Merete Badger

SAR-based Wind Resource Statistics in the Baltic Sea

Ocean winds in the Baltic Sea are expected to power many wind farms in the coming years. This study examines satellite Synthetic Aperture Radar (SAR) images from Envisat ASAR for mapping wind resources with high spatial resolution. Around 900 collocated pairs of wind speed from SAR wind maps and from 10 meteorological masts, established specifically for wind energy in the study area, are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a root mean square error of 1.17 m s−1, bias of −0.25 m s−1, standard deviation of 1.88 m s−1 and correlation coefficient of R2 0.783. Wind directions from a global atmospheric model, interpolated in time and space, are used as input to the geophysical model function CMOD-5 for SAR wind retrieval. Wind directions compared to mast observations show a root mean square error of 6.29° with a bias of 7.75°, standard deviation of 20.11° and R2 of 0.950. The scale and shape parameters, A and k, respectively, from the Weibull probability density function are compared at only one available mast and the results deviate ~2% for A but ~16% for k. Maps of A and k, and wind power density based on more than 1000 satellite images show wind power density values to range from 300 to 800 W m−2 for the 14 existing and 42 planned wind farms.

Wind Class Sampling of Satellite SAR Imagery for Offshore Wind Resource Mapping

Abstract High-resolution wind fields retrieved from satellite synthetic aperture radar (SAR) imagery are combined for mapping of wind resources offshore where site measurements are costly and sparse. A new sampling strategy for the SAR scenes is introduced, based on a method for statistical–dynamical downscaling of large-scale wind conditions using a set of wind classes that describe representative wind situations. One or more SAR scenes are then selected to represent each wind class and the classes are weighted according to their frequency of occurrence. The wind class methodology was originally developed for mesoscale modeling of wind resources. Its performance in connection with sampling of SAR scenes is tested against two sets of random SAR samples and meteorological observations at three sites in the North Sea during 2005–08. Predictions of the mean wind speed and the Weibull scale parameter are within 5% from the mast observations whereas the deviation on power density and the Weibull shape paramete...

Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea

Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data.

Comparison of Geophysical Model Functions for SAR Wind Speed Retrieval in Japanese Coastal Waters

This work discusses the accuracies of geophysical model functions (GMFs) for retrieval of sea surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images in Japanese coastal waters characterized by short fetches and variable atmospheric stability conditions. In situ observations from two validation sites, Hiratsuka and Shirahama, are used for comparison of the retrieved sea surface wind speeds using CMOD (C-band model)4, CMOD_IFR2, CMOD5 and CMOD5.N. Of all the geophysical model functions (GMFs), the latest C-band GMF, CMOD5.N, has the smallest bias and root mean square error at both sites. All of the GMFs exhibit a negative bias in the retrieved wind speed. In order to understand the reason for this bias, all SAR-retrieved wind speeds are separated into two categories: onshore wind (blowing from sea to land) and offshore wind (blowing from land to sea). Only offshore winds were found to exhibit the large negative bias, and short fetches from the coastline may be a possible reason for this. Moreover, it is clarified that in both the unstable and stable conditions, CMOD5.N has atmospheric stability effectiveness, and can keep the same accuracy with CMOD5 in the neutral condition. In short, at the moment, CMOD5.N is thought to be the most promising GMF for the SAR wind speed retrieval with the atmospheric stability correction in Japanese coastal waters, although there is ample room for future improvement for the effect from short fetch.

Extrapolating Satellite Winds to Turbine Operating Heights

AbstractOcean wind retrievals from satellite sensors are typically performed for the standard level of 10 m. This restricts their full exploitation for wind energy planning, which requires wind information at much higher levels where wind turbines operate. A new method is presented for the vertical extrapolation of satellite-based wind maps. Winds near the sea surface are obtained from satellite data and used together with an adaptation of the Monin–Obukhov similarity theory to estimate the wind speed at higher levels. The thermal stratification of the atmosphere is taken into account through a long-term stability correction that is based on numerical weather prediction (NWP) model outputs. The effect of the long-term stability correction on the wind profile is significant. The method is applied to Envisat Advanced Synthetic Aperture Radar scenes acquired over the south Baltic Sea. This leads to maps of the long-term stability correction and wind speed at a height of 100 m with a spatial resolution of 0.0...

