Badger, Merete

Hasager, Charlotte Bay

Kozai, Katsutoshi

Ohsawa, Teruo

Takeyama, Yuko

This paper presents an evaluation of effects of wind directions (NCEP, MANAL, QuickSCAT and WRF) on the sea surface wind speed retrieval from 75 ENVISAT ASAR images with four C-band Geophysical model functions, CMOD4, CMOD_IFR2, CMOD5 and CMOD5N at two target areas, Hiratsuka and Shirahama. As results, the WRF wind direction retrieves sea surface wind speeds with higher accuracies of the other wind directions at Hiratsuka. On the other hand, at Shirahama, it is not found an advantage of the WRF wind direction. Thus, the WRF wind direction cannot always retrieve wind speed with a high accuracy. However, a wind speed error generated with the WRF wind direction is the smallest of those with the other wind directions, and the error is as small as the level of SAR original radiometric errors. Consequently, the WRF wind direction is the most suitable of the four wind directions on the SAR wind speed retrieval when other error factors of the SAR wind speed retrieval are eliminated

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Downloaded from orbit.dtu.dk on: Dec 17, 2017Effect of Wind Direction on ENVISAT ASAR Wind Speed RetrievalTakeyama, Yuko; Ohsawa, Teruo; Kozai, Katsutoshi; Hasager, Charlotte Bay; Badger, MeretePublished in:Proceedings (CD-ROM)Publication date:2010Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Takeyama, Y., Ohsawa, T., Kozai, K., Hasager, C. B., & Badger, M. (2010). Effect of Wind Direction onENVISAT ASAR Wind Speed Retrieval. In Proceedings (CD-ROM) (pp. 008). Techno-Ocean Network.1    Effect of Wind Direction on ENVISAT ASAR Wind Speed Retrieval    Yuko Takeyama  National Institute of Advanced Industrial Science and Technology, JAPAN  Teruo Ohsawa, Katsutoshi Kozai Graduate School of Maritime Sciences, Kobe University, JAPAN  Charlotte Hasager, Merete Badger Risoe National Laboratory for Sustainable Energy, Technical University of Denmark   Abstract - This paper presents an evaluation of effects of wind directions (NCEP, MANAL, QuickSCAT and WRF) on the sea surface wind speed retrieval from 75 ENVISAT ASAR images with four C-band Geophysical model functions, CMOD4, CMOD_IFR2, CMOD5 and CMOD5N at two target areas, Hiratsuka and Shirahama. As results, the WRF wind direction retrieves sea surface wind speeds with higher accuracies of the other wind directions at Hiratsuka. On the other hand, at Shirahama, it is not found an advantage of the WRF wind direction. Thus, the WRF wind direction cannot always retrieve wind speed with a high accuracy. However, a wind speed error generated with the WRF wind direction is the smallest of those with the other wind directions, and the error is as small as the level of SAR original radiometric errors. Consequently, the WRF wind direction is the most suitable of the four wind directions on the SAR wind speed retrieval when other error factors of the SAR wind speed retrieval are eliminated.   I. INTRODUCTION  Offshore wind resources can be assessed from wind speeds retrieved by satellite-borne Synthetic Aperture Radar (SAR) images and the Weibull fitting method. The SAR wind speed retrieval with a high accuracy is required for correct estimation of the amount. In a previous study, it was shown that the SAR wind speed has a Root Mean Square Error (RMSE) of around 1.5 m/s [1]. In general, when the SAR wind speed is retrieved, wind direction is necessary as an input value for the geophysical model function (GMF). In order to obtain the wind direction, two methods have been proposed in previous studies. One is the method to use wind streak detection from a SAR image itself [2], and the other is the utilization of the objective analysis data. In the case of the former, the wind direction retrieved from the wind streak is temporally and spatially synchronized with the SAR wind speed. However, the wind streak cannot always be detected from all SAR images. On the other hand, the latter has the advantage of obtaining the wind direction field at constant temporal and spatial intervals. A problem is the accuracy and its effect on the accuracy of the SAR-retrieved wind speed has not been well discussed. In this paper, SAR wind speeds are retrieved with four kinds of wind directions including objective analysis data, satellite data and numerical meteorological simulation data, and these accuracies are examined to investigate the effects of wind directions on the SAR wind speed retrieval.  