Abstract
In response to the drought experienced in Southern Italy a rain seeding project has been setup and developed during the years 1989-1994. The initiative was taken with the purpose of applying existing methods of rain enhancement technology to regions of south Italy including Puglia. The aim of this study is to provide statistical support for the evaluation of the experimental part of the project. In particular our aim is to reconstruct rainfall fields by combining two data sources: rainfall intensity as measured by ground raingauges and radar reflectivity. A difficulty in modeling the rainfall data here comes from rounding of many recorded rainguages. The rounding of the rainfall measurements make the data essentially discrete and models based on continuous distributions are not suitable for modeling these discrete data. In this study we extend two recently developed spatio-temporal models for continuous data to accommodate rounded rainfall measurements taking discrete values with positive probabilities. We use MCMC methods to implement the models and obtain forecasts in space and time together with their standard errors. We compare the two models using predictive Bayesian methods. The benefits of our modeling extensions are seen in accurate predictions of dry periods with no positive prediction standard errors.
Highlights
In this article our aim is to model a dataset on rain enhancing experiment through seeding operations conducted during the years 1989-1994 in the dry regions of south Italy starting with Puglia
In this study we have compared two competitive spatio-temporal modeling approaches for rainfall data obtained from a cloud seeding experiment in the regions of south Italy including Puglia
The Gaussian random effect model is seen to perform better than the Bayesian Kriged-Kalman filtering (BKKF) model
Summary
In this article our aim is to model a dataset on rain enhancing experiment through seeding operations conducted during the years 1989-1994 in the dry regions of south Italy starting with Puglia. The problem is to extend the model to accommodate more than one discrete rainfall value occurring with non-zero probability Another objective of this study is to develop methods for relating radar reflectance and rainfall intensity to reconstruct rainfall fields in the presence of the discrete rainfall amounts. 1 (4): 282-290, 2005 explicitly regressing rainfall data on the radar measurements in a spatio-temporal model We believe that this method is novel for data obtained from a rain seeding experiment conducted in a very dry region such as southern Italy. The dataset: Our data come from the rain enhancement project carried on in the South of Italy (Fig. 1) during the period 1989-1994 This is a very dry region and the total amount of annual rainfall is usually very small (approximately 80 millimeter on the average per year during the study period). Let X(s,t) denote a continuous latent variable and let there be k particular values of log rainfall λ1, λ2, ..., λk which may occur with positive
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