Browse > Article
http://dx.doi.org/10.7780/kjrs.2018.34.2.1.5

Accuracy Evaluation of Daily-gridded ASCAT Satellite Data Around the Korean Peninsula  

Park, Jinku (Department of Oceanography, Pusan National University)
Kim, Dae-Won (Department of Oceanography, Pusan National University)
Jo, Young-Heon (Department of Oceanography, Pusan National University)
Kim, Deoksu (Department of Oceanography, Pusan National University)
Publication Information
Korean Journal of Remote Sensing / v.34, no.2_1, 2018 , pp. 213-225 More about this Journal
Abstract
In order to access the accuracy of the gridded daily Advanced Scatterometer (hereafter DASCAT) ocean surface wind data in the surrounding of Korea, the DASCAT was compared with the wind data from buoys. In addition, the reanalysis data for wind at 10 m provided by European Centre for Medium-Range Weather Forecasts (ECMWF, hereafter ECMWF), National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR, hereafter NCEP), Modern Era Retrospective-analysis for Research and Applications-2 (MERRA-2, hereafter MERRA) were compared and analyzed. As a result, the RMSE of DASCAT for the actual wind speed is about 3 m/s. The zonal components of wind of buoys and the DASCAT have strong correlation more than 0.8 and the meridional components of wind them have lower correlation than that of zonal wind and are the lowest in the Yellow Sea (r=0.7). When the actual wind speed is below 10 m/s, the EMCWF has the highest accuracy, followed by DASCAT, MERRA, and NCEP. However, under the wind speed more than 10 m/s, DASCAT shows the highest accuracy. In the nature of error according to the wind direction, when the zonal wind is strong, all dataset has the error of more than $70^{\circ}$ on the average. On the other hand, the RMSE of wind direction was recorded $50^{\circ}$ under the strong meridional winds. ECMWF shows the highest accuracy in these results. The RMSE of the wind speed according to the wind direction varied depending on the actual wind direction. Especially, MERRA has the highest RMSE under the westerly and southerly wind condition, while the NCEP has the highest RMSE under the easterly and northerly wind condition.
Keywords
Surface wind; Accuracy; DASCAT; Reanalysis Data;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Bentamy, A., K. B. Katsaros, W. M. Drennan, and E. B. Forde, 2002. Daily surface wind fields produced by merged satellite data, Gas Transfer at Water Surfaces, 127: 343-349.
2 Mears, C. A., D. K. Smith, and F. J. Wentz, 2001. Comparison of special sensor microwave imager and buoy-measured wind speeds from 1987 to 1997, Journal of Geophysical Research: Oceans, 106(C6): 11719-11729.   DOI
3 Naeimi, V., Z. Bartalis, and W. Wagner, 2009. ASCAT Soil Moisture: An Assessment of the Data Quality and Consistency with the ERS Scatterometer Heritage, Journal of Hydrometeorology, 10(2): 555-563.   DOI
4 Oh, H. M. and K. J. Ha, 2005. Analysis of Marine Meteorological Characteristics at Ieodo Ocean Research Station from 2003 to 2004, Asia-Pacific Journal of Atmospheric Sciences, 41(5): 671-680 (in Korean with English Abstract).
5 Bentamy, A., D. Croize-Fillon, P. Queffeulou, C. Liu, and H. Roquet, 2009. Evaluation of high-resolution surface wind products at global and regional scales, Journal of Operational Oceanography, 2(2): 15-27.   DOI
6 Bentamy, A. and D. Croize-Fillon, 2012. Gridded surface wind fields from Metop/ASCAT measurements, International Journal of Remote Sensing, 33(6): 1729-1754.   DOI
7 Bentamy, A. and D. C. Fillon, 2015. Daily ASCAT surface wind fields, IFREMER Technology Report, Laboratoire d'Oceanographie Spatiale, France.
8 Stull, R. B., 1988. An introduction to boundary layer meteorology, Kluwer academic publishers, Dordrecht, The Netherlands.
9 Portabella, M., A. Stoffelen, W. Lin, A. Turiel, A. Verhoef, J. Verspeek, and J. Ballabrera-Poy, 2012. Rain effects on ASCAT-retrieved winds: Toward an improved quality control, IEEE Transactions on Geoscience and Remote Sensing, 50(7): 2495-2506.   DOI
10 Sivareddy, S., M. Ravichandran, and M. S. Girishkumar, 2013. Evaluation of ASCAT-Based daily gridded winds in the tropical Indian ocean, Journal of Atmospheric and Oceanic Technology, 30(7): 1371-1381.   DOI
11 Thomas, B. R., E. C. Kent, and V. R. Swail, 2005. Methods to homogenize wind speeds from ships and buoys, International Journal of Climatology, 25(7): 979-995.   DOI
12 Wagner, W., S. Hahn, R. Kidd, T. Melzer, Z. Bartalis, S. Hasenauer, J. Figa-Saldana, P. De Rosnay, A. Jann, S. Schneider, and J. Komma, 2013. The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications, Meteorologische Zeitschrift, 22(1): 5-33.   DOI
13 Gemmrich, J. and C. Garrett, 2012. The signature of inertial and tidal currents in offshore wave records, Journal of Physical Oceanography, 42(6): 1051-1056.   DOI
14 Choi, W.M., 2013. Accuracy Assessment and Error Characteristics of Sea Surface Wind off the Coast of Korea using Scatterometer Data (Master's thesis), http://hdl.handle.net/10371/128134, Accessed on Jul. 19, 2017.
15 Curry, J. A., A. Bentamy, M. A. Bourassa, D. Bourras, E. F. Bradley, M. Brunke, S. Castro, S. H. Chou, C. A. Clayson, W. J. Emery, L. Eymard, C. W. Cairall, M. Kubota, B. Lin, W. Perrie, R. A. Reeder, I. A. Renfrew, W. B. Rossow, J. Schulz, S. R. Smith, P. J. Webster, G. A. Wick, and X. Zeng, 2004. SEAFLUX, Bulletin of the American Meteorological Society, 85(3): 409-424.   DOI
16 De Rooy, W. and K. Kok, 2004. A combined physical statistical approach for the downscaling of model wind speed, Weather Forecast, 19(3): 485-495.   DOI
17 Jeong, J. Y., J. S. Shim, D. K. Lee, I. K. Min, and J. I. Kwon, 2008. Validation of QuikSCAT Wind with Resolution of 12.5 km in the Vicinity of Korean Peninsula, Ocean and Polar Research, 30(1): 47-58 (in Korean with English Abstract).   DOI