Wind resources at turbine height from Envisat and Sentinel-1 SAR

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

A comprehensive database with ocean wind fields has been built up at the Technical University of Denmark (DTU) through consistent processing of Synthetic Aperture Radar (SAR) observations from Envisat (2002-12) and Sentinel-1 (2014-present). The archived wind fields cover the European seas up to 100 km from the coastline. They can be seen as a series of snapshots showing the instantaneous wind conditions for the areas most relevant for offshore wind power generation. Through statistical processing, these instantaneous snapshots are combined to give maps of the offshore wind resources for the standard output level of 10 m above the sea surface. This presentation demonstrates the effects of two recent improvements related to satellite-based wind resource mapping:
1) The number of satellite samples has increased dramatically since the launch of Sentinel-1A/B
2) A new method looks promising for routine extrapolation of wind fields to the height of modern wind turbines
At DTU, wind maps are retrieved in near-real-time from ESA’s L1 SAR products using the SAROPS processing tool developed by the US National Oceanic and Atmospheric Administration (NOAA). The geophysical model function CMOD5.N is used to obtain the equivalent neutral wind speed. A correction is applied to compensate for lower radar backscatter at HH polarization compared to VV polarization. Ancillary data used for the SAR-wind processing include wind directions from the Global Forecast System (GFS) and ice mask data from the US National Ice Center.
Once the instantaneous wind maps are stored in our database, they can be organized as time series in order to calculate wind resources for any point location or area. Since the time series comprises data from both Envisat and Sentinel-1, a check of the data calibration against one or more independent data sources is needed. Based on the calibrated time series, a Weibull fit is made to calculate the mean wind speed, Weibull scale and shape parameters, and the wind power density. The spatial grid of the output wind resource maps is 0.02 degrees in latitude and longitude. To extrapolate the 10-m wind resource maps from SAR to higher levels within the atmospheric boundary layer, we estimate a wind profile for each grid cell in the maps. Simulations from the Weather Research and Forecasting (WRF) model are used to correct this profile for long-term atmospheric stability effects. Accounting for atmospheric stability allows us to estimate the wind speed at different levels with greater accuracy compared to methods that assume a neutral atmospheric boundary layer. For the Northern European seas, the inclusion of atmospheric stability increases the mean wind speed at 100 m on the order of 0.5m/s.
The SAR-based wind resource maps are used in the New European Wind Atlas – an EU-funded project where European nations work together to produce an updated and validated wind atlas for Europe
Original languageEnglish
Publication date2016
Number of pages1
Publication statusPublished - 2016
EventESA Living Planet Symposium 2016 - Prague, Czech Republic
Duration: 9 May 201613 May 2016
http://lps16.esa.int/

Conference

ConferenceESA Living Planet Symposium 2016
CountryCzech Republic
CityPrague
Period09/05/201613/05/2016
Internet address

