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Preprints
https://doi.org/10.5194/acp-2020-849
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-849
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Aug 2020

19 Aug 2020

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This preprint is currently under review for the journal ACP.

Joint Cloud Water Path and Rain Water Path Retrievals from ORACLES Observations

Andrew M. Dzambo1,2, Tristan L'Ecuyer1, Kenneth Sinclair3,4, Bastiaan van Diedenhoven5, Siddhant Gupta2, Greg McFarquhar2, Joseph R. O'Brien6, Brian Cairns3, Andrzej P. Wasilewski3,7, and Mikhail Alexandrov3,5 Andrew M. Dzambo et al.
  • 1University of Wisconsin – Madison, Madison, WI, USA
  • 2University of Oklahoma – Cooperative Institute for Meteorological Mesoscale Studies (CIMMS), Norman, OK, USA
  • 3NASA Goddard Institute for Space Studies, New York
  • 4Universities Space Research Association (USRA), Columbia, MD 21046, USA
  • 5Columbia University, New York
  • 6University of North Dakota, Grand Forks, ND 58202, USA
  • 7SciSpace LLC

Abstract. This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar-3rd generation (APR-3), passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ~ 10–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %.

Two thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP) with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e. rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in-situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud-precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

Andrew M. Dzambo et al.

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Andrew M. Dzambo et al.

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Short summary
This work highlights a new algorithm using data collected from the 2016–2018 NASA ORACLES field campaign. This algorithm synthesizes cloud and rain measurements to attain estimates of cloud and precipitation properties over the southeast Atlantic Ocean. Estimates produced by this algorithm compare well against in-situ estimates. increased rain fractions and rain rates are found in regions of atmospheric instability. This dataset can be used to explore aerosol–cloud–precipitation interactions.
This work highlights a new algorithm using data collected from the 2016–2018 NASA ORACLES field...
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