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Title: Characterization of shallow oceanic precipitation using profiling and scanning radar observations at the Eastern North Atlantic ARM observatory

Journal Article · · Atmospheric Measurement Techniques (Online)
ORCiD logo [1];  [2];  [3];  [4];  [4]; ORCiD logo [5]
  1. City College of New York, NY (United States)
  2. McGill Univ., Montreal, QC (Canada)
  3. Stony Brook Univ., NY (United States)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  5. Stony Brook Univ., NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
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Shallow oceanic precipitation variability is documented using three second-generation radar systems located at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic observatory: ARM zenith radar (KAZR2), the Ka-band scanning ARM cloud radar (KaSACR2) and the X-band scanning ARM precipitation radar (XSAPR2). First, the radar systems and measurement post-processing techniques, including sea-clutter removal and calibration against colocated disdrometer and Global Precipitation Mission (GPM) observations are described. Then, we present how a combination of profiling radar and lidar observations can be used to estimate adaptive (in both time and height) parameters that relate radar reflectivity (Z) to precipitation rate (R) in the form Z=αRβ, which we use to estimate precipitation rate over the domain observed by XSAPR2. Furthermore, constant altitude plan position indicator (CAPPI) gridded XSAPR2 precipitation rate maps are also constructed. Hourly precipitation rate statistics estimated from the three radar systems differ because KAZR2 is more sensitive to shallow virga and XSAPR2 suffers from less attenuation than KaSACR2 and as such is best suited for characterizing intermittent and mesoscale-organized precipitation. Further analysis reveals that precipitation rate statistics obtained by averaging 12h of KAZR2 observations can be used to approximate that of a 40 km radius domain averaged over similar time periods. However, it was determined that KAZR2 is unsuitable for characterizing domain-averaged precipitation rate over shorter periods. But even more fundamentally, these results suggest that these observations cannot produce an objective domain precipitation estimate and that the simultaneous use of forward simulators is desirable to guide model evaluation studies.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
SC0012704; AC05-76RL01830
OSTI ID:
1569543
Alternate ID(s):
OSTI ID: 1576782
Report Number(s):
BNL-212183-2019-JAAM; PNNL-SA-147307
Journal Information:
Atmospheric Measurement Techniques (Online), Vol. 12, Issue 9; ISSN 1867-8548
Publisher:
European Geosciences UnionCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

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Cited By (1)

Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars journal January 2020

Figures / Tables (11)

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