Advances in Geosciences www.adv-geosci.net 1680-7340 1680-7359 25 Precipitation: Measurement, Climatology, Remote Sensing, and Modeling (EGU Session 2009) 2010 10.5194/adgeo-25-97-2010 http://www.adv-geosci.net/25/97/2010/ http://www.adv-geosci.net/25/97/2010/adgeo-25-97-2010.html http://www.adv-geosci.net/25/97/2010/adgeo-25-97-2010.pdf 97 102 2010-03-30 Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach I. Hussain ijaz.hussain.kherani@gmail.com J. Pilz G. Spoeck Department of Statistics, University of Klagenfurt, Klagenfurt, Austria The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolation are used in order to predict precipitation in Pakistan during the monsoon period. Monthly average precipitation data whose time domain is the monsoon period for the years 1974–2000 and whose spatial domain are various regions in Pakistan are considered. The prediction of space-time precipitation is applicable in many sectors of industry and economy in Pakistan especially; the agricultural sector. Mean field maps and prediction error maps for both methods are estimated and compared. In this paper it is shown that the transformed hierarchical Bayesian model is providing more accuracy and lower prediction error compared to the spatio-temporal Bayesian maximum entropy method; additionally, the transformed hierarchical Bayesian model also provides predictive distributions. Brown, P. J., Le, N. D., and Zidek, J. V.: Inference for a covariance matrix, Aspects of Uncertainty: A Tribute to DV Lindley, edited by: Freeman, P. R. and Smith, A. F. M.: Chichester, Wiley, 77–92, 1994. Chiles, J. P. and Delfiner, P.: Geostatistics, Modeling Spatial Uncertainty, Wiley-Interscience, 1999. Christakos, G.: Random Field Models in Earth Sciences, Academic Press, 1992. Christakos, G., Bogaert, P., and Serre, M. L.: it Temporal Gis, Advanced Functions for Field-Based Applications, Springer Verlag, 2002. \bibitem[Kyriakidis and Journel~(1999)] kyriakidis Kyriakidis, P. C. and Journel, A. G.: Geostatistical SpaceTime Models: A Review, Mathematical geology, 31, 651–684, 1999. Le, N. D., Sun, W., and Zidek, J. V.: Bayesian Multivariate Spatial Interpolation with Data Missing by Design, J. Roy. Stat. Soc. B, 59, 501–510, 1997. Le, N. D. and Zidek, J. V.: Statistical Analysis of Environmental Space-Time Processes, Springer Verlag, 272–294, 2006. Sampson, P. D. and Guttorp, P.: Nonparametric Estimation of Nonstationary Spatial Covariance Structure, J. Am. Stat. Assoc., 87, 108–119, 1992.