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Abstract. Statistical interpolation is applied to 8 years of cloud-interrupted satellite radiometer data to produce estimates of sea surface temperature (SST) at 10-day intervals in the Indo-Australian region. The data are 9-km resolution daily "best SST" values calculated globally by the NOAA/NASA Pathfinder project reanalysis of advanced very high resolution radiometer (AVHRR) data. The optimal averaging technique determines an unbiased estimate of the signal that has the minimum mean square variance from the data, within the limits of the expected measurement error. Previous studies have shown that the method is superior to other linear averaging techniques, especially that of simple composite averaging. The method is applied in the time domain only, preserving the 9-km spatial resolution of the data. The signal and noise covariances were evaluated from the data. This was done with care so that accurate estimates of the error bounds that bracket the optimally averaged values might be obtained. These error bounds were then verified against in situ data. A Markov function, (1 + r/a) exp (-r/a), where r is the time lag and a is a characteristic timescale, was fitted to the data and used for the signal correlation function. This was selected after evaluation of functional forms proposed in other studies. The effect on the analysis of geographical variation in the correlation function was considered. The computational demand of the repeated matrix operations in optimal interpolation was reduced by using a limited duration data window. The complete analysis procedure for the 8-year data set, comprising over 106 time series, was tractable on a modern workstation. The result is a set of SST maps for 1987-1994 at an interval of 10 days and a spatial resolution of 9 km. The analyses are suitable for applications such as high-resolution ocean and atmosphere modeling where the timescales and space scales of interest are comparable to the analysis (i.e., of the order of 10 days, 9 km) and for which the presence of gaps due to clouds is problematic. Some features of Indo-Australian regional mesoscale circulation that the analysis highlights are examined, including examples of detailed mesoscale SST evolution and interannual variability. IntroductionRemotely sensed sea surface temperature (SST) data from the radiometers on board NOAA meteorological satellites are used widely in studies of the ocean. Clouds obscure radiometer SST observations, and this is problematic for applications where an uninterrupted high-resolution SST data set is required. Cloud detection algorithms help minimize data loss, but the gaps created by cloud cover often must be filled by time and space averaging or interpolation. This paper describes the application of a statistical interpolation/averaging method to produce uninterrupted SST maps with a spatial resolution of 9 km and temporal separation of 10 days. The results are suitable as boundary conditions for high-resolution regional models of the ocean and atmosphere, and they also directly chart ...