With the aim of generating a finer time scale data from a coarser time scale observations, the paper develops a rainfall disaggregation method as a combination of Bayesian statespace modeling and the adjusting procedure. The method uses spatio-tempral model incorporating the cross-covariance structure between spatial observation sites. The paper develops algorithms for estimating the parameters in terms of Bayesian method, and for generating finer time scale data with and without adjusting procedure. The model and the computation methods are applied to spatio-temporal rainfall observation data from two rain gages in Sampean Watershed, Bondowoso, Indonesia. Simulation study demonstrates that the Bayesian state space model with adjusting procedure performs better than the model without adjusting procedure, in terms of preserving some of the important data characteristics. © 2013 by CESER Publications.
|Journal||International Journal of Applied Mathematics and Statistics|
|Publication status||Published - 2013|
Astutik, S., Iriawan, N., Nair, G., Suhartono, N. V., & Sutikno, N. V. (2013). Bayesian state space modeling for spatio-temporal rainfall disaggregation. International Journal of Applied Mathematics and Statistics, 37(7), 26-37.