Comparison of WindSat and buoy-measured ocean products from 2004 to 2013
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摘要: 基于海上浮标实测数据,本文对2004年1月到2013年12月的全极化微波辐射计WindSat海面风矢量和海表面温度产品数据进行了评估。对于WindSat的WSPD_LF风速产品,不同研究区域(包括热带太平洋、热带印度洋、热带大西洋及美国沿岸地区)的风速反演误差变化范围为从-0.07 m/s 到0.08 m/s,整体的均方根误差为0.98 m/s,不同研究区域的风速反演均方根误差变化范围为从-0.82 m/s到 1.16 m/s;总体上,位于开阔热带洋面上的风速反演结果优于美国沿岸区域的反演结果。WSPD_LF风速的反演精度优于WSPD_MF的结果。基于WindSat亮温数据反演得到的其它地球物理参数,如水汽含量、云中液态水含量和海表面温度,在一定程度上影响着WindSat反演的风速精度。对于WindSat风向反演结果,当风速大于6 m/s时,其风向反演的均方根误差为19.59;研究区域内,平均偏差变化范围为从-0.46 到 1.19。对于海表面温度反演结果,热带太平洋区域的反演结果与浮标测量结果具有较好的一致性,均方根偏差为0.36,整体上,低纬度区域的海表面温度反演精度优于中高纬度的反演精度。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.
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Key words:
- WindSat /
- polarimetric microwave radiometer /
- wind vector /
- sea surface temperature /
- validation
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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
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