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Title: SCM-Forcing Data

Abstract

Single-Column Model (SCM) Forcing Data are derived from the ARM facility observational data using the constrained variational analysis approach (Zhang and Lin 1997 and Zhang et al., 2001). The resulting products include both the large-scale forcing terms and the evaluation fields, which can be used for driving the SCMs and Cloud Resolving Models (CRMs) and validating model simulations.

Authors:
; ; ;
Publication Date:
DOE Contract Number:  
DE-AC05-00OR22725
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Archive; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Collaborations:
PNL, BNL,ANL,ORNL
Subject:
54 Environmental Sciences
Keywords:
Forcing Data, Single-Column Model
OSTI Identifier:
1273323
DOI:
https://doi.org/10.5439/1273323

Citation Formats

Xie, Shaocheng, Tang, Shuaiqi, Zhang, Yunyan, and Zhang, Minghua. SCM-Forcing Data. United States: N. p., 2016. Web. doi:10.5439/1273323.
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Xie, Shaocheng, Tang, Shuaiqi, Zhang, Yunyan, & Zhang, Minghua. SCM-Forcing Data. United States. doi:https://doi.org/10.5439/1273323
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Xie, Shaocheng, Tang, Shuaiqi, Zhang, Yunyan, and Zhang, Minghua. 2016. "SCM-Forcing Data". United States. doi:https://doi.org/10.5439/1273323. https://www.osti.gov/servlets/purl/1273323. Pub date:Fri Jul 01 00:00:00 EDT 2016
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@article{osti_1273323,
title = {SCM-Forcing Data},
author = {Xie, Shaocheng and Tang, Shuaiqi and Zhang, Yunyan and Zhang, Minghua},
abstractNote = {Single-Column Model (SCM) Forcing Data are derived from the ARM facility observational data using the constrained variational analysis approach (Zhang and Lin 1997 and Zhang et al., 2001). The resulting products include both the large-scale forcing terms and the evaluation fields, which can be used for driving the SCMs and Cloud Resolving Models (CRMs) and validating model simulations.},
doi = {10.5439/1273323},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {7}
}
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Dataset:
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DOI: https://doi.org/10.5439/1273323
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