Estimation of Offshore Wind Resources in Coastal Waters off Shirahama Using ENVISAT ASAR Images

Offshore wind resource maps for the coastal waters off Shirahama, Japan were made based on 104 images of the Advanced Synthetic Aperture Radar (ASAR) onboard the ENVISAT satellite. Wind speed fields were derived from the SAR images with the geophysical model function CMOD5.N. Mean wind speed and energy density were estimated using the Weibull distribution function. These accuracies were examined in comparison with in situ measurements from the Shirahama offshore platform and the Southwest Wakayama buoy (SW-buoy). Firstly, it was found that the SAR-derived 10 m-height wind speed had a bias of 0.52 m/s and a RMSE of 2.33 m/s at Shirahama. Secondly, it was found that the mean wind speeds estimated from SAR images and the Weibull distribution function were overestimated at both sites. The ratio between SAR-derived and in situ measured mean wind speeds at Shirahama is 1.07, and this value was used for a long-term bias correction in the SAR-derived wind speed. Finally, mean wind speed and wind energy density maps at 80 m height were made based on the corrected SAR-derived 10 m-height wind speeds and the ratio U80/U10 calculated from the mesoscale meteorological model WRF.


Introduction
From the satellite-borne Synthetic Aperture Radar (SAR) it is possible to retrieve a sea surface wind speed field with a high spatial resolution of tens to hundreds of meters, and it is thus expected that the SAR image can be used for wind resource assessment in coastal waters.In fact, the offshore wind resource assessment using SAR has been conducted in many places, especially in Europe (e.g., [1][2][3]).
On the other hand, in Japan, since there has been little need for offshore wind resource assessment at least up to the accident of the Fukushima nuclear power plant, there are few papers in which offshore wind resource is practically assessed with SAR, except some preliminary papers like Kozai et al. [4].But now, offshore wind energy is gradually regarded as a promising electric power resource, and there is increased need for assessing the offshore wind resource.It is thus desirable that the SAR-based offshore wind resource assessment, which is reported to work well in European seas, could also be applicable to Japanese coastal waters.However, compared to the European seas such as the North Sea, Japanese coastal waters have more complex coastlines and onshore terrains as well as they are affected by non-neutral atmospheric stability due to the Kuroshio Current.In fact, the authors have found that the performance and accuracy of the SAR-based wind speed estimation method are different between Europe and Japan, and thus have investigated how to use SAR for offshore wind resource assessment in Japanese coastal waters [5][6][7].
First, Takeyama et al. [5] discussed the wind directions used as input to a geophysical model function (GMF) to derive 10 m-height wind speed from a SAR image.As a result, it was found that estimated wind speed became the most accurate when using a high resolution wind direction field output from numerical simulation with the mesoscale meteorological model WRF (Weather Research and Forecasting model) [8].Thus, this study uses the WRF wind direction as input to GMF.Secondly, Takeyama et al. [6] compared the performances of four GMFs: CMOD4, CMOD5, CMOD_IFR2 and CMOD5.N [9] at two sites in Japanese coastal waters and concluded that CMOD5.N, which can correct the effect of atmospheric stability, retrieves the most accurate wind speeds of the four.Thus, the latest GMF CMOD5.N is used to derive wind speed from SAR images.Thirdly, it is generally believed that a larger number of SAR images leads to a higher accuracy of the assessment.Kozai et al. [7] examined the number of SAR images necessary to estimate long-term mean wind speed at Shirahama, and concluded that at least 74 to 128 SAR images are required when assuming a 10% error and 90% confidence interval.The number is a little bit larger than that of Barthelmie and Pryor [10], to which Kozai et al. [7] referred, reporting that 60 to 70 randomly selected images are required to characterize the mean wind speed and Weibull distribution scale parameter, and nearly 2,000 images are needed to obtain energy density.According to these results, the number of 104 SAR images, used in this study, can be considered to be almost sufficient for mean wind speed estimation, but it might be insufficient for wind energy density estimation.
This study aims at two things.One is to examine the accuracy of offshore wind resource estimation (long-term mean wind speed and wind energy density) using SAR images and the Weibull analysis, and the other is to finally make wind resource maps in the coastal waters off Shirahama.The methods of wind speed estimation from SAR images, comparison with in situ measurements, and application of the Weibull distribution function are described in Section 2. Accuracies of SAR-derived wind speeds and Weibull parameters are examined in Subsections 3.1 and 3.2, respectively.Subsection 3.3 describes the way to make the offshore wind resource maps, which are finally presented at the end of this paper.

