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Besides improving the understanding of the physics of the challenging problem of monsoon prediction, it is necessary to evaluate the efficiency of the input parameters used in models. Sea-surface temperature (SST) is the only oceanographic parameter applied in most of the monsoon forecasting models, which many times do not represent the heat energy available to the atmosphere. We studied the impacts of ocean mean temperature (OMT), representing the heat energy of the upper ocean, and SST on the all India summer monsoon rainfall through a statistical relation during 1993-2013 and found that OMT has a better link than SST.

Tropical cyclone heat potential (TCHP) is an important ocean parameter influencing cyclones and hurricanes. The best approach for computing TCHP is to use in situ measurements. However, since in situ data have both spatial and temporal limitations, there is a need for satellite-based estimations. One potential solution is to use sea surface height anomalies (SSHAs) from altimeter observations. However, any estimation derived from satellite measurements requires extensive regional validation. In this letter, we compare satellite-derived TCHP values with those estimated using in situ measurements of the North Indian Ocean collected during 1993-2009. All the available measurements collected from the conductivity temperature and depth (CTD) profiler, expendable CTD profiler (XCTD), bathythermograph (BT), expendable BT (XBT) and Argo floats were used to estimate in situ derived TCHP values. TCHP estimations from satellite observations and in situ measurements are well correlated, with coefficient of determination R 2 of 0.65 (0.76) and a scatter index (SI) of 0.33 (0.25) on a daily (monthly) basis for the North Indian Ocean.

Estimation of the partial pressure of carbon dioxide (pCO2) and its space-time variability in global surface ocean waters is essential for understanding the carbon cycle and predicting the future atmospheric CO2 concentration. Until recently, only basin-scale distribution of pCO2 has been reported by using satellite-derived climatological data due to the lack of models for global-scale applications. In the present work, a multiparametric non-linear regression (MPNR) for the estimation of global-scale distribution of pCO2 on the ocean surface is developed using continuous in-situ measurements of pCO2, chlorophyll-a (Chla) concentration, sea surface temperature (SST) and sea surface salinity (SSS) obtained on a number of cruise programs in various regional oceanic waters. Analysis of these measurement data showed strong relationships of pCO2 with Chla, SST and SSS, because these three parameters are governed by the complex interactions of oceanographic (physical, biological and chemical) and meteorological processes and thus influence pCO2 levels over different spatial and temporal scales. In order to account for regional differences in the influences of these processes on pCO2, model parameterizations are derived as a function of Chla, SST and SSS data with different boundary conditions. Because the strength of each influencing parameters on pCO2 differed at different Chla, SST and SSS ranges, measurement data were grouped with reference to the Chla, SST and SSS ranges and significant correlations of the pCO2 with dominant processes were established: for example, an inverse correlation of the pCO2 with Chla, SST and SSS in polar and sub-polar regions, a positive correlation of the pCO2 with SST and SSS and an inverse correlation of the pCO2 with Chla in tropical and subtropical regions, and an inverse correlation of the pCO2 with SST and a positive correlation of the pCO2 with Chla and SSS in equatorial regions. This indicates that the relationship of pCO2 versus biological and physical parameters is more complex and an individual parameter alone would not serve as an accurate estimator of basin-and global-scale pCO2 trends. Thus, changes in Chla, SST and SSS were systematically analyzed as they account for biological and physical effects on pCO2 and best-constrained based upon their strong relationships with pCO2 using the MPNR regression approach. The accuracy of the MPNR was assessed using independent in-situ data and satellite pCO2 data derived from global Level-3 Chla, SST, and SSS data. Validation results showed that satellite-derived pCO2 data agreed with direct in-situ pCO2 measurements with an RMSE 6.68-7.5 µatm and a relative error less than 5% which is significantly small as compared to the errors associated with earlier satellite pCO2 computations. The

If a cyclone's track and intensity can be predicted precisely, the losses due to cyclones can be minimized. While efforts are under way to improve the understanding of the physics of the problem of track and intensity prediction, it is worthwhile to look again at the efficiency of the input parameters presently used in models and to look for new approaches.