three-dimensional constrained variational analysis

Tang, Shuaiqi; Xie, Shaocheng

doi:10.5439/1491785

Title: three-dimensional constrained variational analysis

Dataset
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Abstract

This is a dataset from three-dimensional constrained variational analysis (3DCVA). It can be used to generate large-scale forcing data for SCM/CRM/LES, or evaluate model results.

Authors:

Tang, Shuaiqi; Xie, Shaocheng

ORNL

Publication Date:: Fri May 31 04:00:00 UTC 2019

DOE Contract Number:: AC05-00OR22725

Research Org.:: Atmospheric Radiation Measurement (ARM) Archive, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US); ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

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

Collaborations:: PNNL, BNL, ANL, ORNL

Subject:: 54 ENVIRONMENTAL SCIENCES; constrained variational analysis; large-scale forcing; single-column model

OSTI Identifier:: 1491785

DOI:: https://doi.org/10.5439/1491785

Citation Formats


                    Tang, Shuaiqi, and Xie, Shaocheng. three-dimensional constrained variational analysis.  United States: N. p., 2019. 
        Web.  doi:10.5439/1491785.

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                    Tang, Shuaiqi, & Xie, Shaocheng. three-dimensional constrained variational analysis.  United States.  doi:https://doi.org/10.5439/1491785

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                    Tang, Shuaiqi, and Xie, Shaocheng. 2019.  
"three-dimensional constrained variational analysis".  United States.  doi:https://doi.org/10.5439/1491785.  https://www.osti.gov/servlets/purl/1491785. Pub date:Fri May 31 04:00:00 UTC 2019

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@article{osti_1491785,

  title        = {three-dimensional constrained variational analysis},

  author       = {Tang, Shuaiqi and Xie, Shaocheng},

  abstractNote = {This is a dataset from three-dimensional constrained variational analysis (3DCVA). It can be used to generate large-scale forcing data for SCM/CRM/LES, or evaluate model results.},

  doi          = {10.5439/1491785},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri May 31 04:00:00 UTC 2019},

  month        = {Fri May 31 04:00:00 UTC 2019}

}

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DOI: https://doi.org/10.5439/1491785

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