1 A dual-polarization radar hydrometeor classification algorithm for ...

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558559560561562563564565566567568569570571572573574575576577578579580plate crystal growth, snowflake aggregation, as well as small-scale melting layer fluctuations andits descent into a vertical bright band. These phenomena were confirmed with externaltemperature sources and surface observations where available, but further testing with more insitumeasurements should be completed in the future.The expected polarimetric radar value ranges, or membership beta functions, used in thealgorithm are trustworthy because they were derived from the universal, physical properties ofdendrites, plates, stratiform rain, freezing rain, and sleet. It was interesting to find that plateshave higher Z dr but lower K dp than dendrites, and this relationship can be used to distinguish thetwo crystal types. Sleet and freezing rain do not have unique expected value ranges from rain andtherefore could not be discerned by the 1-D membership beta functions used here. However, amore advanced algorithm with texture fields and/or 2-D membership beta functions might beable to detect the refreezing signature itself. More thermodynamic information could also beincorporated to differentiate rain, freezing rain, and sleet properly. We simply use soundingtemperature to classify the possibility of freezing/frozen raindrops below the melting layer.The algorithm worked well on four different radar platforms at X-, C-, and S-band invarious regions across the US once a variable weighting system and wavelength-dependent snowmembership beta functions were implemented to make the algorithm more efficient. As long aspolarimetric data are well calibrated, no additional modifications from this text should benecessary to run the algorithm on any radar at these frequencies. The algorithm tended to bemore robust and precise at shorter wavelengths because of its dependence on K dp .HCA output could be used in concert with vertical velocity measurements and in-situthermodynamic data to determine relationships between DGZs, supercooled liquid water, andupdraft speeds. Graupel and heavy-rain categories should also be added to this algorithm in the26
581582583584585586future. The descent of semi-melted particles classified as wet snow could be studied for theirimportance in forecasting near-surface phase changes. Methodologies described herein couldalso be applied to a multiple-wavelength hydrometeor classification algorithm for dual-frequencyradars or different, collocated radars. Finally, hydrometeor classification efforts could be used toinform quantitative precipitation estimation, ice water content calculations, and numerical modelparameterizations.58727
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Page 39 and 40: 878879880881Tables.Table 1: Microph
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Page 47 and 48: 963964965966967968Fig. 2: K dp, Z d
Page 49 and 50: 974975976977Fig. 4: C-band OU-PRIME
Page 51 and 52: 984985986987Fig. 6: X-, C-, and S-b
Page 53 and 54: 991992993Fig. 8: X-, C-, and S-band
Page 55 and 56: 99799899910001001Fig. 10: X-band CA
Page 57 and 58: 100610071008Fig. 12: S-band KICT Z
Page 59: 1013101410151016Fig. 14: X-band CSU

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