Search Results

You are looking at 21 - 25 of 25 items for

  • Author or Editor: R. Meneghini 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
Toshihisa Matsui, Takamichi Iguchi, Xiaowen Li, Mei Han, Wei-Kuo Tao, Walter Petersen, Tristan L'Ecuyer, Robert Meneghini, William Olson, Christian D. Kummerow, Arthur Y. Hou, Mathew R. Schwaller, Erich F. Stocker, and John Kwiatkowski
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
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
W.-K. Tao, E. A. Smith, R. F. Adler, Z. S. Haddad, A. Y. Hou, T. Iguchi, R. Kakar, T. N. Krishnamurti, C. D. Kummerow, S. Lang, R. Meneghini, K. Nakamura, T. Nakazawa, K. Okamoto, W. S. Olson, S. Satoh, S. Shige, J. Simpson, Y. Takayabu, G. J. Tripoli, and S. Yang

Rainfall is a fundamental process within the Earth's hydrological cycle because it represents a principal forcing term in surface water budgets, while its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating well into the middle latitudes. Latent heat production itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics, as well as modify the energetic efficiencies of midlatitude weather systems.

This paper highlights the retrieval of latent heating from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American–Japanese space endeavor. Since then, TRMM measurements have been providing credible four-dimensional accounts of rainfall over the global Tropics and subtropics, information that can be used to estimate the space–time structure of latent heating across the Earth's low latitudes.

A set of algorithm methodologies for estimating latent heating based on precipitation-rate profile retrievals obtained from TRMM measurements has been under continuous development since the advent of the mission s research program. These algorithms are briefly described, followed by a discussion of the latent heating products that they generate. The paper then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

Full access