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Atmosphere 2018, 9(9), 360;

Influence of Disdrometer Type on Weather Radar Algorithms from Measured DSD: Application to Italian Climatology

Radar and Surveillance Systems (RaSS) Laboratory, National Interuniversity Consortium for Telecommunications (CNIT), 56124 Pisa, Italy
Institute of Atmospheric Sciences and Climate, National Research Council of Italy (CNR), 00133 Rome, Italy
Author to whom correspondence should be addressed.
Received: 24 July 2018 / Revised: 11 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
(This article belongs to the Special Issue Precipitation: Measurement and Modeling)
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Relations for retrieving precipitation and attenuation information from radar measurements play a key role in radar meteorology. The uncertainty in such relations highly affects the precipitation and attenuation estimates. Weather radar algorithms are often derived by applying regression methods to precipitation measurements and radar observables simulated from datasets of drop size distributions (DSD) using microphysical and electromagnetic assumptions. DSD datasets can be derived from theoretical considerations or obtained from experimental measurements collected throughout the years by disdrometers. Although the relations obtained from experimental disdrometer datasets can be generally considered more representative of a specific climatology, the measuring errors, which depend on the specific type of disdrometer used, introduce an element of uncertainty to the final retrieval algorithms. Eventually, data quality checks and filtering procedures applied to disdrometer measurements play an important role. In this study, we pursue two main goals: (i) evaluate two different techniques for establishing weather radar algorithms from measured DSD, and (ii) investigate to what extent dual-polarization radar algorithms derived from experimental DSD datasets are influenced by the different error structures introduced by the various disdrometer types (namely 2D video disdrometer, first and second generation of OTT Parsivel disdrometer, and Thies Clima disdrometer) used to collect the data. Furthermore, weather radar algorithms optimized for Italian climatology are presented and discussed. View Full-Text
Keywords: weather radar retrieval algorithms; disdrometer data; rain drop size distribution weather radar retrieval algorithms; disdrometer data; rain drop size distribution

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Adirosi, E.; Roberto, N.; Montopoli, M.; Gorgucci, E.; Baldini, L. Influence of Disdrometer Type on Weather Radar Algorithms from Measured DSD: Application to Italian Climatology. Atmosphere 2018, 9, 360.

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