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Summary of Proposal RES3085

TitleUsing TerraSAR-X data for Reducing Uncertainties of Near-shore wind resource Estimates (RUNE)
Investigator Karagali, Ioanna - DTU - Technical University of Denmark, DTU Wind Energy
Team MemberNo team members defined
SummaryObjectives Since existing wind measurements in near-shore* and offshore areas are both sparse and scarce, simulations from state-of-the-art mesoscale wind models are being used for wind resource predictions [1-3]. In coastal and near-shore areas, such models are rather inaccurate and uncertain, primarily due to their numerical approximations** that do not properly account for the atmospheric stability, land-sea interactions and local topographic features. We estimate that the uncertainty of these models can be as much as 10% on wind speed at such areas based on idealized experiments***. The accuracy of modelled wind resource predictions, however, can be significantly improved by using local wind measurements to‘calibrate’ the models. We estimate that the model uncertainty (for wind speed) can be reduced to about 3% by the use of local measurements. The RUNE project is designed to investigate cost-effective measurement solutions for improving wind resource modelling of coastal areas. Measuring technologies to be deployed are a floating, an onshore scanning and an onshore standard lidar for measuring the offshore vertical wind profile, the near-shore wind field and the wind profile at the coastline, respectively. Further, RUNE will benefit from DTU Wind Energy’s archive of satellite offshore wind maps with spatial resolutions of about 1 km^2 to quantify the spatial wind variations in the coastal zone at 10m above the sea level. Wind retrievals from ENVISAT ASAR and Sentinel-1 can be compared to the lidar near-shore scans and mesoscale model simulations for improving the understanding of the near-shore wind climatology and evaluation of the near-shore wind resource uncertainty. In addition, the project aims to utilize TerraSAR-X data for the same purpose. *The offshore area that extends up to about 10-12 km from the coast **Due to both the numerical recipes used in the model to solve equations and the difference between the model equations and parameterizations compared to the real atmosphere ***Simulations with simplified atmospheric forcing, prescribed topography and surface conditions Method The obtained TerraSAR-X images will be processed with the APL/NOAA (Applied Physics Laboratory/National Oceanic Atmospheric Administration) SAR Wind Retrieval System (ANSWRS) software, developed at the Johns Hopkins University, to derive the wind speed, using a Geophysical Model Function (GMF) specifically developed for X-band instruments [4]. A priori wind direction information will be derived both from in situ measurements but also from the Global Forecast System (GFS) model. Retrieved wind speeds will then be compared to the in situ lidar scans and the high resolution model runs. Data Requirements The project requires TerraSAR-X images for the area of interest from mid-November 2015 and until the end of February 2016. We require as many scans as possible Deliverables Preliminary results are to be expected by the end of January 2016. The final report is to be expected after the end of the project, in March 2016. Funding Acquisition of the TerraSAR-X data will be funded through the RUNE project, which is funded by the Danish Energinet PSO (Public Service Obligation) ForskEL and ForskVE programmes. References [1] Vincent C.L. (2010) Mesoscale Wind Fluctuations over Danish Waters. Risø-PhD-70(EN),Risø DTU, Roskilde [2] Peña A. and Hahmann A.N. (2012) Atmospheric stability and turbulence fluxes at Horns Rev—an intercomparison of sonic, bulk and WRF model data. Wind Energy (15):717—731 [3] Hahmann, A.N., Vincent,C.L., Peña, A., Lange, J., & Hasager,C.B. (2015) Wind climate estimation using WRF model output: Method and model sensitivity over the sea. International Journal of Climatology, 35:3422–3439 (2015) DOI: 10.1002/joc.4217 [4] X.-M.Li and S. Lehner, “Sea surface wind by TerraSAR-X and Tandem-X:Algorithm development,” IEEE Trans. Geosci. Remote Sens.,2013, DOI: 10.1109/TGRS.2013.2267780

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