Comparing satellite SAR and wind farm wake models

The aim of the paper is to present offshore wind farm wake observed from satellite Synthetic Aperture Radar (SAR) wind fields from RADARSAT-1/-2 and Envisat and to compare these wakes qualitatively to wind farm wake model results. From some satellite SAR wind maps very long wakes are observed. These extend several tens of kilometres downwind e.g. 70 km. Other SAR wind maps show near-field fine scale details of wake behind rows of turbines. The satellite SAR wind farm wake cases are modelled by different wind farm wake models including the PARK microscale model, the Weather Research and Forecasting (WRF) model in high resolution and WRF with coupled microscale parametrization.

Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

High-accuracy wind data for coastal regions is needed today, e.g., for the assessment of wind resources. Synthetic Aperture Radar (SAR) is the only satellite borne sensor that has enough resolution to resolve wind speeds closer than 10 km to shore but the Geophysical Model Functions (GMF) used for SAR wind retrieval are not fully validated here. Ground based scanning light detection and ranging (LiDAR) offer high horizontal resolution wind velocity measurements with high accuracy, also in the coastal zone. This study, for the first time, examines accuracies of SAR wind retrievals at 10 m height with respect to the distance to shore by validation against scanning LiDARs. Comparison of 15 Sentinel-1A wind retrievals using the GMF called C-band model 5.N (CMOD5.N) versus LiDARs show good agreement. It is found, when nondimenionalising with a reference point, that wind speed reductions are between 4% and 8% from 3 km to 1 km from shore. Findings indicate that SAR wind retrievals give reliable wind speed measurements as close as 1 km to the shore. Comparisons of SAR winds versus two different LiDAR configurations yield root mean square error (RMSE) of 1.31 ms − 1 and 1.42 ms − 1 for spatially averaged wind speeds.

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor Microwave Imager (SSM/I) ocean winds extrapolated from 10 m to 100 m using the Charnock relationship and the logarithmic profile method are compared to Weather Research and Forecasting (WRF) model results in both seas and to in-situ observations in the North Sea. The mean wind speed from SSM/I and WRF differ only by 0.1 m/s at Fino1 in the North Sea, while west of Hainan in the South China Sea the difference is 1.0 m/s. Linear regression between SSM/I and WRF winds at 100 m show correlation coefficients squared of 0.75 and 0.67, standard deviation of 1.67 m/s and 1.41 m/s, and mean difference of −0.12 m/s and 0.83 m/s for Fino1 and Hainan, respectively. The WRF-derived winds overestimate the values in the South China Sea. The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas.

Spectral Properties of ENVISAT ASAR and QuikSCAT Surface Winds in the North Sea

Spectra derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) and QuikSCAT near-surface ocean winds are investigated over the North Sea. The two sensors offer a wide range of spatial resolutions, from 600 m to 25 km, with different spatial coverage over the area of interest. This provides a unique opportunity to study the impact of the spatial resolution on the spectral properties of the wind over a wide range of length scales. Initially, a sub-domain in the North Sea is chosen, due to the overlap of 87 wind scenes from both sensors. The impact of the spatial resolution is manifested as an increase in spectral density over similar wavenumber ranges as the spatial resolution increases. The 600-m SAR wind product reveals a range of wavenumbers in which the exchange processes between micro- and meso-scales occur; this range is not captured by the wind products with a resolution of 1.5 km or lower. The lower power levels of coarser resolution wind products, particularly when comparing QuikSCAT to ENVISAT ASAR, strongly suggest that the effective resolution of the wind products should be high enough to resolve the spectral properties. Spectra computed from 87 wind maps are consistent with those obtained from several thousands of samples. Long-term spectra from QuikSCAT show that during the winter, slightly higher energy content is identified compared to the other seasons.

Advances in Offshore Wind Resource Estimation

Wind resource mapping is basically a meteorological time-series statistical analysis, to which the features of the landscape such as roughness, topography and local obstacles are integrated. The normal procedure is to use the WAsP program which is de facto standard for wind turbine siting]. The basic principle of the program is to solve the atmospheric flow equation using the logarithmic wind profile law and then to include the effects of the terrain. The optimal situation is to have accurate, long-term wind and turbulence observations from the height in the atmospheric boundary layer at the site where a wind farm is envisioned. This information provides the basis for wind resource mapping, identifying extreme conditions and wind load on the turbines.