Microwave properties of ice-phase hydrometeors for radar and radiometers: Sensitivity to model assumptions

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Abstract

A simplified framework is presented for assessing the qualitative sensitivities of computed microwave properties, satellite brightness temperatures, and radar reflectivities to assumptions concerning the physical properties of ice-phase hydrometeors. Properties considered included the shape parameter m of a gamma size distribution and the melted-equivalent mass median diameter D0, the particle density, the dielectric mixing formula, and the choice of complex index of refraction for ice. These properties are examined for selected radiometer frequencies of 18.7, 36.5, 89.0, and 150.0 GHz and radar frequencies at 2.8, 13.4, 35.6, and 94.0 GHz-consistent with existing and planned remote sensing instruments. Passive and active microwave observables of ice particles are found to be extremely sensitive to the D0 of the size distribution. Similar large sensitivities are found for variations in the ice volume fraction whenever the geometric mass median diameter exceeds approximately 1/ 8th of the wavelength. At 94 GHz the two-way path-integrated attenuation is potentially large for dense/compact particles. The distribution parameter m has a comparatively weak effect on any observable: less than 1-2 K in brightness temperature and a maximum of 2.7 dB (S band only) in the effective radar reflectivity. Reversal of the roles of ice and air in the Maxwell Garnett dielectric mixing formula leads to a substantial change in both microwave brightness temperature (~10 K) and radar reflectivity (approximately 2 dB across all frequencies). The choice of the complex index of refraction of ice can produce a 3%-4% change in the brightness temperature depression.

Original languageEnglish
Pages (from-to)2152-2171
Number of pages20
JournalJournal of Applied Meteorology and Climatology
Volume51
Issue number12
DOIs
StatePublished - Dec 2012

Keywords

  • Ice particles
  • Microwave observations
  • Radars/Radar observations
  • Remote sensing
  • Snowfall

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