Due to the lower efficiency and the larger wind direction error of traditional algorithms, a novel integrated wind retrieval algorithm is proposed for conically scanning scatterometers. The proposed algorithm has the dual advantages of less computational cost and higher wind direction retrieval accuracy by integrating the wind speed standard deviation (WSSD) algorithm and the wind direction interval retrieval (DIR) algorithm. It adopts wind speed standard deviation as a criterion for searching possible wind vector solutions and retrieving a potential wind direction interval based on the change rate of the wind speed standard deviation. Moreover, a modified three-step ambiguity removal method is designed to let more wind directions be selected in the process of nudging and filtering. The performance of the new algorithm is illustrated by retrieval experiments using 300 orbits of SeaWinds/QuikSCAT L2A data (backscatter coefficients at 25 km resolution) and co-located buoy data. Experimental results indicate that the new algorithm can evidently enhance the wind direction retrieval accuracy, especially in the nadir region. In comparison with the SeaWinds L2B Version 2 25 km selected wind product (retrieved wind fields), an improvement of 5.1° in wind direction retrieval can be made by the new algorithm for that?region.
References
[1]
Schroeder, L.C.; Boggs, D.H.; Dome, G.J.; Halberstam, I.M.; Jones, W.L.; Pierson, W.J.; Wentz, F.J. The relationship between wind vector and normalized radar cross section used to derive SEASAT-A satellite scatterometer winds. J. Geophys. Res 1982, 87, 3318–3336.
[2]
Jones, W.L.; Schroeder, L.C.; Boggs, D.H.; Bracalente, E.M.; Brown, R.A.; Dome, G.J.; Pierson, W.J.; Wentz, F.J. The SEASAT-A satellite scatterometer: The geophysical evaluation of remote sensed wind vectors over the ocean. J. Geophys. Res 1982, 87, 3297–3317.
[3]
Wentz, F.J.; Peteherych, S.; Thomas, L.A. A model function for ocean radar cross sections at 14.6 GHz. J. Geophys. Res 1984, 89, 3689–3740.
[4]
Wentz, F.J.; Mattox, L.A. New algorithms for microwave measurements of ocean winds: Application to seasat and the special sensor microwave imager. J. Geophys. Res 1986, 91, 2289–2307.
[5]
Nghiem, S.V.; Li, F.K.; Neumann, G. Ku-Band Ocean Backscatter Functions for Surface Wind Retrieval. Proceedings of the IEEE Geosicence and Remote Sensing Symposium, Lincoln Nebraska, NE, USA, 27–31 May 1996; 3, pp. 1469–1471.
[6]
Wentz, F.J.; Smith, D.K. A model function for ocean radar cross sections at 14 GHz derived from NSCAT observations. J. Geophys. Res 1999, 104, 11499–11514.
[7]
Chi, C.Y.; Li, F.K. A comparative study of several wind estimation algorithms for spaceborne scatterometers. IEEE Trans. Geosci. Remote Sens 1988, 26, 115–121.
[8]
Stoffelen, A.; Anderson, D. Scatterometer data interpretation: Measurement space and inversion. J. Atmos. Ocean. Technol 1997, 14, 1298–1313.
[9]
Stoffelen, A.; Portabella, M. On Bayesian scatterometer wind inversion. IEEE Trans. Geosci. Remote Sens 2006, 44, 1523–1533.
[10]
Freilich, M.H. SeaWinds Algorithm Theoretical Basis Document. Available online: http://podaac.jpl.nasa.gov/quikscat/qscat-doc/html (accessed on 18 March 2002).
[11]
Xie, X.T.; Fang, Y.; Chen, X.X.; Chen, K.H. A New Fast Wind Vector Retrieval Algorithm for SeaWinds Scatterometer. Proceedings of the 2005 IEEE Geoscience and Remote Sensing Symposium, Seoul, Korea, 25–29 July 2005; V, pp. 3298–3301.
[12]
Stiles, B.W.; Pollard, B.D.; Dunbar, R.S. Direction interval retrieval with thresholded nudging: A method for improving the accuracy of QuikSCAT winds. IEEE Trans. Geosci. Remote Sens 2002, 40, 79–89.
[13]
Xie, X.T.; Lin, M.S.; Huang, Z.; Zou, J.H.; Tian, D.X.; Liu, L.X.; Wang, X.N.; Dong, S.W. A Modified Wind Vector Retrieval Algorithm for Polarimetric Scatterometer. Proceedings of the IEEE Geosicence and Remote Sensing Symposium, Hawaii, HI, USA, 25–30 July 2010; pp. 52–55.
[14]
Xie, X.T.; Lin, M.S.; Chen, K.H.; Huang, Z.; Liu, L.X.; Tian, D.X.; Wang, X.N.; Chen, W.X.; He, R.R.; Zou, J.H. A Wind Direction Extension Based Algorithm for Scatterometer Wind Vector Retrieval. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 2821–2824.
[15]
Shaffer, S.J.; Dunbar, R.S.; Hsaio, S.V.; Long, D.G. A median-filter-based ambiguity removal algorithm for NSCAT. IEEE Trans. Geosci. Remote Sens 1991, 29, 167–173.
[16]
Weiss, B. L2A Data Software Interface Specification (SIS-2). Available online: ftp://ftp.scp.byu.edu/data/qscat/docs/PD686-644-2.pdf (accessed on 15 May 2013).
[17]
Elfouhaily, T.; Chapron, B.; Katsaros, K. A unified directional spectrum for long and short wind-driven waves. J. Geophys. Res 1997, 102, 15781–15796.
[18]
Fung, A.K.; Lee, K.K. A semi-empirical sea spectrum model for scattering coefficient estimation. IEEE J. Ocean. Eng 1982, (OE-7), 166–176.