Predicting cyclone intensities in the Indian Ocean has been a challenging problem. Because of the highly varying bathymetry of the Indian coast, even a slight error in the prediction of landfall point and intensity can lead to a totally different storm surge height. Though capabilities of cyclone track prediction have significantly improved during recent years, cyclone intensity forecasts still need improvement. Various dynamical and statistical models have different rates of success for cyclone intensity prediction. In addition to atmospheric parameters and sea surface temperature (SST), another important parameter that enhances the understanding of the intensification of the cyclones is the upper ocean heat storage that is generally reflected in the oceanic eddies and dynamic topography. Sea surface height anomalies (SSHAs) from radar altimeters can provide information on this parameter. The relationship between the SSHAs and the associated hydrographic structure, particularly of eddies, is discussed by Ali et al. [1998], Gopalan et al. [2000], Babu et al. [2003], and Gopalakrishna et al. [2003]. Because of these changes in hydrographic features caused by SSHAs, warm (cold) core oceanic eddies have more (less) heat content compared with their surroundings.

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Oceans are reservoirs of heat energy represented by the heat content or the mean temperature, and are the source of energy for the atmospheric processes. Which process of the atmosphere interacts with the energy of which layer of the ocean is not clear, primarily, because of the nonavailability of oceanic heat energy of different layers on a required temporal and spatial scales. Realizing this requirement, we compute the ocean heat content (OHC) and the ocean mean temperature (OMT) from surface to 50, 100, 150, 200, 300, 500, 700 m and upto 26 • C isotherm depth. Thus, we computed altogether 16 variables from satellite observations of sea surface height anomaly (SSHA), sea surface temperature (SST), and the climatological values of the above 16 variables through an artificial neural network (ANN). The model is developed using 11 472 in situ and satellite collocated observations and is validated using 2479 independent values that are not used for developing the model. These estimations have a strong Pearson correlation coefficient, r, of more than 0.90 (at 99% confidence level) between the estimated and in situ values. These parameters are provided on near real time daily basis at a spatial resolution of 0.25 • at the Bhuvan website of National Remote Sensing Centre, Indian Space Research Organisation, which can be downloaded by a researcher for further ocean-atmosphere interaction investigations. Index Terms-Artificial neural network (ANN), ocean heat content (OHC), ocean mean temperature (OMT), sea surface height anomaly (SSHA), sea surface temperature (SST).