II. METHODOLOGY  A. ENVISAT ASAR images  ASAR images (image mode precision (IMP)) have been collected over the period 2003-2008 in this study. Two target areas are Hiratsuka (35° 18ʹ 20ʺ N, 139° 20ʹ 45ʺ E) and Shirahama (35° 42ʹ 32ʺ N, 135° 19ʹ 58ʺ E) in Japan (Fig. 1), and sea surface wind speeds are retrieved from total of 75 ASAR images covering either Hiratsuka (33 images) or Shirahama (42 images). The observation time of ASAR is around 01 (descending) or 21 (ascending) UTC in Japan. The ASAR IMP mode observes approximately 100 km × 100 km earth surface with 30 m × 30 m special resolutions. All grids of ASAR images are AB Fig.1 Geographical location of Hiratsuka (A) and  Shirahama (B).  2 resampled to 200 m × 200 m grid size after wind speeds are retrieved with GMFs.  B. In situ measurement There are marine observation platforms at both Hiratsuka and Shirahama, respectively. Sea surface wind speed and direction have been continuously measured at 23 m above the mean surface level (MSL) at both platforms. Moreover, at the platforms, air and sea temperature are also measured. Respective measured elements are described in Table I.  C. SAR wind speed retrieval   Sea surface wind is retrieved from the SAR image with GMF, which empirically relates the normalized radar cross section (NRCS) with sea surface wind speed, relative wind direction and radar incidence angle. That is, the GMF assumes that the NRCS at a specific grid is only influenced by local changes in both wind speed and direction. Based on this assumption, four C-band GMFs, CMOD4 [3], CMOD_IFR2 [4], CMOD5 [5] and CMOD5N [6], were developed. In this paper, all of the four C-band GMFs are used for wind speed retrieval, and retrieved wind speeds are compared each other.  Since the all GMFs have been developed for VV-polarized NRCS, HH-polarized NRCS must be corrected by an empirical equation before the GMF processing. In order to convert from the HH-polarized NRCS to the VV-polarized NRCS, Mouche et al. (2005)’s equation [7] is used in this study.  In situ measurement (ISM) and four kinds of wind directions; the National Cholesterol Education Program (NCEP) Re-analysis data, the Japan meteorological agency (JMA) mesoscale analysis data (MANAL), the QuickSCAT winds, the output of Weather Research and Forecasting model (WRF) are used for the sea surface wind retrieval from SAR images. Temporal and horizontal resolutions of all of the five wind directions are described in Table II.  D. Height correction   Using GMFs, wind speed is obtained at 10 m above sea surface, while in situ wind speed is measured at 23 m at both platforms. Therefore, height correction of the in situ wind speed is necessary to enable direct comparison with the retrieved wind speed from SAR. In order to estimate the wind speed at 10 m from that at 23 m, this study uses the LKB code [8], which can calculate a vertical wind profile based on the Monin-Obukhov similarity theory.  III. RESULTS AND DISCUSSION  A. Accuracies of four wind directions  As a first step, accuracies of wind directions themselves are discussed in this section. In Table III, Mean Absolute Errors (MAEs) of four wind directions are shown. At both Hiratsuka and Shirahama, MAEs of the WRF wind direction are the smallest of the other wind directions. Moreover, the NCEP, MANAL and QuickSCAT wind directions have large MAEs of 67 degrees or more. Since the WRF wind direction has higher temporal and horizontal resolutions, those MAEs are considered to become lower than the other wind directions.  