On the vertical structure of modeled and observed deep convective storms: Insights for precipitation retrieval and microphysical parameterization

Jamie L. Smedsmo, Efi Foufoula-Georgiou, Venugopal Vuruputur, Fanyou Kong, Kelvin Droegemeier

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

An understanding of the vertical structure of clouds is important for remote sensing of precipitation from space and for the parameterization of cloud microphysics in numerical weather prediction (NWP) models. The representation of cloud hydrometeor profiles in high-resolution NWP models has direct applications in inversion-type precipitation retrieval algorithms [e.g., the Goddard profiling (GPROF) algorithm used for retrieval of precipitation from passive microwave sensors] and in quantitative precipitation forecasting. This study seeks to understand how the vertical structure of hydrometeors (liquid and frozen water droplets in a cloud) produced by high-resolution NWP models with explicit microphysics, henceforth referred to as cloud-resolving models (CRMs), compares to observations. Although direct observations of 3D hydrometeor fields are not available, comparisons of modeled and observed radar echoes can provide some insight into the vertical structure of hydrometeors and, in turn, into the ability of CRMs to produce precipitation structures that are consistent with observations. Significant differences are revealed between the vertical structure of observed and modeled clouds of a severe midlatitude storm over Texas for which the surface precipitation is reasonably well captured. Possible reasons for this discrepancy are presented, and the need for future research is highlighted.

Original languageEnglish (US)
Pages (from-to)1866-1884
Number of pages19
JournalJournal of Applied Meteorology
Volume44
Issue number12
DOIs
StatePublished - Dec 2005

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