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Precipitation Microphysics of Tropical Cyclones Over the Western North Pacific Based on GPM DPR Observations: A Preliminary Analysis
Author(s) -
Huang Hao,
Chen Fengjiao
Publication year - 2019
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2018jd029454
Subject(s) - precipitation , environmental science , convection , precipitation types , atmospheric sciences , drizzle , tropical cyclone , coalescence (physics) , liquid water path , climatology , cloud physics , rainband , meteorology , geology , physics , cloud computing , astrobiology , computer science , operating system
Using observations from the dual‐frequency precipitation radar (DPR) onboard the Global Precipitation Measurement mission (GPM) satellite, this study analyzes the microphysical structures and processes of tropical cyclone (TC) precipitation over the western North Pacific in terms of different precipitation efficiency indices (PEIs). The statistical results show that the mean mass‐weighted mean diameter of raindrops ( D m ) at 2 km is 1.67 mm (1.37 mm) for convective (stratiform) precipitating clouds. Precipitating clouds with high PEI have higher liquid water path than nonliquid water path for both convective and stratiform clouds. The mean D m of convective and stratiform precipitation increases as the PEI increases. The vertical profiles of D m and reflectivity ( Z e ) for convective and stratiform precipitating clouds in TCs differ substantially as the PEI changes. Below the melting level, there is a clear decrease (increase) in D m and Z e toward the surface for clouds with low (high) PEI. In general, clouds within TCs producing the most efficient precipitation are characterized by strong coalescence, not only for small droplets but also for relatively large raindrops; in contrast, the breakup of hydrometeors is the dominant process in convective and stratiform precipitating clouds with low PEI. These results will help validate and improve the hydrometeor parameterization schemes in cloud and climate models.