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

The Atmospheric Radiation Measurement (ARM) large-scale forcing data is developed based on the constrained variational analysis (VARANAL) and have been widely used for Single-Column Models (SCMs), Cloud-Resolving Models (CRMs) and Large-Eddy Simulation Models (LESs) to understand and improve physical processes in models. The VARANAL algorithm was originally developed by Zhang and Lin (1997) and Zhang et al. (2001) at the 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. 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. [Read More on ARM ...]

The current large-scale forcing data sets archived by ARM includes two major products:
    1) IOP-based forcing: Radiosonde- or NWP-based forcing data for short-term Intensive Operational Periods (IOPs) at different ARM fixed or mobile sites;
    2) Continuous forcing: the multi-year continuous forcing data at the ARM permanent sites.

All the VARANAL forcing data share the same DOI number: doi:10.5439/1273323. They are available to the community from the ARM Archive (http://www.archive.arm.gov/discovery/). To cite the data, please refer to Zhang and Lin (1997) and Zhang et al. (2001) for the VARANAL algorithm and the sounding-based IOP forcing, Xie et al. (2004) for the NWP-based IOP, and Xie et al. (2004) and Tang et al. (2019) for the continuous forcing data at SGP.

For more details, please refer to the One-page description and Technical Report for VARANAL.If you have any questions regarding this data product, please contact Cheng Tao (tao4@llnl.gov) or Shuaiqi Tang (tang32@llnl.gov).