Search Results

You are looking at 11 - 12 of 12 items for

  • Author or Editor: S. Joseph Munchak x
  • All content x
Clear All Modify Search
Christopher R. Williams, V. N. Bringi, Lawrence D. Carey, V. Chandrasekar, Patrick N. Gatlin, Ziad S. Haddad, Robert Meneghini, S. Joseph Munchak, Stephen W. Nesbitt, Walter A. Petersen, Simone Tanelli, Ali Tokay, Anna Wilson, and David B. Wolff
Full access
Christopher R. Williams, V. N. Bringi, Lawrence D. Carey, V. Chandrasekar, Patrick N. Gatlin, Ziad S. Haddad, Robert Meneghini, S. Joseph Munchak, Stephen W. Nesbitt, Walter A. Petersen, Simone Tanelli, Ali Tokay, Anna Wilson, and David B. Wolff

Abstract

Rainfall retrieval algorithms often assume a gamma-shaped raindrop size distribution (DSD) with three mathematical parameters N w, D m, and μ. If only two independent measurements are available, as with the dual-frequency precipitation radar on the Global Precipitation Measurement (GPM) mission core satellite, then retrieval algorithms are underconstrained and require assumptions about DSD parameters. To reduce the number of free parameters, algorithms can assume that μ is either a constant or a function of D m. Previous studies have suggested μ–Λ constraints [where Λ = (4 + μ)/D m], but controversies exist over whether μ–Λ constraints result from physical processes or mathematical artifacts due to high correlations between gamma DSD parameters. This study avoids mathematical artifacts by developing joint probability distribution functions (joint PDFs) of statistically independent DSD attributes derived from the raindrop mass spectrum. These joint PDFs are then mapped into gamma-shaped DSD parameter joint PDFs that can be used in probabilistic rainfall retrieval algorithms as proposed for the GPM satellite program. Surface disdrometer data show a high correlation coefficient between the mass spectrum mean diameter D m and mass spectrum standard deviation σ m. To remove correlations between DSD attributes, a normalized mass spectrum standard deviation is constructed to be statistically independent of D m, with representing the most likely value and std representing its dispersion. Joint PDFs of D m and μ are created from D m and . A simple algorithm shows that rain-rate estimates had smaller biases when assuming the DSD breadth of than when assuming a constant μ.

Full access