varanal3d > Three-dimensional Constrained Variational AnalysisVAP Type(s) > Baseline • Evaluation

The three-dimensional large-scale forcing data are developed using the three-dimensional constrained variational analysis (3DCVA) approach (Tang and Zhang 2015). This approach is an extension of the original one-dimensional constrained variational analysis (1DCVA or VARANAL). It extends the original method from one atmospheric column into many subcolumns, within a similar size of domain. The constraint equations are satisfied in each subcolumn, and all subcolumns interact with one another through horizontal fluxes. The 3D structure allows for studies of spatial variation of the large-scale forcing fields and tests of physical parameterizations across scales.

Initially, the VARANAL3D products are available from two field campaigns at ARM’s Southern Great Plains atmospheric observatory: the March 2000 Spring Cloud Intensive Operational Period (Xie et al. 2005; Tang and Zhang 2015; Tang et al. 2016) and the Midlatitude Continental Convective Clouds Experiment (Xie et al. 2014). Ensemble products are available for both field campaigns by using multiple reanalyses/analyses data as background data to characterize data uncertainties.

References: Tang, S, and M Zhang. 2015. “Three-dimensional constrained variational analysis: Approach and application to analysis of atmospheric diabatic heating and derivative fields during an ARM SGP intensive observational period.” Journal of Geophysical Research – Atmospheres, 120(15): 7283–7299,

Tang, S, M Zhang, and S Xie. 2016. “An ensemble constrained variational analysis of atmospheric forcing data and its application to evaluate clouds in CAM5.” Journal of Geophysical Research – Atmospheres, 121(1): 33–48,

Xie, S, M Zhang, M Branson, RT Cederwall, AD Del Genio, ZA Eitzen, et al. 2005. “Simulations of midlatitude frontal clouds by single-column and cloud-resolving models during the Atmospheric Radiation Measurement March 2000 cloud intensive operational period.” Journal of Geophysical Research – Atmospheres, 110(D15): D15S03,

Xie, S, Y Zhang, SE Giangrande, MP Jensen, R McCoy, and M Zhang. 2014. “Interactions between cumulus convection and its environment as revealed by the MC3E sounding array.” Journal of Geophysical Research – Atmospheres, 119(20): 11,784–11,808,


The ensemble 3DCVA helps users identify the uncertainty in the forcing data.  It also reduces the uncertainties due to using specific background data.



  • Fixed
  • AMF1
  • AMF2
  • AMF3

Data Details

Developed By Shaocheng Xie
Contact Maggie Davis
Resource(s) Data Directory
Data format 180varanal3*.netcdf
Content time range 1 March 2000 - 6 June 2011
Attribute accuracy No formal attribute accuracy tests were conducted.
Positional accuracy No formal positional accuracy tests were conducted.
Data Consistency and Completeness Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details.
Access Restriction No access constraints are associated with this data.
Use Restriction No use constraints are associated with this data.


Dorsey KS, R Jundt, CB Ireland, MR Wasem, RA Stafford, and A Hunzinger. 2021. 2020 Atmospheric Radiation Measurement (ARM) Annual Report. Ed. by Kathryn Dorsey, U.S. Department of Energy. DOE/SC-ARM-20-020.


Tang S, S Xie, and M Zhang. 2020. Description of the Three-Dimensional Large-Scale Forcing Data from the 3D Constrained Variational Analysis (VARANAL3D). Ed. by Robert Stafford, ARM user facility. DOE/SC-ARM-TR-253.

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