Satellite Synthetic Aperture Radar Wind Research Articles

Inter-calibration of SAR data series for offshore wind resource assessment

Wind observations in the marine environment are both costly and sparse. This makes wind retrievals from satellite Synthetic Aperture Radar (SAR) an attractive option in connection with planning of offshore wind farms. Because the wind power density is proportional to the wind speed cubed, it is important to achieve the highest possible absolute accuracy on SAR wind speed retrievals for wind energy applications. A method is presented for inter-calibration of SAR observations from Envisat and Sentinel-1A/B. Sensor-specific effects on the SAR-retrieved wind speeds are first quantified through comparisons against collocated ocean buoy observations. Based on global circulation model simulations of wind speed and direction, we retrieve the Normalized Radar Cross Section (NRCS) for different radar incidence angles. Residuals between the retrieved and the observed NRCS are used to inter-calibrate the observed NRCS before reprocessing to SAR wind fields. The inter-calibration leads to an improved agreement between SAR and buoy wind speeds with biases below 0.2 m s−1 for all investigated SAR sensors. Estimates of the wind resource improve with respect to the buoy observations for ten of the twelve sites investigated. The average deviation between wind power densities is reduced from 20% to 8% as the SAR inter-calibration leads to more conservative estimates of the wind resource.

Satellite SAR observation of the sea surface wind field caused by rain cells

Rain cells or convective rain, the dominant form of rain in the tropics and subtropics, can be easy detected by satellite Synthetic Aperture Radar (SAR) images with high horizontal resolution. The footprints of rain cells on SAR images are caused by the scattering and attenuation of the rain drops, as well as the downward airflow. In this study, we extract sea surface wind field and its structure caused by rain cells by using a RADARSAT-2 SAR image with a spatial resolution of 100 m for case study. We extract the sea surface wind speeds from SAR image by using CMOD4 geophysical model function with outside wind directions of NCEP final operational global analysis data, Advance Scatterometer (ASCAT) onboard European MetOp-A satellite and microwave scatterometer onboard Chinese HY-2 satellite, respectively. The root-mean-square errors (RMSE) of these SAR wind speeds, validated against NCEP, ASCAT and HY-2, are 1.48 m/s, 1.64 m/s and 2.14 m/s, respectively. Circular signature patterns with brighter on one side and darker on the opposite side on SAR image are interpreted as the sea surface wind speed (or sea surface roughness) variety caused by downdraft associated with rain cells. The wind speeds taken from the transect profile which superposes to the wind ambient vectors and goes through the center of the circular footprint of rain cell can be fitted as a cosine or sine curve in high linear correlation with the values of no less than 0.80. The background wind speed, the wind speed caused by rain cell and the diameter of footprint of the rain cell with kilometers or tens of kilometers can be acquired by fitting curve. Eight cases interpreted and analyzed in this study all show the same conclusion.

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.

Applicability of Synthetic Aperture Radar Wind Retrievals on Offshore Wind Resources Assessment in Hangzhou Bay, China

In view of the high cost and sparse spatial resolution of offshore meteorological observations, ocean winds retrieved from satellites are valuable in offshore wind resource assessment as a supplement to in situ measurements. This study examines satellite synthetic aperture radar (SAR) images from ENVISAT advanced SAR (ASAR) for mapping wind resources with high spatial resolution. Around 181 collected pairs of wind data from SAR wind maps and from 13 meteorological stations in Hangzhou Bay are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a standard deviation (SD) of 1.99 m/s and correlation coefficient of R = 0.67. The model wind directions, which are used as input for the SAR wind speed retrieval, show a high correlation coefficient (R = 0.89) but a large standard deviation (SD = 42.3°) compared to in situ observations. The Weibull probability density functions are compared at one meteorological station. The SAR-based results appear not to estimate the mean wind speed, Weibull scale and shape parameters and wind power density from the full in situ data set so well due to the lower number of satellite samples. Distributions calculated from the concurrent 81 SAR and in situ samples agree well.

Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula

Satellite Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillage on the ocean’s surface. Several surveillance applications have been developed based on this technology. Environmental variables such as wind speed should be taken into account for better SAR image segmentation. This paper presents an adaptive thresholding algorithm for detecting oil spills based on SAR data and a wind field estimation as well as its implementation as a part of a functional prototype. The algorithm was adapted to an important shipping route off the Galician coast (northwest Iberian Peninsula) and was developed on the basis of confirmed oil spills. Image testing revealed 99.93% pixel labelling accuracy. By taking advantage of multi-core processor architecture, the prototype was optimized to get a nearly 30% improvement in processing time.

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.

Atmospheric frontal gravity waves observed in satellite SAR images of the Bohai Sea and Huanghai Sea

In the satellite synthetic aperture radar (SAR) images of the Bohai Sea and Huanghai Sea, the authors observe sea surface imprints of wave-like patterns with an average wavelength of 3.8 km. Comparing SAR observations with sea surface wind fields and surface weather maps, the authors find that the occurrence of the wave-like phenomena is associated with the passing of atmospheric front. The authors define the waves as atmospheric frontal gravity waves. The dynamical parameters of the wave packets are derived from statistics of 9 satellite SAR images obtained from 2002 to 2008. A two-dimensional linear physical wave model is used to analyze the generation mechanism of the waves. The atmospheric frontal wave induced wind variation across the frontal wave packet is compared with wind retrievals from the SAR images. The CMOD-5 (C-band scatterometer ocean geophysical model function) is used for SAR wind retrievals VV (transmitted vertical and received vertical) for ENVISAT and HH (transmitted horizontally and received horizontally) for RADARSAT-1. A reasonable agreement between the analytical solution and the SAR observation is reached. This new SAR frontal wave observation adds to the school of SAR observations of sea surface imprints of AGWs including island lee waves, coastal lee waves, and upstream Atmospheric Gravity Waves (AGW).

On offshore wind energy mapping using satellite SAR

Offshore wind is an attractive energy source due to the potential of very high wind power as a result of relatively low sea surface roughness. Currently wind observations at meteorological masts positioned in the sea or along the coastline are collected for a minimum of one year and then used in atmospheric models at local scales, for example in the Wind Atlas Analysis and Application Program (WASP) and at regional scales, for example in the Karlsruhe Atmospheric Mesoscale Model (KAMM). Satellite synthetic aperture radar (SAR) imagery provides spatial patterns of wind speed, yet only as snapshots of various atmospheric situations. Regardless, SAR wind speed data may offer new possibilities for mapping offshore wind resources. SAR-derived sea wind maps may be estimated from empirical scatterometer algorithms that are valid for open sea conditions. Scatterometer wind retrieval algorithms are based upon statistical analysis of open-sea wind observations; medium-range forecast winds from weather models; and scatterometer data. Conventional scatterometers measure the normalized radar cross-section at several different aspect angles to infer wind speed and direction. Backscatter coefficients are physically related to wind speed through the roughness of the open sea. Sea roughness is generated by wind-wave interactions in the capillary and short gravity wave spectrum and additional parameters in fetch-limited seas may influence sea roughness compared to the open sea. Hence, SAR-derived wind maps may be biased in fetch-limited seas. Because SAR data provides only one backscatter value in each resolution cell, wind direction must be known a priori. A simple comparison of meteorological observations at a Danish offshore site and from an European Remote Sensing (ERS) SAR wind speed map retrieved using the CMOD4 algorithm shows promising results for wind retrieval from SAR images in coastal regions.