Next Article in Journal
Current Challenges in Orographic Flow Dynamics: Turbulent Exchange Due to Low-Level Gravity-Wave Processes
Next Article in Special Issue
Sensitivity Study of WRF Numerical Modeling for Forecasting Heavy Rainfall in Sri Lanka
Previous Article in Journal
Characteristics of Atmospheric Boundary Layer Structure during PM2.5 and Ozone Pollution Events in Wuhan, China
Previous Article in Special Issue
Dynamic Ensemble Analysis of Frontal Placement Impacts in the Presence of Elevated Thunderstorms during PRECIP Events
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Atmosphere 2018, 9(9), 360; https://doi.org/10.3390/atmos9090360

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

1
Radar and Surveillance Systems (RaSS) Laboratory, National Interuniversity Consortium for Telecommunications (CNIT), 56124 Pisa, Italy
2
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)
Full-Text   |   PDF [4544 KB, uploaded 19 September 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Atmosphere EISSN 2073-4433 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top