Target Area and in situ Measurements
The target area of this study is the coastal waters off Shirahama, shown in Figure 1.This area is located in the western part of Japan, including the Kii Channel facing the Pacific Ocean, and known as a relatively windy coastal area in this region, because this channel gives passage to the northwesterly winter monsoon wind.In this area there are two observation sites; the Shirahama offshore platform and the South Wakayama buoy (Hereinafter, SW-buoy).The first one, the Shirahama offshore platform (33°42'32''N, 135°19'58''E) is the oceanographic and meteorological observation station operated by the Disaster Prevention Research Institute, Kyoto University since 1994.On the platform, wind speed and direction are measured at a height of 23 m above mean sea level with a propeller anemometer.This study uses the hourly 10-min averaged wind speed from 2003 to 2011.The second one, the SW-buoy (33°38'32''N, 135°09'24''E) is a buoy for wave observation and is operated by the Ports and Harbors Bureau, Ministry of Land, Infrastructure, Transport and Tourism.On the buoy, wind speed and direction are measured with a propeller anemometer at a height of 7 m.The hourly 10-min averaged wind speed data for two years from 2009 to 2010 is used in this study.
In order to compare the SAR-derived wind speed at 10 m height with in situ measured wind speeds, the in situ wind speeds at 23 m height at Shirahama is corrected to the 10 m-height wind speed.For this height correction, the LKB code [11], which can calculate vertical profile of wind speed based on the Monin-Obukhov similarity theory, is used.Three kinds of inputs; air temperature, relative humidity, and sea surface temperature (SST) are required in the LKB code.The wind profile, which can take the effect of atmospheric stability expressed as Ψ u (ζ) into account, is shown as Here, u * is frictional velocity, z 0 is roughness length, and κ is the von Karman constant (=0.4).The relation between z 0 and u * is given as 0.11 (2) where α is Charnock's parameter with a value of 0.011 [12], υ is the kinematic viscosity, and g is the acceleration due to gravity.The parameters, z 0 and u * can be determined iteratively through the Equations ( 1) and ( 2   For deriving wind speed from the SAR image, CMOD5.N [9] is used to derive wind speed from normalized radar cross section (NRCS) represented in the SAR images.The primary equation of CMOD5.N can be written as where is the VV-polarized NRCS obtained from a SAR image, φ is the relative wind direction defined as the angle between the radar look direction and true wind direction, and b 0 , b 1 , and b 2 are the parameters depending on the radar incidence angle and wind speed.Here, it is necessary to acquire values of wind direction from another external data source.Same as [5], this study uses the wind direction obtained from numerical simulation with the mesoscale meteorological model WRF [8].Details of the WRF simulation are described in Subsection 2.3.

Conversion from Equivalent Wind Speed (ENW) to Stability-Dependent Wind Speed (SDW)
The output from CMOD5.N is the equivalent neutral wind speed (ENW) [13], which is the wind speed obtained under the assumption of neutral atmospheric stability in the surface layer.Thus, the LKB code [11] is used to convert the ENW to the stability-dependent wind speed (SDW), which is comparable to a true wind speed.Since Takeyama et al. [6] provides an in-depth description of how to calculate SDW from ENW with the LKB code, this paper omits to describe it.What is important is that the LKB code requires three parameters; air temperature, relative humidity, and sea surface temperature (SST) to calculate SDW, and this study obtains these three values from numerical simulation with the mesoscale meteorological model WRF.The WRF (Weather Research and Forecasting model) [8] is the mesoscale numerical weather prediction system developed by seven institutes in the United States including the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Researches (NCAR).In this study, WRF is set up with two domains consisting of 100 × 100 grids with horizontal resolutions of 5 km and 1 km, and 28 vertical layers.As the initial and boundary conditions, 3-hourly (6-hourly before February 2006) 5 km × 5 km (10 km × 10 km before April 2009) mesoscale analysis MANAL provided from Japan Meteorological Agency and daily 0.05° × 0.05° sea surface temperature OSTIA SST provided from Met Office [14] are used in the simulation.WRF is run for 24 h for each SAR image, corresponding to the time of passage of ENVISAT (mostly at 01 and 13 UTC) with two-way nesting, which allows the interaction between the mother and child domains.More in-depth model in the WRF on from the he WRF sim s normally bability den      Further work is necessary to increase the accuracy of the maps by combining them with information from remote sensing measurements by satellite-borne scatterometers and radiometers and simulation results from a mesoscale model, as well as by increasing the number of SAR images used in the analysis.
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Table 2 .
Configurations of the mesoscale meteorological model WRF and input data.
the mesoscale meteorological model WRF, mean wind speed and wind energy density maps at 80 m height were made and presented at the end of the paper.