The nature of the inherent temporal variability of surface winds is analyzed by comparison of winds obtained through different measurement methods. In this work, an auto-correlation analysis of a time series data of surface winds measuredin situ by a deep water buoy in the Indian Ocean has been carried out. Hourly time series data available for 240 hours in the month of May, 1999 were subjected to an auto-correlation analysis. The analysis indicates an exponential fall of the autocorrelation in the first few hours with a decorrelation time scale of about 6 hours. For a meaningful comparison between satellite derived products andin situ data, satellite data acquired at different time intervals should be used with appropriate ‘weights’, rather than treating the data as concurrent in time. This paper presents a scheme for temporal weighting using the auto-correlation analysis. These temporal ‘weights’ can potentially improve the root mean square (rms) deviation between satellite andin situ measurements. A case study using the TRMM Microwave Imager (TMI) and Indian Ocean buoy wind speed data resulted in an improvement of about 10%.

Time series observations of significant wave heights in the Bay of Bengal were subjected to auto-correlation analysis to determine temporal variability scale. The analysis indicates an exponential fall of auto-correlation in the first few hours with a decorrelation time scale of about six hours. A similar figure was found earlier for ocean surface winds. The nature of variation of auto-correlation with time lags was also found to be similar for winds and wave heights