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Add the necessary code in ufs-weather-model, fv3atm, ccpp-physics, and stochastic_physics to run the FV3 with Stochastically Perturbed Parameterizations (SPP). The associated code uses the stochastic_physics pattern generator (same as for SKEB, SHUM, and SPPT) to create a random pattern based on scales, magnitudes, etc., from the FV3 input.nml namelist and perturbs RAP/HRRR-based physics scheme parameters in selected parameterizations.
Namelist options:
&gfs_physics_nml
do_spp = .true. or .false. -- flag to turn SPP on/off; .false. is the default
n_var_spp = 1 -- determines the number of RAP/HRRR-based schemes that will be perturbed by SPP (these include MYNN SFC, MYNN PBL, RRTMG, GSL GWD, and Thompson MP)
&nam_sppperts (new stanza in namelist; all settings here are comma separated and apply to the parameterizations listed in 'spp_var_list')
iseed_spp = 4 -- the initial seed value for the perturbation pattern
spp_lscale = 150000.0 -- length scale in meters
spp_prt_list = 0.2 -- perturbation magnitude
spp_sigtop1 = 0.1 -- controls vertical tapering of perturbations at the tropopause
spp_sigtop2 = 0.025 -- controls vertical tapering of perturbations at the tropopause
spp_stddev_cutoff = 2.0 -- perturbation magnitude cutoff in number of standard deviations from the mean
spp_tau = 21600.0 -- time decorrelation length in seconds
spp_var_list = 'gwd' -- the list of parameterizations to perturb (options include 'pbl', 'sfc', 'rad', 'gwd', and 'mp')
Solution
Perturbs selected parameters in RAP/HRRR-based physics parameterizations, identically to how SPP was implemented in WRF (Jankov et al 2017 and Jankov et al 2019). Uses pre-existing SPP code in ccpp-physics from WRF-based parameterizations, but which was disabled during the transition to CCPP. Changes were only made where necessary to implement all parameter perturbations correctly within CCPP.
JeffBeck-NOAA
changed the title
Add support for Stochastically Perturbed Parameterizations (SPP) in the FV3.
Add support for Stochastically Perturbed Parameterizations (SPP) in the FV3
Dec 26, 2021
Description
Add the necessary code in ufs-weather-model, fv3atm, ccpp-physics, and stochastic_physics to run the FV3 with Stochastically Perturbed Parameterizations (SPP). The associated code uses the stochastic_physics pattern generator (same as for SKEB, SHUM, and SPPT) to create a random pattern based on scales, magnitudes, etc., from the FV3 input.nml namelist and perturbs RAP/HRRR-based physics scheme parameters in selected parameterizations.
Namelist options:
&gfs_physics_nml
do_spp = .true. or .false. -- flag to turn SPP on/off; .false. is the default
n_var_spp = 1 -- determines the number of RAP/HRRR-based schemes that will be perturbed by SPP (these include MYNN SFC, MYNN PBL, RRTMG, GSL GWD, and Thompson MP)
&nam_sppperts (new stanza in namelist; all settings here are comma separated and apply to the parameterizations listed in 'spp_var_list')
iseed_spp = 4 -- the initial seed value for the perturbation pattern
spp_lscale = 150000.0 -- length scale in meters
spp_prt_list = 0.2 -- perturbation magnitude
spp_sigtop1 = 0.1 -- controls vertical tapering of perturbations at the tropopause
spp_sigtop2 = 0.025 -- controls vertical tapering of perturbations at the tropopause
spp_stddev_cutoff = 2.0 -- perturbation magnitude cutoff in number of standard deviations from the mean
spp_tau = 21600.0 -- time decorrelation length in seconds
spp_var_list = 'gwd' -- the list of parameterizations to perturb (options include 'pbl', 'sfc', 'rad', 'gwd', and 'mp')
Solution
Perturbs selected parameters in RAP/HRRR-based physics parameterizations, identically to how SPP was implemented in WRF (Jankov et al 2017 and Jankov et al 2019). Uses pre-existing SPP code in ccpp-physics from WRF-based parameterizations, but which was disabled during the transition to CCPP. Changes were only made where necessary to implement all parameter perturbations correctly within CCPP.
Related to
PR #982 and needed by companion PRs in stochastic_physics, ccpp-physics, and fv3atm.
@llpcarson, @jwolff-ncar, @willmayfield, @bluefinweiwei, @michelleharrold, @judithberner, @pjpegion
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