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Title: Primary Modes of Global Drop Size Distributions

Abstract

Understanding drop size distribution (DSD) variability has important implications for remote sensing and numerical modeling applications. Twelve disdrometer datasets across three latitude bands are analyzed in this study, spanning a broad range of precipitation regimes: light rain, orographic, deep convective, organized midlatitude, and tropical oceanic. Principal component analysis (PCA) is used to reveal comprehensive modes of global DSD spatial and temporal variability. Although the locations contain different distributions of individual DSD parameters, all locations are found to have the same modes of variability. Based on PCA, six groups of points with unique DSD characteristics emerge. The physical processes that underpin these groups are revealed through supporting radar observations. Group 1 (group 2) is characterized by high (low) liquid water content (LWC), broad (narrow) distribution widths, and large (small) median drop diameters D 0 . Radar analysis identifies group 1 (group 2) as convective (stratiform) rainfall. Group 3 is characterized by weak, shallow radar echoes and large concentrations of small drops, indicative of warm rain showers. Group 4 identifies heavy stratiform precipitation. The low latitudes exhibit distinct bimodal distributions of the normalized intercept parameter N w , LWC, and D 0 and are found to have a clustering of points (groupmore » 5) with high rain rates, large N w , and moderate D 0 , a signature of robust warm rain processes. A distinct group associated with ice-based convection (group 6) emerges in the midlatitudes. Although all locations exhibit the same covariance of parameters associated with these groups, it is likely that the physical processes responsible for shaping the DSDs vary as a function of location.« less

Authors:
 [1];  [1];  [1];  [1];  [2]
  1. Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado
  2. Applied Physics Laboratory, University of Washington, Seattle, Washington
Publication Date:
Research Org.:
Colorado State Univ., Fort Collins, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1434832
Alternate Identifier(s):
OSTI ID: 1541826
Grant/Contract Number:  
SC0014371
Resource Type:
Journal Article: Published Article
Journal Name:
Journal of the Atmospheric Sciences
Additional Journal Information:
Journal Name: Journal of the Atmospheric Sciences Journal Volume: 75 Journal Issue: 5; Journal ID: ISSN 0022-4928
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences

Citation Formats

Dolan, B., Fuchs, B., Rutledge, S. A., Barnes, E. A., and Thompson, E. J. Primary Modes of Global Drop Size Distributions. United States: N. p., 2018. Web. doi:10.1175/JAS-D-17-0242.1.
Dolan, B., Fuchs, B., Rutledge, S. A., Barnes, E. A., & Thompson, E. J. Primary Modes of Global Drop Size Distributions. United States. doi:10.1175/JAS-D-17-0242.1.
Dolan, B., Fuchs, B., Rutledge, S. A., Barnes, E. A., and Thompson, E. J. Tue . "Primary Modes of Global Drop Size Distributions". United States. doi:10.1175/JAS-D-17-0242.1.
@article{osti_1434832,
title = {Primary Modes of Global Drop Size Distributions},
author = {Dolan, B. and Fuchs, B. and Rutledge, S. A. and Barnes, E. A. and Thompson, E. J.},
abstractNote = {Understanding drop size distribution (DSD) variability has important implications for remote sensing and numerical modeling applications. Twelve disdrometer datasets across three latitude bands are analyzed in this study, spanning a broad range of precipitation regimes: light rain, orographic, deep convective, organized midlatitude, and tropical oceanic. Principal component analysis (PCA) is used to reveal comprehensive modes of global DSD spatial and temporal variability. Although the locations contain different distributions of individual DSD parameters, all locations are found to have the same modes of variability. Based on PCA, six groups of points with unique DSD characteristics emerge. The physical processes that underpin these groups are revealed through supporting radar observations. Group 1 (group 2) is characterized by high (low) liquid water content (LWC), broad (narrow) distribution widths, and large (small) median drop diameters D 0 . Radar analysis identifies group 1 (group 2) as convective (stratiform) rainfall. Group 3 is characterized by weak, shallow radar echoes and large concentrations of small drops, indicative of warm rain showers. Group 4 identifies heavy stratiform precipitation. The low latitudes exhibit distinct bimodal distributions of the normalized intercept parameter N w , LWC, and D 0 and are found to have a clustering of points (group 5) with high rain rates, large N w , and moderate D 0 , a signature of robust warm rain processes. A distinct group associated with ice-based convection (group 6) emerges in the midlatitudes. Although all locations exhibit the same covariance of parameters associated with these groups, it is likely that the physical processes responsible for shaping the DSDs vary as a function of location.},
doi = {10.1175/JAS-D-17-0242.1},
journal = {Journal of the Atmospheric Sciences},
issn = {0022-4928},
number = 5,
volume = 75,
place = {United States},
year = {2018},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1175/JAS-D-17-0242.1

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Cited by: 7 works
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Works referencing / citing this record:

Rainfall Estimation From Ground Radar and TRMM Precipitation Radar Using Hybrid Deep Neural Networks
journal, September 2019

  • Chen, Haonan; Chandrasekar, V.; Tan, Haiming
  • Geophysical Research Letters, Vol. 46, Issue 17-18
  • DOI: 10.1029/2019gl084771

The Green Ocean: precipitation insights from the GoAmazon2014/5 experiment
journal, January 2018

  • Wang, Die; Giangrande, Scott E.; Bartholomew, Mary Jane
  • Atmospheric Chemistry and Physics, Vol. 18, Issue 12
  • DOI: 10.5194/acp-18-9121-2018

Rainfall Estimation From Ground Radar and TRMM Precipitation Radar Using Hybrid Deep Neural Networks
journal, September 2019

  • Chen, Haonan; Chandrasekar, V.; Tan, Haiming
  • Geophysical Research Letters, Vol. 46, Issue 17-18
  • DOI: 10.1029/2019gl084771

The Green Ocean: precipitation insights from the GoAmazon2014/5 experiment
journal, January 2018

  • Wang, Die; Giangrande, Scott E.; Bartholomew, Mary Jane
  • Atmospheric Chemistry and Physics, Vol. 18, Issue 12
  • DOI: 10.5194/acp-18-9121-2018