Abstract: The geophysical model function (GMF) describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that the GMF plays an important role in an ocean wind vector retrieval. The performance of the existing Ku-band model function QSCAT-1 is considered to be effective at low and moderate wind speed ranges. However, in the conditions of higher wind speeds, the existing algorithms diverge alarmingly. owing to the lack of in situ data required for developing the GMF for the high wind conditions, the QSCAT-1 appears to overestimate the σ₀, which results in underestimating the wind speeds. Several match-up QuikSCAT and special sensor microwave/imager (SSM/I) wind speed measurements of the typhoons occurring in the west Pacific Ocean are analyzed. The results show that the SSM/I wind exhibits better agreement with the "best track" analysis wind speed than the QuikSCAT wind retrieved using QSCAT-1. On the basis of this evaluation, a correction of the QSCAT-1 model function for wind speed above 16 m/s is proposed, which uses the collocated SSM/I and QuikSCAT measurements as a training set, and a neural network approach as a multiple nonlinear regression technologytechnology.In order to validate the revised GMF for high winds, the modified GMF was applied to the QuikSCAT observations of Hurricane IOKE. The wind estimated by the QuikSCAT for Typhoon IOKE in 2006 was improved with the maximum wind speed reaching 55 m/s. An error analysis was performed using the wind fields from the Holland model as the surface truth. The results show an improved agreement with the Holland model wind when compared with the wind estimated using the QSCAT-1. However, large bias still existed, indicating that the effects of rain must be considered for further improvement.