/**
\page GFS_SPP Stochastically-Perturbed Parameterizations (SPP)

\section des_spp Description

For both regional and global ensembles, sufficient initial condition and 
physics uncertainty representation is critically important to ensemble 
performance.  Otherwise, members may tend toward similar solutions, 
resulting in forecasts not capturing the full envelope of possible 
outcomes.  To improve the effectiveness of the ensemble, stochastic 
physics can be employed.  Stochastically-Perturbed Parameterizations 
(SPP) provides one method through which unknown or uncertain physical 
processes can be represented in ensemble forecasting. As it is currently 
employed in CCPP (Beck et al.(2022) \cite beck_et_al_2022), SPP can be applied to the following physics parameterizations: 
\ref MYNNEDMF, \ref SFC_MYNNSFL, \ref GFS_RRTMG, \ref GFS_UNIFIED_UGWP,\ref GFS_drag_suite and 
\ref THOMPSON (note that both \ref RUCLSM and \ref GFS_NOAH can also be 
run with stochastic parameter perturbations).  A list of the parameters perturbed 
within each scheme can be found below: 

- \ref MYNNEDMF
 - Eddy diffusivity
 - Viscosity
 - Background water vapor
 - Lateral entrainment rate
- \ref SFC_MYNNSFL
 - Thermal roughness length
 - Moisture roughness length
 - Aerodynamic roughness length
- \ref GFS_UNIFIED_UGWP and \ref GFS_drag_suite
 - Subgrid-scale terrain variations
 - Froude number
 - Wind speed tendencies
- \ref GFS_RRTMG
 - Effective cloud water, snow, and ice radii
- \ref THOMPSON
 - Graupel intercept parameter
 - Cloud droplet shape parameter
 - Vertical velocity used in CCN activation
 - Ice number concentration

Magnitudes, time decorrelation lengths, and other namelist settings can be modified to control the perturbations for each scheme; however, for now, 
all parameters are perturbed identically within each scheme. For detailed information on how to activate and modify SPP settings for 
these schemes, the reader is referred to <a href="https://ufs-srweather-app.readthedocs.io/en/latest/index.html">the SRW App Users Guide</a>.

Each parameter listed above was chosen through consultation with 
physics experts, often the author of the parameterization, in order 
to target fields that could benefit from the application of SPP. However, there are many other parameters which could be effectively 
perturbed to improve uncertainty representation and ensemble spread generation. 
 In most of the perturbed parameterizations, the perturbation magnitudes 
are multiplied by coefficients to generate reasonable parameter values; 
however, users should be careful when making changes to the magnitudes 
provided as a guideline in the SRW App documentation, since it is still 
possible to generate unphysical parameter values.  Default magnitudes 
and decorrelation lengths in the SRW App documentation were the result 
of extensive testing with 3-km CONUS simulations. If the user would 
like to apply SPP at other resolutions, it is recommended that retrospective 
simulations be conducted over the area of interest to ensure reasonable 
application of the perturbations. 

## GFS Surface Parameter Perturbation

Land surface perturbation (Gehne et al. (2019) \cite Gehne_2019) has been recently introduced
into UFS. This treatment is based on the hypothesis that one of the major
causes of the insufficient spread in current global NWP model,especially near the surface, is a lack of treatment of
uncertainty in the soil state and in the associated model parameters. It allows for land surface parameters such as surface albedo,
vegetation fraction, soil hydraulic conductivity, leaf area index (LAI), surface roughness lengths for heat and momentom to vary in space.
These parameters and variables have been shown to impact forecasts of 2m temperature, 10m wind and precipitation. Based on the parameter
or variable,different strategies to perturb are necessary.

*/