Open AccessA One‐Year‐Long Evaluation of a Wind‐Farm Parameterization in HARMONIE‐AROME
Author(s) -
Stratum Bart,
Theeuwes Natalie,
Barkmeijer Jan,
Ulft Bert,
Wijnant Ine
Publication year - 2022
Publication title -
journal of advances in modeling earth systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.03
H-Index - 58
ISSN - 1942-2466
DOI - 10.1029/2021ms002947
Subject(s) - environmental science , wind speed , meteorology , renewable energy , offshore wind power , wind power , numerical weather prediction , atmospheric sciences , geography , engineering , geology , electrical engineering
The need to mitigate climate change will boost the demand for renewable energy and lead to more wind turbines both on‐ and offshore. In the near future, the effect these wind farms have on the atmosphere can no longer be neglected. In numerical weather prediction models wind‐farm parameterisations (WFP) can be used to model the effect of wind farms on the atmosphere. There are different modeling approaches, but the parameterization developed by Fitch et al. (2012) is mostly used in previous studies. It models the wind farm as a momentum sink and a source of power production and turbulent kinetic energy. In this paper, we have implemented the Fitch et al. (2012) WFP into HARMONIE‐AROME, the numerical weather prediction model that is currently used by at least 11 national weather services in Europe. We used HARMONIE‐AROME to make year‐long simulations for 2016 with and without the WFP. The results were extensively evaluated using lidar, tower and flight measurements at several locations near wind farms. Including the WFP reduces the model bias for wind speed near offshore wind farms. Wind farms not only affect wind, but also temperature and humidity, especially during stable atmospheric conditions: the enhanced mixing caused by the wind turbines reduces the stratification of temperature and humidity. Including the WFP in HARMONIE‐AROME results in a more realistic representation of the atmosphere near wind farms and makes it a more future‐proof model for weather forecasting.