B. Effect of four wind directions  Figs. 2 and 3 show RMSEs and biases of SAR-retrieved wind speeds using four GMFs with five kinds of wind directions at Hiratsuka, respectively. Notice the average (orange line) of GMFs, the NCEP and WRF wind directions have smaller RMSEs of wind speeds than the MANAL and QuickSCAT wind directions. The difference of RMSE between ISM and NCEP is 0.07 m/s and that between ISM and WRF is 0.02 m/s. Moreover, the absolute bias in the case of using the WRF wind direction is lower than those using the other three wind directions. These results imply that the WRF is the most suitable wind direction in the four wind directions at Hiratsuka. In the case of Shirahama (Figs. 4 and 5), all wind directions have RMSEs of 2.0 to 2.3 m/s, and there are no large differences of those RMSEs. Moreover, the biases using the MANAL, QuickSCAT and WRF wind directions have large differences from biases of the case of Hiratsuka. These results suggest that the WRF wind direction cannot always retrieve the SAR wind speed with a high accuracy. As the next step, Table IV shows RMSEs of SAR-retrieved wind speeds using CMOD5 with four wind directions; NCEP, MANAL, QuickSCAT and WRF under the assumption that the SAR-retrieved wind speed with ISM is an actual wind speeds. In other words, these RMSEs are errors generated only by differences of wind directions. This table shows that errors of wind directions contribute 26 % of relative errors of SAR wind speed at the TABLE I Measured elements at marine platforms. Site Elements Observation height(reference MSL) RemarkAir temperture above 20 mSea temperture below 3 mWind speed anddirection above 23 m Propeller anemometerAir temperture avobe 15 mSea temperture below 5 mWind speed anddirection above 23 m Propeller anemometerHiratsukaShirahamaResistance temperturesensorResistance temperturesensor  TABLE II Temporal and horizontal resolutuins of wind directions. Wind direction Temporal resolution Horizontal resolutionISM 1 hour on siteNCEP 6 hours(0, 6, 12, 18 UTC) 2.5° × 2.5°MANAL6 hours (0, 6, 12, 21) or3 hours(0, 3, 6, 9, 12, 15, 18, 21)10 km × 10 kmor5 km × 5 kmQuickSCAT 1 day 12.5 km × 12.5 kmWRF 1 hour 500 m × 500 m TABLE III MAEs of four wind directions at Hiratsuka and Shirahama. ISM NCEP MANAL QuickSCAT WRFHiratsuka 76.5 71.9 77.3 64.0Shirahama 73.1 67.3 79.3 56.6(deg.)   3  Fig. 4 RMSEs (m/s) of SAR wind speed retrieved by four GMFs with five wind directions at Shirahama. Five written cvalues are average of all GMFs.   Fig. 5 Same as in Fig. 4 but for biases (m/s). maximum. Moreover, relative errors with the WRF wind direction are smaller than the other three wind directions. Thus, the WRF wind direction, which is the closest to the actual wind direction, generates the smallest errors of the all wind directions. Consequently, the WRF wind direction can retrieve the SAR wind speed with a higher accuracy of the other wind directions if the SAR wind speed could be retrieved correctly using ISM and GMFs.   C. Evaluation of Wind Speed Errors caused by WRF wind direction In this section, NRCSs estimated from in situ wind speeds, radar incidence angles and four wind directions with three GMFs; CMOD4, CMOD5 and CMOD_IFR are compared with NRCSs estimated from ISM. Table V shows RMSEs of NRCS at Hiratsuka. In Table V, the case using the WRF wind direction has the smallest RMSE than other cases, and these RMSEs are smaller than 0.75 dB. Similarly, in the case of Shirahama (Table VI), the WRF wind direction exhibits the smallest RMSE. In a previous study, ASAR radiometric stability was reported to be 0.2 - 0.7 dB [9]. Consequently, the error generated by the WRF wind direction is considered to be the same level as the ASAR original error. Those results also indicate that the WRF wind direction leads the SAR-retrieved wind speed with a higher accuracy when the SAR wind speed can be retrieved with a high accuracy using ISM and GMFs.   IV. CONCLUSION   In the present work, to disclose the effect of wind direction on ENVISAT ASAR wind speed retrieval, five kinds of wind directions have been attempted in the wind speed retrieval with four GMFs.   