Bibliographical note

Oral presentation; http://lps16.esa.int/page_session172.php#421p

Cite this

Badger, M., Hasager, C. B., Pena Diaz, A., Hahmann, A. N., & Volker, P. (2016). Wind resources at turbine height from Envisat and Sentinel-1 SAR. Paper presented at ESA Living Planet Symposium 2016, Prague, Czech Republic.
Badger, Merete ; Hasager, Charlotte Bay ; Pena Diaz, Alfredo ; Hahmann, Andrea N. ; Volker, Patrick. / Wind resources at turbine height from Envisat and Sentinel-1 SAR. Paper presented at ESA Living Planet Symposium 2016, Prague, Czech Republic.1 p.
@conference{6044667d7cfc448fa4bc362ed86d3a13,
title = "Wind resources at turbine height from Envisat and Sentinel-1 SAR",
abstract = "A comprehensive database with ocean wind fields has been built up at the Technical University of Denmark (DTU) through consistent processing of Synthetic Aperture Radar (SAR) observations from Envisat (2002-12) and Sentinel-1 (2014-present). The archived wind fields cover the European seas up to 100 km from the coastline. They can be seen as a series of snapshots showing the instantaneous wind conditions for the areas most relevant for offshore wind power generation. Through statistical processing, these instantaneous snapshots are combined to give maps of the offshore wind resources for the standard output level of 10 m above the sea surface. This presentation demonstrates the effects of two recent improvements related to satellite-based wind resource mapping:1) The number of satellite samples has increased dramatically since the launch of Sentinel-1A/B2) A new method looks promising for routine extrapolation of wind fields to the height of modern wind turbinesAt DTU, wind maps are retrieved in near-real-time from ESA’s L1 SAR products using the SAROPS processing tool developed by the US National Oceanic and Atmospheric Administration (NOAA). The geophysical model function CMOD5.N is used to obtain the equivalent neutral wind speed. A correction is applied to compensate for lower radar backscatter at HH polarization compared to VV polarization. Ancillary data used for the SAR-wind processing include wind directions from the Global Forecast System (GFS) and ice mask data from the US National Ice Center.Once the instantaneous wind maps are stored in our database, they can be organized as time series in order to calculate wind resources for any point location or area. Since the time series comprises data from both Envisat and Sentinel-1, a check of the data calibration against one or more independent data sources is needed. Based on the calibrated time series, a Weibull fit is made to calculate the mean wind speed, Weibull scale and shape parameters, and the wind power density. The spatial grid of the output wind resource maps is 0.02 degrees in latitude and longitude. To extrapolate the 10-m wind resource maps from SAR to higher levels within the atmospheric boundary layer, we estimate a wind profile for each grid cell in the maps. Simulations from the Weather Research and Forecasting (WRF) model are used to correct this profile for long-term atmospheric stability effects. Accounting for atmospheric stability allows us to estimate the wind speed at different levels with greater accuracy compared to methods that assume a neutral atmospheric boundary layer. For the Northern European seas, the inclusion of atmospheric stability increases the mean wind speed at 100 m on the order of 0.5m/s. The SAR-based wind resource maps are used in the New European Wind Atlas – an EU-funded project where European nations work together to produce an updated and validated wind atlas for Europe",
author = "Merete Badger and Hasager, {Charlotte Bay} and {Pena Diaz}, Alfredo and Hahmann, {Andrea N.} and Patrick Volker",
note = "Oral presentation; http://lps16.esa.int/page_session172.php#421p ; ESA Living Planet Symposium 2016 ; Conference date: 09-05-2016 Through 13-05-2016",
year = "2016",
language = "English",
url = "http://lps16.esa.int/",

}

Badger, M, Hasager, CB, Pena Diaz, A, Hahmann, AN & Volker, P 2016, 'Wind resources at turbine height from Envisat and Sentinel-1 SAR' Paper presented at ESA Living Planet Symposium 2016, Prague, Czech Republic, 09/05/2016 - 13/05/2016, .

Wind resources at turbine height from Envisat and Sentinel-1 SAR. / Badger, Merete; Hasager, Charlotte Bay; Pena Diaz, Alfredo; Hahmann, Andrea N.; Volker, Patrick.

2016. Paper presented at ESA Living Planet Symposium 2016, Prague, Czech Republic.

Research output: Contribution to conferencePaperResearchpeer-review

TY - CONF

T1 - Wind resources at turbine height from Envisat and Sentinel-1 SAR

AU - Badger, Merete

AU - Hasager, Charlotte Bay

AU - Pena Diaz, Alfredo

AU - Hahmann, Andrea N.

