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Merge #1351
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1351: Remove MD Entrainment hard coded settings r=costachris a=costachris

Remove and clean up hard-coded dimensional scales in MD entrainment closure.

Co-authored-by: costachris <christopouloscosta@gmail.com>
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@bors @costachris
bors[bot] and costachris authored Oct 26, 2023
2 parents d51fc6f + 15fd70c commit add5c50
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Showing 4 changed files with 113 additions and 129 deletions.
2 changes: 1 addition & 1 deletion .buildkite/pipeline.yml
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Expand Up @@ -180,7 +180,7 @@ steps:
steps:

- label: ":partly_sunny: Bomex on sphere"
command: "julia --color=yes --project=integration_tests integration_tests/driver.jl --case Bomex --config sphere --set_src_seed true --skip_tests true --suffix _sphere"
command: "julia --color=yes --project=integration_tests integration_tests/driver.jl --case Bomex --config sphere --set_src_seed true --dt_max 8.0 --skip_tests true --suffix _sphere"
artifact_paths: "Output.Bomex.01_sphere/stats/comparison/*"

- group: "Config testing"
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204 changes: 102 additions & 102 deletions post_processing/mse_tables.jl
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Expand Up @@ -5,35 +5,35 @@
all_best_mse = OrderedCollections.OrderedDict()
#
all_best_mse["ARM_SGP"] = OrderedCollections.OrderedDict()
all_best_mse["ARM_SGP"]["qt_mean"] = 0.2553186789471826
all_best_mse["ARM_SGP"]["updraft_area"] = 327.363026092805
all_best_mse["ARM_SGP"]["updraft_w"] = 154.39080457756052
all_best_mse["ARM_SGP"]["updraft_qt"] = 30.495217840790417
all_best_mse["ARM_SGP"]["updraft_thetal"] = 172.0122203509946
all_best_mse["ARM_SGP"]["qt_mean"] = 0.2554721817619923
all_best_mse["ARM_SGP"]["updraft_area"] = 327.3474673266643
all_best_mse["ARM_SGP"]["updraft_w"] = 154.35344399818703
all_best_mse["ARM_SGP"]["updraft_qt"] = 30.150851900997715
all_best_mse["ARM_SGP"]["updraft_thetal"] = 172.01178075159103
all_best_mse["ARM_SGP"]["u_mean"] = 1.3235797273549681e-5
all_best_mse["ARM_SGP"]["tke_mean"] = 1104.9552327187212
all_best_mse["ARM_SGP"]["temperature_mean"] = 0.00012756767381880596
all_best_mse["ARM_SGP"]["ql_mean"] = 212.16983101753948
all_best_mse["ARM_SGP"]["tke_mean"] = 1104.6622152226375
all_best_mse["ARM_SGP"]["temperature_mean"] = 0.00012761739499360915
all_best_mse["ARM_SGP"]["ql_mean"] = 212.28215041954877
all_best_mse["ARM_SGP"]["qi_mean"] = "NA"
all_best_mse["ARM_SGP"]["thetal_mean"] = 0.00011935034851130479
all_best_mse["ARM_SGP"]["Hvar_mean"] = 1942.2737057522902
all_best_mse["ARM_SGP"]["QTvar_mean"] = 1143.7711037862375
all_best_mse["ARM_SGP"]["thetal_mean"] = 0.00011940508710793993
all_best_mse["ARM_SGP"]["Hvar_mean"] = 1449.5024867351578
all_best_mse["ARM_SGP"]["QTvar_mean"] = 992.5448118267991
#
all_best_mse["Bomex"] = OrderedCollections.OrderedDict()
all_best_mse["Bomex"]["qt_mean"] = 0.09811104477070465
all_best_mse["Bomex"]["updraft_area"] = 127.52646116888195
