Supervised Classification and Estimation of Hydrometeors From C-Band Dual-Polarized Radars: A Bayesian Approach

Marzano, Frank S.; Scaranari, D.; Montopoli, Mario; Vulpiani, G.

doi:10.1109/tgrs.2007.906476

Cited by 62 publications

(47 citation statements)

References 24 publications

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“…The current HC algorithms used in the polarimetric chain are an adjusted version of Park et al (2009) for X band, the one proposed by Marzano et al (2006) for C band, and the one proposed by Park et al (2009) for S band. The polarimetric chain represents the set of all processing and analyzing programs for polarimetric data; see Boumahmoud et al (2010) for more details.…”

Section: Formulation Of the Fuzzy Logic Approachmentioning

confidence: 99%

A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars

Al-Sakka

Boumahmoud

Fradon

et al. 2013

Journal of Applied Meteorology and Climatology

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A new fuzzy logic hydrometeor classification algorithm is proposed that takes into account data-based membership functions, measurement conditions, and three-dimensional temperature information provided by a high-resolution nonhydrostatic numerical weather prediction model [the Application of Research to Operations at Mesoscale model (AROME)]. The formulation of the algorithm is unique for X-, C-, and S-band radars and employs wavelength-adapted bivariate membership functions for (Z H , Z DR ), (Z H , K DP ), and (Z H , r HV ) that were established by using real data collected by the French polarimetric radars and T-matrix simulations. The distortion of membership functions caused by deteriorating measurement conditions (e.g., precipitation-induced attenuation, signal-to-clutter ratio, signal-to-noise ratio, partial beam blocking, and distance) is documented empirically and subsequently parameterized in the algorithm. The result is an increase in the amount of overlapping between the membership functions of the different hydrometeor types. The relative difference between the probability function values of the first and second choice of the hydrometeor classification algorithm is analyzed as a measure of the quality of identification. Semiobjective scores are calculated using an expert-built validation dataset to assess the respective improvements brought by using ''richer'' temperature information and by using more realistic membership functions. These scores show a significant improvement in the detection of wet snow.

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Section: Formulation Of the Fuzzy Logic Approachmentioning

confidence: 99%

A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars

Al-Sakka

Boumahmoud

Fradon

et al. 2013

Journal of Applied Meteorology and Climatology

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“…Principles of fuzzy logic form the basis for most polarimetric classification algorithms; these were first explored by Straka and Zrni c (1993) and Straka (1996) and have since been further refined into more sophisticated classification routines described by Zrni c and Ryzhkov (1999), Vivekanandan et al (1999), Liu and Chandrasekar (2000), Zrni c et al (2001), Schuur et al (2003), Keenan (2003), Lim et al (2005), and Marzano et al (2008), among others.…”

Section: Introductionmentioning

confidence: 99%

The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS

Park

Ryzhkov

Zrnić

et al. 2009

Weather and Forecasting

400

321

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This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric variables and discerns 10 different classes of radar echoes. Furthermore, a methodology for the new fuzzy logic classification scheme is discussed and the results are illustrated using polarimetric radar data collected with the Norman, Oklahoma (KOUN), WSR-88D prototype radar during a mesoscale convective system event on 13 May 2005.

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“…a technique for hydrometeor classification aimed at partitioning a radar volume in terms of microphysical hydrometeor types. The algorithm provides 12 hydrometeor class index (see Table 2) for each radar range bin using a Bayesian decision rule starting from radar observables and temperature information (Marzano et al, 2008). The hydrometeor classification technique is trained with a radar backscattering-model simulation, based on the T matrix code where liquid, ice and mixed phase hydrometeors are simulated;…”

Section: Hydrorad Radar-based Product Generatormentioning

confidence: 99%

“…This mainly affects the classification procedure which assigns a not classified (NC) label to some pixels. As argued in Marzano et al (2008), if the minimum Bayesian distance is larger than a decision threshold, the corresponding radar bin is labeled as "not classified" (NC). The decision threshold is usually determined in an empirical way.…”

Section: Hydroalg Hydrometeor Classificationmentioning

confidence: 99%

Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project

Picciotti

Marzano

Anagnostou

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Hydro-meteorological hazards like convective\ud outbreaks leading to torrential rain and floods are among the\ud most critical environmental issues world-wide. In that context\ud weather radar observations have proven to be very useful\ud in providing information on the spatial distribution of rainfall\ud that can support early warning of floods. However, quantitative\ud precipitation estimation by radar is subjected to many\ud limitations and uncertainties. The use of dual-polarization\ud at high frequency (i.e. X-band) has proven particularly useful\ud for mitigating some of the limitation of operational systems,\ud by exploiting the benefit of easiness to transport and\ud deploy and the high spatial and temporal resolution achievable\ud at small antenna sizes. New developments on X-band\ud dual-polarization technology in recent years have received\ud the interest of scientific and operational communities in these\ud systems. New enterprises are focusing on the advancement of\ud cost-efficient mini-radar network technology, based on highfrequency\ud (mainly X-band) and low-power weather radar\ud systems for weather monitoring and hydro-meteorological\ud forecastin

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Supervised Classification and Estimation of Hydrometeors From C-Band Dual-Polarized Radars: A Bayesian Approach

Cited by 62 publications

References 24 publications

A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars

A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French X-, C-, and S-Band Polarimetric Radars

The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS

Coupling X-band dual-polarized mini-radars and hydro-meteorological forecast models: the HYDRORAD project

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