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In and Out of Equilibrium 2A Hierarchical Bayes Model for Combining Precipitation Measurements from Different Sources

In and Out of Equilibrium 2: A Hierarchical Bayes Model for Combining Precipitation Measurements... [Surface rain rate is an important climatic variable and many entities are interested in obtaining accurate rain rate estimates. Rain rate, however, cannot be measured directly by currently available instrumentation. A hierarchical Bayes model is used as the framework for estimating rain rate parameters through time, conditional on observations from multiple instruments such as rain gauges, ground radars, and distrometers. The hierarchical model incorporates relationships between physical rainfall processes and collected data. A key feature of this model is the evolution of drop-size distributions (DSD) as a hidden process. An unobserved DSD is modeled as two independent component processes; 1) an AR (1) time-varying mean with GARCH errors for the total number of drops evolving through time, and 2) a time-varying lognormal distribution for the size of drops. From the modeled DSDs, precipitation parameters of interest, including rain rate, are calculated along with associated uncertainty. This model formulation deviates from the common notion of rain gauges as “ground truth”; rather, information from the various precipitation measurements is incorporated into the parameter estimates and the estimate of the hidden process. The model is implemented using Markov chain Monte Carlo methods.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

In and Out of Equilibrium 2A Hierarchical Bayes Model for Combining Precipitation Measurements from Different Sources

Part of the Progress in Probability Book Series (volume 60)
Editors: Sidoravicius, Vladas; Vares, Maria Eulália
Cardoso, Tamre P.; Guttorp, Peter
In and Out of Equilibrium 2 — Jan 1, 2008
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References (35)

Publisher
Birkhäuser Basel
Copyright
© Birkhäuser Basel 2008
ISBN
978-3-7643-8785-3
Pages
185 –210
DOI
10.1007/978-3-7643-8786-0_9
Publisher site
See Chapter on Publisher Site

Abstract

[Surface rain rate is an important climatic variable and many entities are interested in obtaining accurate rain rate estimates. Rain rate, however, cannot be measured directly by currently available instrumentation. A hierarchical Bayes model is used as the framework for estimating rain rate parameters through time, conditional on observations from multiple instruments such as rain gauges, ground radars, and distrometers. The hierarchical model incorporates relationships between physical rainfall processes and collected data. A key feature of this model is the evolution of drop-size distributions (DSD) as a hidden process. An unobserved DSD is modeled as two independent component processes; 1) an AR (1) time-varying mean with GARCH errors for the total number of drops evolving through time, and 2) a time-varying lognormal distribution for the size of drops. From the modeled DSDs, precipitation parameters of interest, including rain rate, are calculated along with associated uncertainty. This model formulation deviates from the common notion of rain gauges as “ground truth”; rather, information from the various precipitation measurements is incorporated into the parameter estimates and the estimate of the hidden process. The model is implemented using Markov chain Monte Carlo methods.]

Published: Jan 1, 2008

Keywords: Hierarchical Bayes; MCMC; Precipitation; Rain rate; Drop-size distribution; 62P12; 62M09; 86A10

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