Correlation of mesoscale wind speeds over the sea

Anna R. Mehrens, Andrea N. Hahmann, Andrea N. Hahmann, Xiaoli Guo Larsén, Lueder von Bremen

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Abstract

A large offshore observational data set from stations across the North and Baltic Sea is used to investigate the planetary boundary layer wind characteristics and their coherence, correlation and power spectra. The data of thirteen sites, with pairs of sites at a horizontal distance of 4 to 848 km, are analyzed for typical wind turbine nacelle heights. Mean wind characteristics, correlation and coherence are also calculated for analogous wind data from simulations with the Weather Research and Forecasting (WRF) model.

Results indicate a general good agreement for the coherence calculated based on measurements and the WRF-derived time series. By normalising the frequency axes with the distance and mean wind speed it can be demonstrated that even for data with a wide range of distances, the coherence is a function of the frequency, mean wind and distance, which is consistent with earlier studies. The correlation coefficient as a function of the distance calculated from WRF is however higher than observed in the measurements. For the power spectra, wind speed and wind speed step changes distribution the results for all sites are quite similar. The land masses strongly influence the individual wind direction distribution of each site. The ability of the WRF model to reproduce the coherence of the measurements demonstrates that its output can be used to estimate the coherence of fluctuations for the integration of offshore energy. The power spectra of WRF time series underestimates the high frequency fluctuations. Due to the large number of measurement sites, the results can be used for further plausibility validation for mesoscale model runs over the sea.
Original languageEnglish
JournalQuarterly Journal of the Royal Meteorological Society
Volume142
Issue number701 Part B
Pages (from-to)3186–3194
Number of pages12
ISSN0035-9009
DOIs
Publication statusPublished - 2016

Keywords

  • Coherence
  • Cross-spectrum
  • Power spectrum
  • Offshore
  • Wind integration
  • Wind correlation

Cite this

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title = "Correlation of mesoscale wind speeds over the sea",
abstract = "A large offshore observational data set from stations across the North and Baltic Sea is used to investigate the planetary boundary layer wind characteristics and their coherence, correlation and power spectra. The data of thirteen sites, with pairs of sites at a horizontal distance of 4 to 848 km, are analyzed for typical wind turbine nacelle heights. Mean wind characteristics, correlation and coherence are also calculated for analogous wind data from simulations with the Weather Research and Forecasting (WRF) model.Results indicate a general good agreement for the coherence calculated based on measurements and the WRF-derived time series. By normalising the frequency axes with the distance and mean wind speed it can be demonstrated that even for data with a wide range of distances, the coherence is a function of the frequency, mean wind and distance, which is consistent with earlier studies. The correlation coefficient as a function of the distance calculated from WRF is however higher than observed in the measurements. For the power spectra, wind speed and wind speed step changes distribution the results for all sites are quite similar. The land masses strongly influence the individual wind direction distribution of each site. The ability of the WRF model to reproduce the coherence of the measurements demonstrates that its output can be used to estimate the coherence of fluctuations for the integration of offshore energy. The power spectra of WRF time series underestimates the high frequency fluctuations. Due to the large number of measurement sites, the results can be used for further plausibility validation for mesoscale model runs over the sea.",
keywords = "Coherence, Cross-spectrum, Power spectrum, Offshore, Wind integration, Wind correlation",
author = "Mehrens, {Anna R.} and Hahmann, {Andrea N.} and Hahmann, {Andrea N.} and Lars{\'e}n, {Xiaoli Guo} and {von Bremen}, Lueder",
year = "2016",
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Correlation of mesoscale wind speeds over the sea. / Mehrens, Anna R.; Hahmann, Andrea N.; Hahmann, Andrea N.; Larsén, Xiaoli Guo; von Bremen, Lueder.

In: Quarterly Journal of the Royal Meteorological Society, Vol. 142, No. 701 Part B, 2016, p. 3186–3194.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Correlation of mesoscale wind speeds over the sea

AU - Mehrens, Anna R.

AU - Hahmann, Andrea N.

AU - Hahmann, Andrea N.

AU - Larsén, Xiaoli Guo

AU - von Bremen, Lueder

PY - 2016

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N2 - A large offshore observational data set from stations across the North and Baltic Sea is used to investigate the planetary boundary layer wind characteristics and their coherence, correlation and power spectra. The data of thirteen sites, with pairs of sites at a horizontal distance of 4 to 848 km, are analyzed for typical wind turbine nacelle heights. Mean wind characteristics, correlation and coherence are also calculated for analogous wind data from simulations with the Weather Research and Forecasting (WRF) model.Results indicate a general good agreement for the coherence calculated based on measurements and the WRF-derived time series. By normalising the frequency axes with the distance and mean wind speed it can be demonstrated that even for data with a wide range of distances, the coherence is a function of the frequency, mean wind and distance, which is consistent with earlier studies. The correlation coefficient as a function of the distance calculated from WRF is however higher than observed in the measurements. For the power spectra, wind speed and wind speed step changes distribution the results for all sites are quite similar. The land masses strongly influence the individual wind direction distribution of each site. The ability of the WRF model to reproduce the coherence of the measurements demonstrates that its output can be used to estimate the coherence of fluctuations for the integration of offshore energy. The power spectra of WRF time series underestimates the high frequency fluctuations. Due to the large number of measurement sites, the results can be used for further plausibility validation for mesoscale model runs over the sea.

AB - A large offshore observational data set from stations across the North and Baltic Sea is used to investigate the planetary boundary layer wind characteristics and their coherence, correlation and power spectra. The data of thirteen sites, with pairs of sites at a horizontal distance of 4 to 848 km, are analyzed for typical wind turbine nacelle heights. Mean wind characteristics, correlation and coherence are also calculated for analogous wind data from simulations with the Weather Research and Forecasting (WRF) model.Results indicate a general good agreement for the coherence calculated based on measurements and the WRF-derived time series. By normalising the frequency axes with the distance and mean wind speed it can be demonstrated that even for data with a wide range of distances, the coherence is a function of the frequency, mean wind and distance, which is consistent with earlier studies. The correlation coefficient as a function of the distance calculated from WRF is however higher than observed in the measurements. For the power spectra, wind speed and wind speed step changes distribution the results for all sites are quite similar. The land masses strongly influence the individual wind direction distribution of each site. The ability of the WRF model to reproduce the coherence of the measurements demonstrates that its output can be used to estimate the coherence of fluctuations for the integration of offshore energy. The power spectra of WRF time series underestimates the high frequency fluctuations. Due to the large number of measurement sites, the results can be used for further plausibility validation for mesoscale model runs over the sea.

KW - Coherence

KW - Cross-spectrum

KW - Power spectrum

KW - Offshore

KW - Wind integration

KW - Wind correlation

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