Comparison of WindSat and buoy-measured ocean products from 2004 to 2013

ZHANG Lei; SHI Hanqing; DU Huadong; ZHU Enze; ZHANG Zhihua; FANG Xun

doi:10.1007/s13131-016-0798-9

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2016 > 35(1): 67-78

ZHANG Lei, SHI Hanqing, DU Huadong, ZHU Enze, ZHANG Zhihua, FANG Xun. Comparison of WindSat and buoy-measured ocean products from 2004 to 2013[J]. Acta Oceanologica Sinica, 2016, 35(1): 67-78. doi: 10.1007/s13131-016-0798-9

Citation:

ZHANG Lei, SHI Hanqing, DU Huadong, ZHU Enze, ZHANG Zhihua, FANG Xun. Comparison of WindSat and buoy-measured ocean products from 2004 to 2013[J]. Acta Oceanologica Sinica, 2016, 35(1): 67-78. doi: 10.1007/s13131-016-0798-9

Citation:

PDF( 6262 KB)

Comparison of WindSat and buoy-measured ocean products from 2004 to 2013

doi: 10.1007/s13131-016-0798-9

Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China

Received Date: 2014-11-20
Rev Recd Date: 2015-06-05

Abstract

Abstract

To evaluate the ocean surface wind vector and the sea surface temperature obtained from WindSat, we compare these quantities over the time period from January 2004 to December 2013 with moored buoy measurements. The mean bias between the WindSat wind speed and the buoy wind speed is low for the low frequency wind speed product (WSPD_LF), ranging from -0.07 to 0.08 m/s in different selected areas. The overall RMS error is 0.98 m/s for WSPD_LF, ranging from 0.82 to 1.16 m/s in different selected regions. The wind speed retrieval result in the tropical Ocean is better than that of the coastal and offshore waters of the United States. In addition, the wind speed retrieval accuracy of WSPD_LF is better than that of the medium frequency wind speed product. The crosstalk analysis indicates that the WindSat wind speed retrieval contains some cross influences from the other geophysical parameters, such as sea surface temperature, water vapor and cloud liquid water. The mean bias between the WindSat wind direction and the buoy wind direction ranges from -0.46° to 1.19° in different selected regions. The overall RMS error is 19.59° when the wind speed is greater than 6 m/s. Measurements of the tropical ocean region have a better accuracy than those of the US west and east coasts. Very good agreement is obtained between sea surface temperatures of WindSat and buoy measurements in the tropical Pacific Ocean; the overall RMS error is only 0.36°C, and the retrieval accuracy of the low latitudes is better than that of the middle and high latitudes.
- WindSat,
- polarimetric microwave radiometer,
- wind vector,
- sea surface temperature,
- validation

FullText(HTML)

References(22)

References

Atlas R, Reale O, Ardizzone J, et al. 2006. Geophysical validation of WINDSAT surface wind data and its impact on numerical weather prediction. In: Proceedings of SPIE-Atmospheric and Environmental Remote Sensing Data Processing and Utilization II: Perspective on Calibration/Validation Initiatives and Strategies. San Diego, California, USA: SPIE

Boukabara S A, Weng Fuzhong. 2008. Microwave emissivity over ocean in all-weather conditions: validation using WINDSAT and airborne GPS dropsondes. Geoscience and Remote Sensing, IEEE Transactions on, 46(2): 376-384

Candy B, English S J, Keogh S J. 2009. A Comparison of the impact of QuikScat and WindSat wind vector products on met office analyses and forecasts. Geoscience and Remote Sensing, IEEE Transactions on, 47(6): 1632-1640

Freilich M H, Vanhoff B A. 2006. The accuracy of preliminary Wind- Sat vector wind measurements: Comparisons with NDBC buoys and QuikSCAT. Geoscience and Remote Sensing, IEEE Transactions on, 44(3): 622-637

