13 Sep 2021

13 Sep 2021

Review status: a revised version of this preprint is currently under review for the journal WES.

Can Reanalysis Products Outperform Mesoscale Numerical Weather Prediction Models in Modeling the Wind Resource in Simple Terrain?

Vincent Pronk1, Nicola Bodini1, Mike Optis1, Julie K. Lundquist1,2,3, Patrick Moriarty1, Caroline Draxl1,3, Avi Purkayastha1, and Ethan Young1 Vincent Pronk et al.
  • 1National Renewable Energy Laboratory, Golden, Colorado USA
  • 2Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado USA
  • 3Renewable and Sustainable Energy Institute, Boulder, Colorado USA

Abstract. Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at site in Oklahoma, United States, and in a U.S. Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~ −1 m s−1 ) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a slight negative bias (~ −0.5 m s−1 ) offshore and a slight positive bias (~ +0.5 m s−1) at the land-based site. Surprisingly, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED’s higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle both onshore and offshore.

Vincent Pronk et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-97', Anonymous Referee #1, 14 Oct 2021
    • AC1: 'Reply on RC1', Nicola Bodini, 12 Nov 2021
  • RC2: 'Comment on wes-2021-97', Anonymous Referee #2, 01 Nov 2021
    • AC2: 'Reply on RC2', Nicola Bodini, 12 Nov 2021

Vincent Pronk et al.

Data sets

NYSERDA Lidar observations NYSERDA

SGP Lidar observations DOE ARM

Vincent Pronk et al.


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Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.