| Popis: |
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. |
