## 1. Introduction

[2] Accurate information on the sea surface state such as wind vector is of great importance for modeling the air-sea interaction in weather and climate models. Therefore many remote sensing algorithms have been developed to retrieve the surface wind speed. With the Special Sensor/Microwave Imager (SSM/I) and collocated buoy data, *Goodberlet et al.* [1990] derived a simple regression algorithm using all seven SSM/I channels. *Wentz* [1992] also developed an algorithm to derive the wind direction with the weather model wind vectors as a first guess. Recently, *Yueh et al.* [1999], *Yueh and Wilson* [1999], and *Meissner and Wentz* [2002] also analyzed and confirmed the correlation between the microwave polarimetric signatures and the surface wind direction.

[3] The future passive microwave sensors such as the U.S. Navy WindSAT/Coriolis and U.S. National Polar-Orbiting Environmental Satellite System (NPOESS) Conical Microwave Imager Sounder (CMIS) are all developed with the polarimetric sensors for global remote sensing of surface wind vectors. The retrieval of the surface wind speed can be improved beyond the SSM/I performance by deriving the speed and direction simultaneously. In doing so, a physical algorithm often derives the state of the atmospheric parameters through iterations until the simulated radiances by the forward radiative transfer model converge to the measurements within a predefined error margin [*Schmetz and Turpeinen*, 1988; *Kummerow et al.*, 1989; *Weng and Grody*, 2000]. Since the retrieval of the sea surface wind direction is a nonlinear problem and the variation of the polarimetric signatures due to the wind direction is less than a few degrees in kelvins, the radiative transfer model must be developed very accurately. As an example, a fast and accurate polarimetric surface emissivity model is yet to be developed and to be utilized in the retrieval [*Yueh*, 1997; *Gasiewski and Kunkee*, 1994], while a number of accurate polarimetric atmospheric radiative transfer model have been developed and validated [*Weng*, 1992; *Haferman et al.*, 1993; *Liu et al.*, 1996].

[4] This study proposed a parameterized forward model and its tangent linear model that can be directly used in the ocean wind vector retrieval. The algorithm is tested with simulated measurements. In section 2, the physical base of the algorithm is described. A sensitivity of Stokes components to wind speed and direction is studied in section 3. The algorithm performance and discussion are given in sections 4 and 5, respectively.