Posted October 22, 2020 in Featured News

Three different interpolation methodologies (see section 3 for details) have been applied over the wind speeds from the reanalysis to produce wind speed values at the locations of the 77 tall towers. 2012).

2002).

An additional research topic is directed towards an improved handling of systematic model bias. ", "ERA5.1 is very close to ERA5 in the lower and middle troposphere. Departures from MR follow for (c) ERA‐Interim, (d) ERA5, (e) JRA55, (f) MERRA2, and (g) R1 reanalyses, Normalized linear trend (% per decade), calculated as the linear trend of surface wind speeds divided by the seasonal mean surface wind speeds in DJF over the 1980–2017 period for (a) ERA‐Interim, (b) ERA5, (c) JRA55, (d) MERRA2, and (e) R1.

The wider range of values noted in DJF (Figure 8a) illustrates the complications of the reanalysis in reproducing the observed IAV in that particular season. However, these products have inherent uncertainties that derive from model simplifications, observational uncertainties, and the data assimilation procedure.

1996). How to download ERA-Interim data from the ECMWF data archive, ERA-Interim Monthly Means of Daily Means: wind U and V versus 'wind speed', ERA-Interim: What is the difference between TCWV (Total Column Water Vapour) and VIWV (Vertical Integral of Water Vapour), ERA-Interim: compute geopotential on model levels, How to specify dates for ERA-Interim daily and monthly data using Python, What is the difference between ERA-Interim (1979-2019) and ERA-40 (1957-2002), 6-hourly atmospheric fields on model levels, pressure levels, potential temperature and potential vorticity, 3-hourly surface fields and daily vertical integrals.

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Likewise, in the case of ERA5 it is observed that the near‐surface winds reduce the spread of differences in JJA compared with the ERA5 surface winds (Figure 7b).
For variables with high spatial variability, this method produces smoother results. Finally, reanalysis seasonal means and IAVs are compared against those computed using the observational dataset.

We intended to filter those tall towers within the Tall Tower Dataset, containing data spanning more than 20 years and without homogeneity issues. (1971), and subsequently the Monin–Obukhov similarity theory. Results have been produced for each season: December–January–February (DJF), March–April–May (MAM), June–July–August (JJA) and September–October–November (SON). The Tall Tower Dataset: a unique initiative to boost wind energy research. In terms of instantaneous RMS wind speed agreement, ERA5 winds show a 20% improvement relative to ERA interim, and a performance similar … Numerical Recipes in C: The Art of Scientific Computing, Inter‐annual variability of wind indices across Europe, The Tall Tower Dataset. All grids have been interpolated to the F47(T62) horizontal grid, which is R1's grid and is the coarsest out of the five reanalysis grids.

This has been documented in previous studies, such as Wu et al. In particular, the most significant disagreements are encountered within continental areas. (2017a), the extrapolation of surface winds in the JRA55 reanalysis from the lowest model level is carried out assuming neutral stability. Any queries (other than missing content) should be directed to the corresponding author for the article. Some of the most recent reanalyses (e.g., MERRA2 and ERA5) have started to provide winds at turbine hub heights, in reaction to the demands of the wind‐power industry.

The quality of the vertically extrapolated reanalysis surface winds is assessed now in terms of correlation, standard deviation, and CRMSE.

Concerning the last two items, one might expect that artefacts originated by the assimilation systems and available observations are partially compensated after considering several reanalysis products, since they employ diverging data assimilation schemes and ingest different amounts of records. University Corporation for Atmospherice Research (UCAR). Future work will be devoted to the study of the possible improvements that a vertical extrapolation that considers the stability of the planetary boundary layer could introduce, and their implications for the impact models currently employed in the wind energy sector to perform wind power estimations.

It is true, however, that several studies have focused on verifying reanalysis data on a regional scale (Kumar and Hu, 2012; Alvarez et al. 2010), which is published by NCEP, has been produced with two different model configurations before and after March 31, 2011, including different spectral resolutions.11 https://www.ncdc.noaa.gov/data‐access/model‐data/model‐datasets/climate‐forecast‐system‐version2‐cfsv2 Since this discontinuity produces detectable changes in mean wind speeds, the CFSR dataset has not been considered for the present study.

The ERA-Interim dataset contains atmospheric and surface parameters: Synoptic monthly averages at 00 UTC, 06 UTC, 12 UTC, 18 UTC. Global reanalysis datasets are an essential tool in different research disciplines to investigate past atmospheric conditions. Nonetheless, some studies (e.g., Lucio‐Eceiza et al.

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