Preprints
https://doi.org/10.5194/wes-2023-148
https://doi.org/10.5194/wes-2023-148
09 Jan 2024
 | 09 Jan 2024
Status: this preprint is currently under review for the journal WES.

On predicting offshore hub-height wind speed and wind power density in the Northeast US coast using high-resolution WRF model configurations during anticyclones coinciding with wind drought

Tasnim Zaman, Timothy Juliano, Pat Hawbecker, and Marina Astitha

Abstract. We investigated the predictive capability of various configurations of the Weather Research and Forecasting (WRF) model version 4.4, to predict hub-height offshore wind speed and wind power density in the Northeast US wind farm lease areas. The selected atmospheric conditions were high-pressure systems (anticyclones) coinciding with wind speed below the cut-in wind turbine threshold. There are many factors affecting the potential of offshore wind power generation, one of them being low winds, namely wind droughts, that have been present in future climate change scenarios. The efficiency of high-resolution hub-height wind prediction for such events has not been extensively investigated, even though the anticipation of such events will be important in our increased reliance on wind and solar power resources in the near future. We used offshore wind observations from the Woods Hole Oceanographic Institution's (WHOI) Air-Sea Interaction Tower (ASIT) tower located south of Martha’s Vineyard to assess the impact of initial and boundary conditions, number of model vertical levels, and inclusion of high-resolution sea surface temperature (SST) fields. Our findings showed that the initial and boundary conditions exhibited the strongest influence on hub height wind predictions above all other factors, such as SST and model vertical layers. NAM/WRF and HRRR/WRF were able to capture the decreased wind speed, and there was no single configuration that systematically produced better results. However, when using the predicted wind speed to estimate wind power density, HRRR/WRF had statistically improved results, with lower errors than NAM/WRF. Our work underscored that for predicting offshore wind resources, it is important to evaluate not only the WRF predictive wind speed, but also the connection of wind speed to wind power.

Tasnim Zaman, Timothy Juliano, Pat Hawbecker, and Marina Astitha

Status: open (extended)

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  • RC1: 'Comment on wes-2023-148', Anonymous Referee #1, 27 Jan 2024 reply
Tasnim Zaman, Timothy Juliano, Pat Hawbecker, and Marina Astitha
Tasnim Zaman, Timothy Juliano, Pat Hawbecker, and Marina Astitha

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Short summary
We suggest a model configuration to predict offshore wind speed and wind power density in the Northeast US. We focused on wind droughts, long periods of low wind speed that affect the reliability of wind power generation. We show that wind prediction depends primarily on the initial and boundary conditions, and that it is important to evaluate the connection of wind speed to wind power generation, to select the best model configuration.
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