Huang Xiaoqi, Zhu Jianhua, Lin Mingsen, et al. 2014. A preliminary assessment of the sea surface wind speed production of HY-2 scanning microwave radiometer. Acta Oceanologica Sinica, 33(1): 114-119

Kim D J, Lyzenga D R. 2008. Efficient model-based estimation of atmospheric transmittance and ocean wind vectors from Wind- Sat data. Geoscience and Remote Sensing, IEEE Transactions on, 46(8): 2288-2297

Lee T, Goerss J, Hawkins J, et al. 2006. Weather Forecasting Applications using WindSat. In: 14th Conference on Satellite Meteorology and Oceanography, Atlanta, Georgia

Mears C A, Smith D K, Wentz F J. 2001. Comparison of special sensor microwave imager and buoy-measured wind speeds from 1987 to 1997. Journal of Geophysical Research, 106(C6): 11719-11729

Meissner, T, Ricciardulli, Wentz F J. 2011. All-weather wind vector measurements from intercalibrated active and passive microwave satellite sensors. In: 2011 IEEE International Geoscience and Remote Sensing Symposium IGARSS., Vancouver, BC, Canada: IEEE, 1509-1511

Meissner T, Wentz F J. 2005. Ocean retrievals for WindSat: Radiative transfer model, algorithm, validation. In: OCEANS, Proceedings of MTS/IEEE. Washington, DC: IEEE, 130-133

Monaldo F M. 2006. Evaluation of WindSat wind vector performance with respect to QuikSCAT estimates. Geoscience and Remote Sensing, IEEE Transactions on, 44(3): 638-644

Narvekar P S, Heygster G, Tonboe R, et al. 2008. Analysis of WindSat data over Arctic Sea ice. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2008. Boston, MA: IEEE, 5: V-369-V-372

Peixoto J P, Oort A H. 1992. Physics of Climate. New York: American Institute of Physics

Quilfen Y, Prigent C, Chapron B, et al. 2007. The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions. Journal of Geophysical Research, 112(C9), doi: 10.1029/2007JC004163

Turk F J, DiMichele S, Hawkins J. 2006. Observations of tropical cyclone structure from WindSat. Geoscience and Remote Sensing, IEEE Transactions on, 44(3): 645-655

Wang He, Zhu Jianhua, Lin Mingsen, et al. 2013. First six months quality assessment of HY-2A SCAT wind products using in situ measurements. Acta Oceanologica Sinica, 32(11): 27-33

Wentz F J, Gentemann C L, Hilburn K. 2005a. Three years of ocean products from AMSR-E: evaluation and applications. In: 2005 IEEE International Geoscience and Remote Sensing IEEE International Symposium-IGARSS. Seoul: IEEE, 7: 4929-4932

Wentz F J, Meissner T, Smith D K. 2005b. Assessment of the initial release of WindSat wind retrievals. RSS Technical Report 010605

Yu Lisan, Jin Xiangze. 2012. Buoy perspective of a high-resolution global ocean vector wind analysis constructed from passive radiometers and active scatterometers (1987-present). Journal of Geophysical Research, 117(C11), doi: 10.1029/2012JC008069

Zhao Yili, Zhu Jianhua, Lin Mingsen, et al. 2014. Assessment of the initial sea surface temperature product of the scanning microwave radiometer aboard on HY-2 satellite. Acta Oceanologica Sinica, 33(1): 109-113

Zheng Chongwei, Pan Jing, Li Jiaxun. 2013. Assessing the China Sea Wind Energy and Wave Energy Resources from 1988 to 2009. Ocean Engineering, 65: 39-48

Zheng Weizhong, Zou Chengzhi. 2010. Three-dimensional variational data assimilation of WindSat ocean surface winds for the genesis and forecasting of tropical storm Henri. Scientia Meteorologica Sinica, 30(5): 615-620

Relative Articles

Supplements(0)

Cited By

Proportional views