Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods

Prospecting for wind farm sites and pre-development studies of wind energy projects require knowledge of the wind energy resource over large areas (that is, areas of the order of 10’000 km2 and greater). One approach to detail this wind resource is the use of mesoscale numerical weather prediction models. In this paper, the mesoscale Weather Research and Forecasting (WRF) model is used to examine the effect of horizontal grid resolution on the fidelity of the predictions of the wind resource. The simulations are made for three test cases, Switzerland (land area 39’770 km2), Iowa (land area 145,743 km2) and Oregon (land area 248’647 km2), representing a range of terrain types, from complex terrain to flat terrain, over the period from 2006–2010. On the basis of comparisons to the data from meteorological masts and tall communication towers, guidelines are given for the horizontal grid required in the use of mesoscale models of large area wind resource assessment, especially over complex terrain.

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Wind Resource Assessment for Alaska’s Offshore Regions: Validation of a 14-Year High-Resolution WRF Data Set

Energies ◽

10.3390/en12142780 ◽

2019 ◽

Vol 12 (14) ◽

pp. 2780

Author(s):

Jared A. Lee ◽

Paula Doubrawa ◽

Lulin Xue ◽

Andrew J. Newman ◽

Caroline Draxl ◽

...

Keyword(s):

High Resolution ◽

Weather Prediction ◽

Regional Climate ◽

Wind Farms ◽

Resource Assessment ◽

Small Sample ◽

Offshore Wind ◽

Data Set ◽

Wind Resource Assessment ◽

Wind Resource

Offshore wind resource assessments for the conterminous U.S. and Hawai’i have been developed before, but Alaska’s offshore wind resource has never been rigorously assessed. Alaska, with its vast coastline, presents ample potential territory in which to build offshore wind farms, but significant challenges have thus far limited Alaska’s deployment of utility-scale wind energy capacity to a modest 62 MW (or approximately 2.7% of the state’s electric generation) as of this writing, all in land-based wind farms. This study provides an assessment of Alaska’s offshore wind resource, the first such assessment for Alaska, using a 14-year, high-resolution simulation from a numerical weather prediction and regional climate model. This is the longest-known high-resolution model data set to be used in a wind resource assessment. Widespread areas with relatively shallow ocean depth and high long-term average 100-m wind speeds and estimated net capacity factors over 50% were found, including a small area near Alaska’s population centers and the largest transmission grid that, if even partially developed, could provide the bulk of the state’s energy needs. The regional climate simulations were validated against available radiosonde and surface wind observations to provide the confidence of the model-based assessment. The model-simulated wind speed was found to be skillful and with near-zero average bias (−0.4–0.2 m s−1) when averaged over the domain. Small sample sizes made regional validation noisy, however.

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

10.5194/wes-2021-97 ◽

2021 ◽

Author(s):

Vincent Pronk ◽

Nicola Bodini ◽

Mike Optis ◽

Julie K. Lundquist ◽

Patrick Moriarty ◽

...

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Numerical Weather Prediction ◽

Prediction Models ◽

Weather Prediction ◽

Atmospheric Stability ◽

Negative Bias ◽

Numerical Weather ◽

Wind Resource ◽

Mesoscale Numerical Weather Prediction

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.

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Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers

Energies ◽

10.3390/en14144169 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4169

Author(s):

Giovanni Gualtieri

Keyword(s):

Wind Speed ◽

Prediction Models ◽

Weather Prediction ◽

Resource Assessment ◽

Reanalysis Data ◽

Solar Irradiation ◽

Wind Resource Assessment ◽

Regional Reanalysis ◽

Wind Resource ◽

Wind Resources

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

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