Wind climate estimation using WRF model output: method and model sensitivities over the sea

Andrea N. Hahmann, Claire Louise Vincent, Alfredo Peña, Julia Lange, Charlotte Bay Hasager

    Research output: Contribution to journalJournal articleResearchpeer-review


    High-quality tall mast and wind lidar measurements over the North and Baltic Seas are used to validate the wind climatology produced from winds simulated by the Weather, Research and Forecasting (WRF) model in analysis mode. Biases in annual mean wind speed between model and observations at heights around 100m are smaller than 3.2% at offshore sites, except for those that are affected by the wake of a wind farm or the coastline. These biases are smaller than those obtained by using winds directly from the reanalysis. We study the sensitivity of the WRF-simulated wind climatology to various model setup parameters. The results of the year-long sensitivity simulations show that the long-term mean wind speed simulated by the WRF model offshore in the region studied is quite insensitive to the global reanalysis, the number of vertical levels, and the horizontal resolution of the sea surface temperature used as lower boundary conditions. Also, the strength and form (grid vs spectral) of the nudging is quite irrelevant for the mean wind speed at 100 m. Large sensitivity is found to the choice of boundary layer parametrization, and to the length of the period that is discarded as spin-up to produce a wind climatology. It is found that the spin-up period for the boundary layer winds is likely larger than 12 h over land and could affect the wind climatology for points offshore for quite a distance downstream from the coast.
    Original languageEnglish
    JournalInternational Journal of Climatology
    Pages (from-to)3422-3439
    Number of pages18
    Publication statusPublished - 2015


    • Wind climate
    • Offshore wind
    • Wind power resources
    • WRF
    • North and Baltic Seas
    • Wind shear


    Dive into the research topics of 'Wind climate estimation using WRF model output: method and model sensitivities over the sea'. Together they form a unique fingerprint.

    Cite this