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Spatial reconstruction of rainfall fields from rain gauge and radar data

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Abstract

Rainfall is a phenomenon difficult to model and predict, for the strong spatial and temporal heterogeneity and the presence of many zero values. We deal with hourly rainfall data provided by rain gauges, sparsely distributed on the ground, and radar data available on a fine grid of pixels. Radar data overcome the problem of sparseness of the rain gauge network, but are not reliable for the assessment of rain amounts. In this work we investigate how to calibrate radar measurements via rain gauge data and make spatial predictions for hourly rainfall, by means of Monte Carlo Markov Chain algorithms in a Bayesian hierarchical framework. We use zero-inflated distributions for taking zero-measurements into account. Several models are compared both in terms of data fitting and predictive performances on a set of validation sites. Finally, rainfall fields are reconstructed and standard error estimates at each prediction site are shown via easy-to-read spatial maps.

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Acknowledgments

We wish to thank ARPA-SIMC Emilia Romagna for introducing us to the problem, providing data and performing data pre-processing. The research work underlying this paper was funded by an ARPA-SIMC/Department of Statistical Sciences agreement (nr.20/2012) and a FIRB 2012 grant (project no. RBFR12URQJ; title: Statistical modelling of environmental phenomena: pollution, meteorology, health and their interactions) for research projects by the Italian Ministry of Education, Universities and Research.

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Correspondence to Francesca Bruno.

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Bruno, F., Cocchi, D., Greco, F. et al. Spatial reconstruction of rainfall fields from rain gauge and radar data. Stoch Environ Res Risk Assess 28, 1235–1245 (2014). https://doi.org/10.1007/s00477-013-0812-0

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