Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification

By Hongping Liu, Venkatachalam Chandrasekar. 2000

382 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/a9331e956b2caff53a085e34669dbf8ac3de3883

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Citations in the corpus

The 29 June 2000 Supercell Observed during STEPS. Part I: Kinematics and Microphysics (2005, methodology): “A fuzzy logic hydrometeor classification algorithm (hereafter FHC), adapted from Liu and Chandrasekar (2000) and Straka et al. (2000), was implemented for the Cartesian gridded data to estimate bulk hydrometeor types within the FIG.”
A Robust C-Band Hydrometeor Identification Algorithm and Application to a Long-Term Polarimetric Radar Dataset (2013, methodology): “Hydrometeor identification using fuzzy logic allows for determination of bulk microphysics within a given radar volume (Liu and Chandrasekar 2000; Ryzhkov et al. 2005c; Chandrasekar et al. 2013).”
The relationship between lightning activity and ice fluxes in thunderstorms (2008, methodology): “from the polarimetric radar data [e.g., Vivekanandan et al., 1999; Straka et al., 2000; Liu and Chandrasekar, 2000; Zrnic et al., 2001].”
Inverted-polarity electrical structures in thunderstorms in the Severe Thunderstorm Electrification and Precipitation Study (STEPS) (2005, methodology): “17 Examples of hydrometeor classification using the fuzzy logic scheme of Liu and Chandrasekar (2000) are presented in Fig.”
Testing a Procedure for Automatic Classification of Hydrometeor Types (2001, methodology): “Much has been written about this method (Mendel 1995), and several articles with applications to meteorological problems have appeared (Straka 1996; Cornman et al. 1998; Vivekanandan et al. 1999; Liu and Chandrasekar 2000).”
A Theory-Based Hydrometeor Identification Algorithm for X-Band Polarimetric Radars (2009, methodology): “To date, polarimetric-based hydrometeor identiﬁcation (HID) algorithms have been appliedtodata from primarily S-band (10–11 cm) radars (Vivekanandan et al. 1999; Liu and Chandrasekar 2000; Straka et al. 2000, hereafter S00; Ryzhkov et al. 2005; Tessendorf et al. 2005).”
Principles and applications of dual-polarization weather radar. Part II: Warm- and cold-season applications (2013, methodology): “The shape of the membership function is given, for example, by trapezoids (e.g., Park et al. 2009) or beta functions (e.g., Liu and Chandrasekar 2000; Lim et al. 2005).”
The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS (2009, methodology): “…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.”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, background): “…classification” or “hydrometeor identification” algorithms provide some operational value in comparisons to simulations (e.g., Dolan & Rutledge, 2009; Liu & Chandrasekar, 2000; Park et al., 2009; Straka et al., 2000; Vivekanandan et al., 1999) but impose predefined categories that may have…”