AU - Volker, Patrick

N1 - Oral presentation; http://lps16.esa.int/page_session172.php#421p

PY - 2016

Y1 - 2016

N2 - A comprehensive database with ocean wind fields has been built up at the Technical University of Denmark (DTU) through consistent processing of Synthetic Aperture Radar (SAR) observations from Envisat (2002-12) and Sentinel-1 (2014-present). The archived wind fields cover the European seas up to 100 km from the coastline. They can be seen as a series of snapshots showing the instantaneous wind conditions for the areas most relevant for offshore wind power generation. Through statistical processing, these instantaneous snapshots are combined to give maps of the offshore wind resources for the standard output level of 10 m above the sea surface. This presentation demonstrates the effects of two recent improvements related to satellite-based wind resource mapping:1) The number of satellite samples has increased dramatically since the launch of Sentinel-1A/B2) A new method looks promising for routine extrapolation of wind fields to the height of modern wind turbinesAt DTU, wind maps are retrieved in near-real-time from ESA’s L1 SAR products using the SAROPS processing tool developed by the US National Oceanic and Atmospheric Administration (NOAA). The geophysical model function CMOD5.N is used to obtain the equivalent neutral wind speed. A correction is applied to compensate for lower radar backscatter at HH polarization compared to VV polarization. Ancillary data used for the SAR-wind processing include wind directions from the Global Forecast System (GFS) and ice mask data from the US National Ice Center.Once the instantaneous wind maps are stored in our database, they can be organized as time series in order to calculate wind resources for any point location or area. Since the time series comprises data from both Envisat and Sentinel-1, a check of the data calibration against one or more independent data sources is needed. Based on the calibrated time series, a Weibull fit is made to calculate the mean wind speed, Weibull scale and shape parameters, and the wind power density. The spatial grid of the output wind resource maps is 0.02 degrees in latitude and longitude. To extrapolate the 10-m wind resource maps from SAR to higher levels within the atmospheric boundary layer, we estimate a wind profile for each grid cell in the maps. Simulations from the Weather Research and Forecasting (WRF) model are used to correct this profile for long-term atmospheric stability effects. Accounting for atmospheric stability allows us to estimate the wind speed at different levels with greater accuracy compared to methods that assume a neutral atmospheric boundary layer. For the Northern European seas, the inclusion of atmospheric stability increases the mean wind speed at 100 m on the order of 0.5m/s. The SAR-based wind resource maps are used in the New European Wind Atlas – an EU-funded project where European nations work together to produce an updated and validated wind atlas for Europe

AB - A comprehensive database with ocean wind fields has been built up at the Technical University of Denmark (DTU) through consistent processing of Synthetic Aperture Radar (SAR) observations from Envisat (2002-12) and Sentinel-1 (2014-present). The archived wind fields cover the European seas up to 100 km from the coastline. They can be seen as a series of snapshots showing the instantaneous wind conditions for the areas most relevant for offshore wind power generation. Through statistical processing, these instantaneous snapshots are combined to give maps of the offshore wind resources for the standard output level of 10 m above the sea surface. This presentation demonstrates the effects of two recent improvements related to satellite-based wind resource mapping:1) The number of satellite samples has increased dramatically since the launch of Sentinel-1A/B2) A new method looks promising for routine extrapolation of wind fields to the height of modern wind turbinesAt DTU, wind maps are retrieved in near-real-time from ESA’s L1 SAR products using the SAROPS processing tool developed by the US National Oceanic and Atmospheric Administration (NOAA). The geophysical model function CMOD5.N is used to obtain the equivalent neutral wind speed. A correction is applied to compensate for lower radar backscatter at HH polarization compared to VV polarization. Ancillary data used for the SAR-wind processing include wind directions from the Global Forecast System (GFS) and ice mask data from the US National Ice Center.Once the instantaneous wind maps are stored in our database, they can be organized as time series in order to calculate wind resources for any point location or area. Since the time series comprises data from both Envisat and Sentinel-1, a check of the data calibration against one or more independent data sources is needed. Based on the calibrated time series, a Weibull fit is made to calculate the mean wind speed, Weibull scale and shape parameters, and the wind power density. The spatial grid of the output wind resource maps is 0.02 degrees in latitude and longitude. To extrapolate the 10-m wind resource maps from SAR to higher levels within the atmospheric boundary layer, we estimate a wind profile for each grid cell in the maps. Simulations from the Weather Research and Forecasting (WRF) model are used to correct this profile for long-term atmospheric stability effects. Accounting for atmospheric stability allows us to estimate the wind speed at different levels with greater accuracy compared to methods that assume a neutral atmospheric boundary layer. For the Northern European seas, the inclusion of atmospheric stability increases the mean wind speed at 100 m on the order of 0.5m/s. The SAR-based wind resource maps are used in the New European Wind Atlas – an EU-funded project where European nations work together to produce an updated and validated wind atlas for Europe

M3 - Paper

ER -

Badger M, Hasager CB, Pena Diaz A, Hahmann AN, Volker P. Wind resources at turbine height from Envisat and Sentinel-1 SAR. 2016. Paper presented at ESA Living Planet Symposium 2016, Prague, Czech Republic.