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  • Author or Editor: Liang Liao x
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Zhi Li
,
Yixin Wen
,
Liang Liao
,
David Wolff
,
Robert Meneghini
, and
Terry Schuur

Abstract

The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA’s WSR-88D network and NASA–JAXA’s spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed.

Open access
C. P. Weaver
,
X.-Z. Liang
,
J. Zhu
,
P. J. Adams
,
P. Amar
,
J. Avise
,
M. Caughey
,
J. Chen
,
R. C. Cohen
,
E. Cooter
,
J. P. Dawson
,
R. Gilliam
,
A. Gilliland
,
A. H. Goldstein
,
A. Grambsch
,
D. Grano
,
A. Guenther
,
W. I. Gustafson
,
R. A. Harley
,
S. He
,
B. Hemming
,
C. Hogrefe
,
H.-C. Huang
,
S. W. Hunt
,
D.J. Jacob
,
P. L. Kinney
,
K. Kunkel
,
J.-F. Lamarque
,
B. Lamb
,
N. K. Larkin
,
L. R. Leung
,
K.-J. Liao
,
J.-T. Lin
,
B. H. Lynn
,
K. Manomaiphiboon
,
C. Mass
,
D. McKenzie
,
L. J. Mickley
,
S. M. O'neill
,
C. Nolte
,
S. N. Pandis
,
P. N. Racherla
,
C. Rosenzweig
,
A. G. Russell
,
E. Salathé
,
A. L. Steiner
,
E. Tagaris
,
Z. Tao
,
S. Tonse
,
C. Wiedinmyer
,
A. Williams
,
D. A. Winner
,
J.-H. Woo
,
S. WU
, and
D. J. Wuebbles

This paper provides a synthesis of results that have emerged from recent modeling studies of the potential sensitivity of U.S. regional ozone (O3) concentrations to global climate change (ca. 2050). This research has been carried out under the auspices of an ongoing U.S. Environmental Protection Agency (EPA) assessment effort to increase scientific understanding of the multiple complex interactions among climate, emissions, atmospheric chemistry, and air quality. The ultimate goal is to enhance the ability of air quality managers to consider global change in their decisions through improved characterization of the potential effects of global change on air quality, including O3 The results discussed here are interim, representing the first phase of the EPA assessment. The aim in this first phase was to consider the effects of climate change alone on air quality, without accompanying changes in anthropogenic emissions of precursor pollutants. Across all of the modeling experiments carried out by the different groups, simulated global climate change causes increases of a few to several parts per billion (ppb) in summertime mean maximum daily 8-h average O3 concentrations over substantial regions of the country. The different modeling experiments in general do not, however, simulate the same regional patterns of change. These differences seem to result largely from variations in the simulated patterns of changes in key meteorological drivers, such as temperature and surface insolation. How isoprene nitrate chemistry is represented in the different modeling systems is an additional critical factor in the simulated O3 response to climate change.

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