In the current scenario, ocean observations have immense potential to monitor real-time environment changes in the Indian ocean and atmosphere. This brings new opportunities to better understand the role of the oceans in the geophysical system. Specifically, the ability to forecast the weather, study the global climate change and minimize the disastrous effects of weather events such as the cyclones, storm surges and the tsunamis is dependent on the capability to observe the oceanographic parameters such as ocean surface currents, surface wind speed, and wind direction. For the analysis of the climate and weather data, the statistical models of these oceanographic variables play a very important role. The ocean surface wind speed is one of the essential parameters influencing the ocean currents, wind direction, studies with air-sea fluxes and several coastal applications. From the literature, it is observed that the Weibull probability distribution is widely used for modeling the wind speed data in the ocean and surface applications. In this paper, an improved statistical model based on a mixture of standard models is proposed. Specifically, the Gaussian-Gamma mixture (GGM) model is proposed for pdf of the ocean surface wind speed data. The model is validated using both the quantitative and qualitative analysis for long and short range data sets. It is found that the proposed model has better fit compared to the other existing models.