Land surface parameterization from new satellite sensors

M. Badger*, E. Dellwik, R. Floors, H. Skriver, T. Bondo, K. Grogan, M. Thøgersen

*Corresponding author for this work

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

6 Downloads (Pure)

Abstract

Wind resource assessment using microscale modelling relies on descriptions of the land surface roughness and on maps of the terrain height. This presentation demonstrates the potential of new satellite based sensors for providing novel input data layers for wind energy flow modelling. The ultimate goal is to reduce the uncertainty of wind resource assessment on land.
The aerodynamic roughness length is typically estimated from global or regional land cover maps from satellites (e.g. GlobCover, Corine) in combination with a land-cover-to-roughness translation table. This approach does not take any seasonal variability of the land surface properties into account and the spatial
resolution of the input data sets is coarse. The Sentinel-2 mission from the European Space Agency (ESA) delivers new imagery every ten days at a high spatial resolution. We present a method for dynamic roughness estimation based on the new satellite observations.
An alternative and more physical approach to land surface roughness parameterization is to observe the vegetation height and density and use these properties directly in flow models. Research based on aerial lidar scans show promising results1,2 and several new satellite sensors can deliver similar observations with global coverage. We present examples of vegetation properties given by interferometric coherence and multi-temporal change detection based on Synthetic Aperture Radar (SAR) from ESA’s Sentinel-1 mission. The sensitivity of wind resource estimates to the use of the different vegetation data layers described here is tested through cross-prediction analyses at two forest sites: Østerild in Denmark and Ryningsnäs in Sweden.
A new generation of Digital Elevation Models (DEM) from satellites provide terrain heights at a very high resolution at the global scale. A systematic comparison of terrain heights given by different DEMs at wind turbine sites with a known elevation shows a significant difference between products. We examine the consequence of choosing a given DEM for wind resource assessment.
Overall, there is a vast potential for reducing uncertainties in wind resource assessment by introducing new satellite based data layers as input to flow modelling. The InnoWind project (www.innowind.dk) works towards achieving the full benefit of new satellite based data layers in connection with flow modelling for wind energy through the development of novel data products and upgrading of flow modelling tools in parallel.
Original languageEnglish
Publication date2019
Number of pages1
Publication statusPublished - 2019
EventWind Energy Science Conference 2019 - University College Cork, Cork, Ireland
Duration: 17 Jun 201920 Jun 2019
https://www.wesc2019.org/

Conference

ConferenceWind Energy Science Conference 2019
LocationUniversity College Cork
CountryIreland
CityCork
Period17/06/201920/06/2019
Internet address

Keywords

  • wind resources
  • flow modelling
  • roughness length
  • elevation
  • satellite observations

Cite this

Badger, M., Dellwik, E., Floors, R., Skriver, H., Bondo, T., Grogan, K., & Thøgersen, M. (2019). Land surface parameterization from new satellite sensors. Abstract from Wind Energy Science Conference 2019, Cork, Ireland.
Badger, M. ; Dellwik, E. ; Floors, R. ; Skriver, H. ; Bondo, T. ; Grogan, K. ; Thøgersen, M. / Land surface parameterization from new satellite sensors. Abstract from Wind Energy Science Conference 2019, Cork, Ireland.1 p.
@conference{ffcf4581a00b4849a9bc35770da25b2a,
title = "Land surface parameterization from new satellite sensors",
abstract = "Wind resource assessment using microscale modelling relies on descriptions of the land surface roughness and on maps of the terrain height. This presentation demonstrates the potential of new satellite based sensors for providing novel input data layers for wind energy flow modelling. The ultimate goal is to reduce the uncertainty of wind resource assessment on land.The aerodynamic roughness length is typically estimated from global or regional land cover maps from satellites (e.g. GlobCover, Corine) in combination with a land-cover-to-roughness translation table. This approach does not take any seasonal variability of the land surface properties into account and the spatialresolution of the input data sets is coarse. The Sentinel-2 mission from the European Space Agency (ESA) delivers new imagery every ten days at a high spatial resolution. We present a method for dynamic roughness estimation based on the new satellite observations.An alternative and more physical approach to land surface roughness parameterization is to observe the vegetation height and density and use these properties directly in flow models. Research based on aerial lidar scans show promising results1,2 and several new satellite sensors can deliver similar observations with global coverage. We present examples of vegetation properties given by interferometric coherence and multi-temporal change detection based on Synthetic Aperture Radar (SAR) from ESA’s Sentinel-1 mission. The sensitivity of wind resource estimates to the use of the different vegetation data layers described here is tested through cross-prediction analyses at two forest sites: {\O}sterild in Denmark and Ryningsn{\"a}s in Sweden.A new generation of Digital Elevation Models (DEM) from satellites provide terrain heights at a very high resolution at the global scale. A systematic comparison of terrain heights given by different DEMs at wind turbine sites with a known elevation shows a significant difference between products. We examine the consequence of choosing a given DEM for wind resource assessment. Overall, there is a vast potential for reducing uncertainties in wind resource assessment by introducing new satellite based data layers as input to flow modelling. The InnoWind project (www.innowind.dk) works towards achieving the full benefit of new satellite based data layers in connection with flow modelling for wind energy through the development of novel data products and upgrading of flow modelling tools in parallel.",
keywords = "wind resources, flow modelling, roughness length, elevation, satellite observations",
author = "M. Badger and E. Dellwik and R. Floors and H. Skriver and T. Bondo and K. Grogan and M. Th{\o}gersen",
year = "2019",
language = "English",
note = "Wind Energy Science Conference 2019, WESC 2019 ; Conference date: 17-06-2019 Through 20-06-2019",
url = "https://www.wesc2019.org/",

}

Badger, M, Dellwik, E, Floors, R, Skriver, H, Bondo, T, Grogan, K & Thøgersen, M 2019, 'Land surface parameterization from new satellite sensors', Wind Energy Science Conference 2019, Cork, Ireland, 17/06/2019 - 20/06/2019.

