Evaluation Product : Large-Scale Forcing Data for SCM/CRM/LES from Constrained Variational Analysis (VARANAL)

[ ARM research - evaluation data product ]

The large-scale forcing data is developed using the constrained variational analysis approach developed by Zhang and Lin (1997) and Zhang et al. (2001). It calculates the time varying vertical profiles of large-scale forcing data for an atmospheric column by adjusting state variables (horizontal wind u, v, water vapor mixing ratio q, dry static energy s) from sounding measurements to satisfy the conservations of mass, moisture, heat, and momentum. To ease the requirement of this method on high-density sounding measurements which are only available during specific intensive operational periods (IOPs), and to reduce errors in the NWP-derived forcing data, Xie et al. (2004) combined RUC analysis with observed constraint variables to obtain long-term forcing data (the ARM Continuous Forcing Product). The derived large-scale forcing data can be used to drive SCM/CRM/LES for different systems over long time periods. Results from these simulations are then used to improve cloud parameterizations in Global Climate Models (GCMs). It also includes diagnostic fields such as heating profiles and cloud fields to evaluate model results.

Currently two types of data are available.
IOP sounding-based forcings: State variables from a sounding array. The forcing is at 3-hr and 25-mb resolutions.
Continuous forcing: State variables from RUC analysis (replaced by RAP since May 2012) (Xie et al., 2004). The forcing is at 1-hr and 25-mb resolutions.

How to cite:
For IOP sounding-based forcings: "The large-scale forcing data (ARM Climate Research Facility, 2001) is derived from a constrained variational analysis approach developed by Zhang and Lin (1997) and Zhang et al. (2001). The data can be obtained at http://www.arm.gov/data/eval/29."
For specific fields campaigns, please also refer to their introductive paper, which can be obtained by clicking the following links and are also listed in the reference section: M-PACE , TWP-ICE, MC3E, GOAmazon
For continuous forcing: "The continuous forcing data (ARM Climate Research Facility, 2001) is derived from the NOAA rapid update cycle (RUC) (or rapid refresh [RAP]) analysis data constrained with the ARM surface and TOA measurements (Xie et al., 2004) using the constrained variational analysis approach developed by Zhang and Lin (1997) and Zhang et al. (2001). The data can be obtained at http://www.arm.gov/data/eval/29."

Purpose

The derived large-scale forcing data can be used to drive SCM/CRM/LES for different systems over long time periods. Results from these simulations are then used to improve cloud parameterizations in Global Climate Models (GCMs). It also includes diagnostic fields such as heating profiles and cloud fields to evaluate model results.

Data Details

Developed by Shaocheng Xie
Contact
Shuaiqi Tang
tang32@llnl.gov
(925) 422-6023
Livermore, CA  94550
Resource(s) Data Directory, ReadMe
Data Format netcdf, ascii
Site Information GAN  Gan Island, Maldives; Mobile Facility (AMIE-GAN)
HFE  Shouxian, Anhui, China; Mobile Facility
MAO  Manacapuru, Amazonas, Brazil; Mobile Facility (GOAMAZON)
NSA  North Slope Alaska
SGP  Southern Great Plains
TWP  Tropical Western Pacific
Content Time Range 1999.01.01 — 2015.08.01
Scientific Measurements
Measurement Variables
Vertical velocity

advective tendencies

Q1

Q2

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.
Citations Zhang, M., and J. Lin (1997), Constrained Variational Analysis of Sounding Data Based on Column-Integrated Budgets of Mass, Heat, Moisture, and Momentum: Approach and Application to ARM Measurements, Journal of the Atmospheric Sciences, 54(11), 1503-1524, doi: 10.1175/1520-0469(1997)054<1503:CVAOSD>2.0.CO;2.

Zhang, M., J. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie (2001), Objective Analysis of ARM IOP Data: Method and Sensitivity, Monthly Weather Review, 129(2), 295-311, doi: 10.1175/1520-0493(2001)129<0295:OAOAID>2.0.CO;2.

Xie, S., R. T. Cederwall, and M. Zhang (2004), Developing long-term single-column model/cloud system???resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations, Journal of Geophysical Research, 109(D1), doi: 10.1029/2003jd004045.

Xie, S., S. A. Klein, M. Zhang, J. J. Yio, R. T. Cederwall, and R. McCoy (2006), Developing large-scale forcing data for single-column and cloud-resolving models from the Mixed-Phase Arctic Cloud Experiment, Journal of Geophysical Research, 111(D19), doi: 10.1029/2005jd006950.

Xie, S., T. Hume, C. Jakob, S. A. Klein, R. B. McCoy, and M. Zhang (2010), Observed Large-Scale Structures and Diabatic Heating and Drying Profiles during TWP-ICE, Journal of Climate, 23(1), 57-79, doi: 10.1175/2009jcli3071.1.

Xie, S., Y. Zhang, S. E. Giangrande, M. P. 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, 2014JD022011, doi: 10.1002/2014JD022011.

Tang, S., Xie, S., Zhang, Y., Zhang, M., Schumacher, C., Upton, H., Jensen, M. P., Johnson, K. L., Wang, M., Ahlgrimm, M., Feng, Z., Minnis, P., and Thieman, M. (2016), Large-Scale Vertical Velocity, Diabatic Heating and Drying Profiles Associated with Seasonal and Diurnal Variations of Convective Systems Observed in the GoAmazon2014/5 Experiment, Atmospheric Chemistry and Physics Discussions, doi: 10.5194/acp-2016-644. in review.