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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-160
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-160
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 18 Apr 2019

Submitted as: research article | 18 Apr 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.

Characterization of Shallow Oceanic Precipitation using Profiling and Scanning Radar Observations at the Eastern North Atlantic ARM Observatory

Katia Lamer1, Bernat Puigdomènech Treserras2, Zeen Zhu3, Bradley Isom4, Nitin Bharadwaj4, and Pavlos Kollias3,5 Katia Lamer et al.
  • 1Department of Earth and Atmospheric Science, The City College of New York
  • 2Department of Atmospheric and Oceanic Sciences, McGill University
  • 3School of Marine and Atmospheric Sciences, Stony Brook University
  • 4Atmospheric Measurement and Data Sciences, Pacific Northwest National Laboratory
  • 5Department of Environmental and Climate Sciences, Brookhaven National Laboratory

Abstract. Shallow oceanic precipitation variability is documented using 2nd generation radars located at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic observatory: the Ka-band ARM zenith radar (KAZR2), the Ka-band scanning ARM cloud radar (KaSACR2) and the X-band scanning ARM precipitation radar (XSAPR2). First, the radars and measurement post-processing techniques, including sea clutter removal and calibration against collocated 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 radars differ; that is because KAZR2 is more sensitive to shallow virga and because XSAPR2 suffers from less attenuation that KaSACR2 and as such is best suited to characterize 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 domain of 2500 km2 averaged over similar time periods. However, it was determined that KAZR2 is unsuitable to characterize domain average precipitation rate over shorter periods. But even more fundamentally, these results suggest that observations cannot produce objective domain precipitation estimate and that forward-simulators should be used to guide high temporal-resolution model evaluation studies.

Katia Lamer et al.
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Katia Lamer et al.
Katia Lamer et al.
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Latest update: 19 Aug 2019
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Short summary
This article describes the three 2nd generation radars newly deployed by the Atmospheric Radiation Measurement program. Techniques to retrieve precipitation rate from their measurements are presented; they involve noise and clutter filtering, gas and liquid attenuation correction and radar reflectivity calibration. Precipitation rate for a 2500 km2 domain around Graciosa estimated from all 3 radars differ, which highlights the need to consider sensors capabilities when interpreting measurements.
This article describes the three 2nd generation radars newly deployed by the Atmospheric...
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