The lack of adequate observational information over the ocean, create a great difficulty in prediction of ocean state near
the Indian coasts. Frequent satellite passes over this region provides valuable wind data resources that can be used to
force regional models to evaluate ocean wave spectrum near coasts with a better accuracy. In this work both
scatterometer wind from QuikSCAT as well as the ETA model wind from NCMRWF are used to force coastal wave
model SWAN nested in open-ocean WAM model. The results indicate that the SWAN nested in WAM predicts the wind
generated wave height with better accuracy when forced when forced with the QuikSCAT wind. But the swell height
predominantly depends on the boundary conditions provided on the model.
In this study global temperature profiles available from Argo have been assimilated into an Ocean General Circulation
Model (OGCM) to study its impact on ocean temperatures. In the control run the model was forced with daily
QuickSCAT derived scatterometer winds for the period Jan-June 2004 and air temperature, specific humidity, net shortwave
and net long wave radiation from NCEP reanalysis. Two assimilation experiments were performed for Jan-Jun
2004; one in which the monthly averaged Argo profiles were assimilated in the OGCM using nudging technique (Exp-1)
and another experiment (Exp-2) in which daily data from Argo was assimilated into the OGCM using Cressman
technique. Temperature outputs from all the three runs (control and assimilation runs) were first inter-compared and then
compared with independent observations from one of the Indian Ocean TRITON buoys. Errors in surface temperature at
the TRITON buoy location are reduced by 37% and 16% in Exp-1 and Exp-2 respectively. However, the variance
explained in surface temperature with respect to observations is reduced in the assimilated runs as compared to the
control run. Subsurface features like ILD and D20 show significant improvement in terms of error reduction in both the
experiments implying improvement in the mixed layer and the thermocline region. Exp-2 scores over Exp-1 in terms of
the explained variance of ILD and D20. This is so because in exp-1 monthly averaged data is assimilated which
constraints the high frequency variability of the parameter.
Possibility of predicting surface boundary layer winds over coastal land and ocean has been explored in this paper. Prediction has been effected using a modern nonlinear data-fitting algorithm known as genetic algorithm (GA) based on the Darwinian evolutionary theory. Time series of tower-mounted anemometer measured wind speed has been used for carrying out forecast over land while time series of satellite scatterometer derived winds has been used for forecast over coastal ocean. The prediction over land can feed into weather advisories required for rocket launching stations while prediction over coastal ocean can be of use in offshore industries.