Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales

Liang, Xin-Zhong

doi:10.2172/1133982

Title: Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales

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Abstract

This is the final report for the DOE funded project.

Authors:: Liang, Xin-Zhong

Publication Date:: Tue Oct 01 00:00:00 EDT 2013

Research Org.:: University of Maryland, College Park

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

OSTI Identifier:: 1133982

Report Number(s):: DOE-UMD-7169

DOE Contract Number:: SC0007169

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Subject:: 58 GEOSCIENCES; cloud, aerosol, radiation, climate, model, uncertainty

Citation Formats


                    Liang, Xin-Zhong. Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales.  United States: N. p., 2013. 
        Web.  doi:10.2172/1133982.

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                    Liang, Xin-Zhong. Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales.  United States.  https://doi.org/10.2172/1133982

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                    Liang, Xin-Zhong. 2013.  
        "Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales".  United States.  https://doi.org/10.2172/1133982.  https://www.osti.gov/servlets/purl/1133982.

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@article{osti_1133982,

  title        = {Optimizing the Cloud-Aerosol-Radiation Ensemble Modeling System to Improve Future Climate Change Projections at Regional to Local Scales},

  author       = {Liang, Xin-Zhong},

  abstractNote = {This is the final report for the DOE funded project.},

  doi          = {10.2172/1133982},

  url          = {https://www.osti.gov/biblio/1133982},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Oct 01 00:00:00 EDT 2013},

  month        = {Tue Oct 01 00:00:00 EDT 2013}

}

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