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

Abstract

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=αR^β, 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 12 h of KAZR2 observations can be used to approximate that of a 40 km radius domain averaged over similar timemore » 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.« less

Authors:

Lamer, Katia  ^[1];

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Puigdomènech Treserras, Bernat ^[2]; Zhu, Zeen ^[3]; Isom, Bradley ^[4]; Bharadwaj, Nitin ^[4]; Kollias, Pavlos  ^[5]

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+ Show Author Affiliations

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)

Publication Date:

2019-09-11

Research Org.:

Brookhaven National Lab. (BNL), Upton, NY (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

OSTI Identifier:

1569543

Alternate Identifier(s):

OSTI ID: 1576782

Report Number(s):

BNL-212183-2019-JAAM; PNNL-SA-147307
Journal ID: ISSN 1867-8548

Grant/Contract Number:  

SC0012704; AC05-76RL01830

Resource Type:

Accepted Manuscript

Journal Name:

Atmospheric Measurement Techniques (Online)

Additional Journal Information:

Journal Name: Atmospheric Measurement Techniques (Online); Journal Volume: 12; Journal Issue: 9; Journal ID: ISSN 1867-8548

Publisher:

European Geosciences Union

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES