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In Situ Microphysical Observations of the 29–30 May 2012 Kingfisher, OK, Supercell With a Balloon‐Borne Video Disdrometer
Author(s) -
Waugh Sean M.,
Ziegler Conrad L.,
MacGorman Donald R.
Publication year - 2018
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2017jd027623
Subject(s) - disdrometer , supercell , particle (ecology) , storm , meteorology , ice crystals , precipitation , physics , environmental science , computational physics , geology , atmospheric sciences , oceanography , rain gauge
Abstract The microphysical characteristics of deep convective storms are a critically important, yet difficult aspect of storm morphology to observe in situ. These observations play a large role in several areas of ongoing research, but detailed observations in key portions of clouds do not exist with the currently available measurement platforms. To facilitate these observations, a balloon‐borne particle imaging device known as the PArticle Size, Image, and Velocity (PASIV) probe was developed at the National Severe Storms Laboratory, which is capable of measuring particle counts on scales as small as 50 m. The videosonde observations from the PASIV showed that precipitation particle counts change rapidly between analysis layers, sometimes by as much as a few orders of magnitude (10 2 –10 4 in adjacent layers), and radar reflectivities calculated directly from observed particle size distributions agree with mobile radar measurements. Furthermore, the observations allow the distributions to be partitioned into various particle types, which sheds light on which particles are dominating the radar signal at various times. These distributions per particle type, and their associated mixing ratios, agree with independent model analysis. Additionally, single‐, double‐, and triple‐moment parameterized particle size distribution functions are fit to various layers, with the general result that ice particles are easily represented by exponential distributions while rain is better fit with a gamma distribution. The parametric fits for ice and rain distributions found here support previous work and some portions of modeled microphysics, but also suggest room for improvement when deciding which schemes to use with cloud modeling.