Land surface parameterization from new satellite sensors. / Badger, M.; Dellwik, E.; Floors, R.; Skriver, H.; Bondo, T.; Grogan, K.; Thøgersen, M.

2019. Abstract from Wind Energy Science Conference 2019, Cork, Ireland.

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

TY - ABST

T1 - Land surface parameterization from new satellite sensors

AU - Badger, M.

AU - Dellwik, E.

AU - Floors, R.

AU - Skriver, H.

AU - Bondo, T.

AU - Grogan, K.

AU - Thøgersen, M.

PY - 2019

Y1 - 2019

N2 - Wind resource assessment using microscale modelling relies on descriptions of the land surface roughness and on maps of the terrain height. This presentation demonstrates the potential of new satellite based sensors for providing novel input data layers for wind energy flow modelling. The ultimate goal is to reduce the uncertainty of wind resource assessment on land.The aerodynamic roughness length is typically estimated from global or regional land cover maps from satellites (e.g. GlobCover, Corine) in combination with a land-cover-to-roughness translation table. This approach does not take any seasonal variability of the land surface properties into account and the spatialresolution of the input data sets is coarse. The Sentinel-2 mission from the European Space Agency (ESA) delivers new imagery every ten days at a high spatial resolution. We present a method for dynamic roughness estimation based on the new satellite observations.An alternative and more physical approach to land surface roughness parameterization is to observe the vegetation height and density and use these properties directly in flow models. Research based on aerial lidar scans show promising results1,2 and several new satellite sensors can deliver similar observations with global coverage. We present examples of vegetation properties given by interferometric coherence and multi-temporal change detection based on Synthetic Aperture Radar (SAR) from ESA’s Sentinel-1 mission. The sensitivity of wind resource estimates to the use of the different vegetation data layers described here is tested through cross-prediction analyses at two forest sites: Østerild in Denmark and Ryningsnäs in Sweden.A new generation of Digital Elevation Models (DEM) from satellites provide terrain heights at a very high resolution at the global scale. A systematic comparison of terrain heights given by different DEMs at wind turbine sites with a known elevation shows a significant difference between products. We examine the consequence of choosing a given DEM for wind resource assessment. Overall, there is a vast potential for reducing uncertainties in wind resource assessment by introducing new satellite based data layers as input to flow modelling. The InnoWind project (www.innowind.dk) works towards achieving the full benefit of new satellite based data layers in connection with flow modelling for wind energy through the development of novel data products and upgrading of flow modelling tools in parallel.

AB - Wind resource assessment using microscale modelling relies on descriptions of the land surface roughness and on maps of the terrain height. This presentation demonstrates the potential of new satellite based sensors for providing novel input data layers for wind energy flow modelling. The ultimate goal is to reduce the uncertainty of wind resource assessment on land.The aerodynamic roughness length is typically estimated from global or regional land cover maps from satellites (e.g. GlobCover, Corine) in combination with a land-cover-to-roughness translation table. This approach does not take any seasonal variability of the land surface properties into account and the spatialresolution of the input data sets is coarse. The Sentinel-2 mission from the European Space Agency (ESA) delivers new imagery every ten days at a high spatial resolution. We present a method for dynamic roughness estimation based on the new satellite observations.An alternative and more physical approach to land surface roughness parameterization is to observe the vegetation height and density and use these properties directly in flow models. Research based on aerial lidar scans show promising results1,2 and several new satellite sensors can deliver similar observations with global coverage. We present examples of vegetation properties given by interferometric coherence and multi-temporal change detection based on Synthetic Aperture Radar (SAR) from ESA’s Sentinel-1 mission. The sensitivity of wind resource estimates to the use of the different vegetation data layers described here is tested through cross-prediction analyses at two forest sites: Østerild in Denmark and Ryningsnäs in Sweden.A new generation of Digital Elevation Models (DEM) from satellites provide terrain heights at a very high resolution at the global scale. A systematic comparison of terrain heights given by different DEMs at wind turbine sites with a known elevation shows a significant difference between products. We examine the consequence of choosing a given DEM for wind resource assessment. Overall, there is a vast potential for reducing uncertainties in wind resource assessment by introducing new satellite based data layers as input to flow modelling. The InnoWind project (www.innowind.dk) works towards achieving the full benefit of new satellite based data layers in connection with flow modelling for wind energy through the development of novel data products and upgrading of flow modelling tools in parallel.

KW - wind resources

KW - flow modelling

KW - roughness length

KW - elevation

KW - satellite observations

M3 - Conference abstract for conference

ER -

Badger M, Dellwik E, Floors R, Skriver H, Bondo T, Grogan K et al. Land surface parameterization from new satellite sensors. 2019. Abstract from Wind Energy Science Conference 2019, Cork, Ireland.