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| Titel |
Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach |
| VerfasserIn |
I. Hussain, J. Pilz, G. Spoeck |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7340
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| Digitales Dokument |
URL |
| Erschienen |
In: Precipitation: Measurement, Climatology, Remote Sensing, and Modeling (EGU Session 2009) ; Nr. 25 (2010-03-30), S.97-102 |
| Datensatznummer |
250015743
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| Publikation (Nr.) |
 copernicus.org/adgeo-25-97-2010.pdf |
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| Zusammenfassung |
| 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. |
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