Improved radar ice water content retrieval algorithms using coincident microphysical and radar measurements

AMS Citation:
Heymsfield, A. J., Z. Wang, and S. Matrosov, 2005: Improved radar ice water content retrieval algorithms using coincident microphysical and radar measurements. Journal of Applied Meteorology 1989-2005 (After 2005 - Journal of Applied Meteorology and Climatology), 44, 1391-1412, doi:10.1175/JAM2282.1.
Date:2005-09-01
Resource Type:article
Title:Improved radar ice water content retrieval algorithms using coincident microphysical and radar measurements
Abstract: Airborne radar reflectivity measurements at frequencies of 9.6 and 94 GHz, with collocated, in situ particle size distribution and ice water content measurements from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) in Florida in July 2002, offer one of the first opportunities to evaluate and improve algorithms for retrieving ice water content from single-wavelength spaceborne radar measurements. Both ice water content and radar reflectivity depend on the distribution of particle mass with size. It is demonstrated that single, power-law, mass dimensional relationships are unable to adequately account for the dominating contribution of small particles at lower reflectivities and large particles at higher reflectivities. To circumvent the need for multiple, or complex, mass dimensional relationships, analytic expressions that use particle ensemble mean ice particle densities that are derived from the coincident microphysical and radar observations are developed. These expressions, together with more than 5000 CRYSTAL FACE size distributions, are used to develop radar reflectivity-ice water content relationships for the two radar wavelengths that appear to provide improvements over earlier relationships, at least,for convectively generated stratiform ice clouds.
Peer Review:Refereed
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OpenSky citable URL: ark:/85065/d7t1540g
Publisher's Version: 10.1175/JAM2282.1
Author(s):
  • Andrew Heymsfield - NCAR/UCAR
  • Z. Wang
  • S. Matrosov
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