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Observation-Based Cloud Radiative Kernels from A-TrainWe develop a method to empirically derive broadband and spectral cloud radiative kernels by cloud type from pixel-scale collocated A-Train observations and reanalysis, which does not require additional cloudy radiative transfer calculations nor cloud properties. This method is able to estimate the cloud feedback by maintaining the consistency between CRKs and cloud responses.
Document ID
20190033547
Acquisition Source
Jet Propulsion Laboratory
Document Type
Presentation
External Source(s)
Authors
Yue, Qing ORCID
(Jet Propulsion Laboratory (JPL), California Institute of Technology (CalTech) Pasadena, CA, United States)
Kahn, Brian ORCID
(Jet Propulsion Laboratory (JPL), California Institute of Technology (CalTech) Pasadena, CA, United States)
Fetzer, Eric
(Jet Propulsion Laboratory (JPL), California Institute of Technology (CalTech) Pasadena, CA, United States)
Schreier, Mathias
(Jet Propulsion Laboratory (JPL), California Institute of Technology (CalTech) Pasadena, CA, United States)
Wong, Sun
(Jet Propulsion Laboratory (JPL), California Institute of Technology (CalTech) Pasadena, CA, United States)
Chen, Xiuhong
(Michigan Univ. (HQ) Ann Arbor, MI, United States)
Huang, Xianglei ORCID
(Michigan Univ. (HQ) Ann Arbor, MI, United States)
Zelinka, Mark ORCID
(Lawrence Livermore National Lab. Livermore, CA, United States)
Date Acquired
December 12, 2019
Publication Date
April 26, 2016
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
JPL-CL-16-1857
Report Number: JPL-CL-16-1857
Meeting Information
Meeting: CERES-II Science Team Meeting
Location: Hampton, VA
Country: United States
Start Date: April 26, 2016
End Date: April 28, 2016
Sponsors: NASA Langley Research Center
Distribution Limits
Public
Copyright
Other

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