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Title: Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2

Abstract

This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: 1. The method used to develop multi-year continuous forcing data (Xie et al. 2004), 2. The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and 3. Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang etmore » al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products. We also extended the VARANAL algorithm into a three-dimensional constrained variational analysis (3DCVA) (Tang and Zhang 2015) and designed an ensemble framework (Tang et al. 2016a) to address the forcing uncertainty. The 3D large-scale forcing data are released as another datastream named “varanal3d”. Please refer to the ARM varanal3d technical report for more information (https://www.arm.gov/capabilities/vaps/varanal3d).« less

Authors:
 [1];  [1];  [1];  [2]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Stony Brook Univ., NY (United States)
Publication Date:
Research Org.:
DOE Office of Science Atmospheric Radiation Measurement (ARM) Program (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Contributing Org.:
Stony Brook University
OSTI Identifier:
1546995
Report Number(s):
DOE/SC-ARM-TR-222
DOE Contract Number:  
DE-ACO5-7601830
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
Southern Great Plains, Tropical Western Pacific, North Slope of Alaska, variational analysis, cloud-resolving models, single-column models, large-eddy simulation ECOR, EBBR, numerical weather prediction, vertical velocity, advection

Citation Formats

Tang, Shuaiqi, Tao, Cheng, Xie, Shaocheng, and Zhang, Minghua. Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2. United States: N. p., 2019. Web. doi:10.2172/1546995.
Tang, Shuaiqi, Tao, Cheng, Xie, Shaocheng, & Zhang, Minghua. Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2. United States. doi:10.2172/1546995.
Tang, Shuaiqi, Tao, Cheng, Xie, Shaocheng, and Zhang, Minghua. Mon . "Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2". United States. doi:10.2172/1546995. https://www.osti.gov/servlets/purl/1546995.
@article{osti_1546995,
title = {Description of the ARM Large-Scale Forcing Data from the Constrained Variational Analysis (VARANAL) Version 2},
author = {Tang, Shuaiqi and Tao, Cheng and Xie, Shaocheng and Zhang, Minghua},
abstractNote = {This technical report represents an update of the previous technical report written by Zhang et al. (2001a) (available at http://www.arm.gov/publications/tech_reports/arm-tr-005.pdf), which described the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility constrained variational analysis (VARANAL) that is used to develop the large-scale forcing data for driving single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulation models (LES). The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001b) at Stony Brook University and was migrated to the Lawrence Livermore National Laboratory (LLNL) as the ARM operational objective analysis system in May 1999. Since then, the algorithm has been evolved with time along with the availability of new observations and techniques to meet various modeling needs. Major updates include: 1. The method used to develop multi-year continuous forcing data (Xie et al. 2004), 2. The incorporation of eddy correlation flux measurement system (ECOR) turbulent fluxes into the analysis (Tang et al. 2019), and 3. Improvements to the workflow (e.g., implementing part of the code into the ARM Data Integrator [ADI]) to increase efficiency. The ARM large-scale forcing data have been widely used for SCM/CRM/LES to understand and improve physical processes in models. Zhang et al. (2016) has provided a comprehensive review of the SCM concept, early efforts to derive forcing data for SCM studies, efforts of the ARM constrained variational analysis, and previous SCM studies using ARM cases. This technical report focuses on the constrained variational analysis algorithm and the introduction of the ARM VARANAL products. We also extended the VARANAL algorithm into a three-dimensional constrained variational analysis (3DCVA) (Tang and Zhang 2015) and designed an ensemble framework (Tang et al. 2016a) to address the forcing uncertainty. The 3D large-scale forcing data are released as another datastream named “varanal3d”. Please refer to the ARM varanal3d technical report for more information (https://www.arm.gov/capabilities/vaps/varanal3d).},
doi = {10.2172/1546995},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}