or roughly 9° 9°, 18° 18°, and 27° 27° latitude and longitude at 45°N).…”

“…Coburn 27 ), and different numbers of clusters (i.e., synoptic types) for grouping the data (5, 10, 15, 20, and 25). We also tested different clustering variables (sea level pressure [SLP], which is commonly used for synoptic classification, 2-m air temperature [T2m], which was used to potentially capture thermodynamic effects, or both in conjunction) and the spatial range over which to perform the classification (1000 Â 1000, 2000 Â 2000, and 3300 Â 3300 km 2 or roughly 9 Â 9 , 18 Â 18 , and 27 Â 27 latitude and longitude at 45 N).…”