A Bayesian Model to Study Spatio-temporal Variability of Latent Heat Flux and its Trend

This paper talks about two models. First model is presented to study space-time variability of latent heat flux, where latent heat flux has been decomposed into three periodic terms, spatio-temporal process term, long term trend and a term due to covariates. And the second model is presented to characterize the long term trend and its possible causes. For both the models Bayesian approach was adopted. The method presented is particularly useful for characterizing environmental spatio- temporal processes variability. The model parameters were sampled using a Markov chain Monte Carlo simulation technique. The models were used for studying latent heat flux components in the Indian Ocean for the period of January 1985 to April 2010. The results showed that in LHF variability, dominant factors were annual variability, spatio-temporal variability and variability due to covariates. Further it has been found that the long term positive trend of LHF is dominated by the increase in wind speed. In some regions of Indian Ocean, increase in sea surface temperature has also been the cause for increase in LHF.