Results are as follows:  1) The AME of the WRF wind direction is smaller than the other three wind directions (NCEP, MANAL and QuickSCAT). 2) In the case of Hiratsuka, the WRF is the most suitable wind direction of the other wind directions. However, the WRF wind direction cannot always estimate the SAR-retrieved wind speed with a high accuracy. 3) Under the assumption that the SAR-retrieved wind speeds with ISM are actual wind speeds, the WRF wind direction has the smallest relative error of the other wind directions. 4) Errors of NRCS generated by the WRF wind direction  Fig. 2  RMSEs (m/s) of SAR wind speed retrieved by GMFs (CMOD4, CMOD_IFR2, CMOD5 and CMOD5N) with five wind directions (ISM, NCEP, MANAL, QuickSCAT and WRF) at Hiratsuka.   Fig. 3 Same as in Fig. 2 but for biases (m/s).  4 are the same level as original errors of ASAR images. 5) Those results indicate that the WRF wind direction is the most suitable for the sea surface wind retrieval with C-band GMFs when other error factors on the SAR wind speed retrieval are eliminated.   ACKNOWLEDGEMENTS  ENVISAT ASAR images were acquired from the European Space Agency under the co-operative research project “Offshore wind resource assessments using SAR and MM5 over Japanese coastal waters”, C1P4068. The measured data at Hiratsuka were provided by the Institute of Industrial Science at the University of Tokyo (IIS). The data observed at Shirahama were provided by Dr. Yasunori Muto at Kyoto University and Mr. Atsushige Serizawa at the Disaster Prevention Research Institute (DPRI).   REFERENCES  [1] P. W. Vachon and F. W. Dobson, “Validation of wind vector retrieval from ERS-1 SAR images over the ocean”, The Global Atmosphere and Ocean System, vol. 5, pp.177-187, 1996. [2] S. Leher, J. Horstmann, W. Koch and W. Rosenthal, “ Mesoscale wind measurements using recalibrated ERS SAR images”, Geophysical Research, vol. 103, No. C4, pp.7847-7856, 1998. [3]A. Stoffelen and D. Anderson, “Scatterometer data interpretation: Estimation and validation of the transfer function CMOD-4”, Journal of Geophysical Research, vol. 102, No. C3, pp.5767-5780, 1997. [4] Y. Quilfen, B. Chapron, T. Elfouhaily, K. Katsaros and J. Tournadre, “Observation of tropical cyclones by high-resolution scatterometry”, Journal of Geophysical Research, vol. 103, No. C4, 7767-7786, 1998. [5] H. Hersbach, “CMOD5 An Improved Geophysical Model Function for ERS C-Band Scatterometry”, Report of the European Centre Medium-Range Weather Forecasts (ECMWF), 2003. [6] H. Hersbach, “CMOD5.N A C-band Geophysical Model Function for Equivalent Neutral Wind”, Report of the European Centre Medium-Range Weather Forecasts (ECMWF), 2008. [7] A. A. Mouche, D. Hauser, J. Daloze and C. Gueri,  Dual-Polarization measurements at C-Band over the ocean: result from airborne radar observations and comparison with ENVISAT ASAR data”, IEEE Transaction on Geoscience and Remote Sensing, vol. 43, No. 4, pp.753-769, 2005. [8] W. T. Liu and W. Tang, “Equivalent Neutral Wind”, JPL publication 96-17, 8pp, 1996. [9] C. Goetz, H. Jackson, B. Rosich, M. Transfaglia, P. Meadows, M. Canela, R. Lorza-pitt and P. Viau, “Global Redeployment of the ENVISAT ASAR Transponders for around orbit calibration”, ENVISAT symposium 2007. TABLE IV RMSEs of SAR retrieved wind speed using CMOD5 with NCEP, MANAL, QuickSCAT and WRF wind directions when SAR wind speed with ISM is set as autual wind speeds. ISM NCEP MANAL QuickSCAT WRF0.89 1.28 1.39 0.64(17%) (24%) (26%) (12%)1.59 1.83 1.39 1.33(23%) (26%) (20%) (19%)(m/s)HiratsukaShirahama  TABLE V RMSEs of NRCS at Hiratsuka. ISM NCEP MANAL QuickSCAT WRFCMOD4 1.18 1.22 1.19 0.68CMOD_IFR 1.17 1.43 1.15 0.75CMOD5 0.99 1.00 1.05 0.55(dB)  TABLE VI RMSEs of NRCS at Shirahama. ISM NCEP MANAL QuickSCAT WRFCMOD4 1.03 1.00 0.91 0.79CMOD_IFR 0.98 0.96 0.86 0.73CMOD5 0.85 0.80 0.75 0.66(dB)  

Effect of Wind Direction on ENVISAT ASAR Wind Speed Retrieval

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Effect of Wind Direction on ENVISAT ASAR Wind Speed Retrieval

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