all_best_mse["Bomex"]["updraft_w"] = 17.480564668884448
all_best_mse["Bomex"]["updraft_qt"] = 7.102456350531833
all_best_mse["Bomex"]["updraft_thetal"] = 69.79451791946437
all_best_mse["Bomex"]["v_mean"] = 64.97390154544397
all_best_mse["Bomex"]["u_mean"] = 0.2659632443361422
all_best_mse["Bomex"]["tke_mean"] = 70.44980303343306
all_best_mse["Bomex"]["temperature_mean"] = 3.9508287833912895e-5
all_best_mse["Bomex"]["ql_mean"] = 8.197428845893032
all_best_mse["Bomex"]["qt_mean"] = 0.09745746285642798
all_best_mse["Bomex"]["updraft_area"] = 127.53035805846918
all_best_mse["Bomex"]["updraft_w"] = 17.60768883839848
all_best_mse["Bomex"]["updraft_qt"] = 6.88050204822434
all_best_mse["Bomex"]["updraft_thetal"] = 69.79162133589787
all_best_mse["Bomex"]["v_mean"] = 64.95579818778579
all_best_mse["Bomex"]["u_mean"] = 0.2658131989475454
all_best_mse["Bomex"]["tke_mean"] = 70.51114148771046
all_best_mse["Bomex"]["temperature_mean"] = 3.9300658049064214e-5
all_best_mse["Bomex"]["ql_mean"] = 8.256816467784288
all_best_mse["Bomex"]["qi_mean"] = "NA"
all_best_mse["Bomex"]["thetal_mean"] = 4.010826899041727e-5
all_best_mse["Bomex"]["Hvar_mean"] = 3159.7800594011524
all_best_mse["Bomex"]["QTvar_mean"] = 1124.2526774816963
all_best_mse["Bomex"]["thetal_mean"] = 3.990513801679012e-5
all_best_mse["Bomex"]["Hvar_mean"] = 2644.319895527311
all_best_mse["Bomex"]["QTvar_mean"] = 974.0942622784959
#
all_best_mse["DryBubble"] = OrderedCollections.OrderedDict()
all_best_mse["DryBubble"]["updraft_area"] = 2202.8250933477757
Expand All @@ -46,35 +46,35 @@ all_best_mse["DryBubble"]["thetal_mean"] = 4.2309956577682094e-5
all_best_mse["DryBubble"]["Hvar_mean"] = 121.33184191421392
#
all_best_mse["DYCOMS_RF01"] = OrderedCollections.OrderedDict()
all_best_mse["DYCOMS_RF01"]["qt_mean"] = 0.032229266919466225
all_best_mse["DYCOMS_RF01"]["ql_mean"] = 35.24367587802814
all_best_mse["DYCOMS_RF01"]["updraft_area"] = 29.918284685521208
all_best_mse["DYCOMS_RF01"]["updraft_w"] = 6.115951172262932
all_best_mse["DYCOMS_RF01"]["updraft_qt"] = 1.8880856402079553
all_best_mse["DYCOMS_RF01"]["updraft_thetal"] = 46.18766521884257
all_best_mse["DYCOMS_RF01"]["v_mean"] = 0.01054128704894003
all_best_mse["DYCOMS_RF01"]["u_mean"] = 0.10339578822385861
all_best_mse["DYCOMS_RF01"]["tke_mean"] = 17.149948426949422
all_best_mse["DYCOMS_RF01"]["temperature_mean"] = 9.952426566556289e-5
all_best_mse["DYCOMS_RF01"]["thetal_mean"] = 9.872177053048469e-5
all_best_mse["DYCOMS_RF01"]["Hvar_mean"] = 1280.4368756952708
all_best_mse["DYCOMS_RF01"]["QTvar_mean"] = 514.6927662355838
all_best_mse["DYCOMS_RF01"]["qt_mean"] = 0.03222926680876207
all_best_mse["DYCOMS_RF01"]["ql_mean"] = 35.24367550027249
all_best_mse["DYCOMS_RF01"]["updraft_area"] = 29.918284618025712
all_best_mse["DYCOMS_RF01"]["updraft_w"] = 6.115951158816949
all_best_mse["DYCOMS_RF01"]["updraft_qt"] = 1.8880856409405162
all_best_mse["DYCOMS_RF01"]["updraft_thetal"] = 46.18766521884522
all_best_mse["DYCOMS_RF01"]["v_mean"] = 0.010541287036688721
all_best_mse["DYCOMS_RF01"]["u_mean"] = 0.10339578819985162
all_best_mse["DYCOMS_RF01"]["tke_mean"] = 17.149948437020576
all_best_mse["DYCOMS_RF01"]["temperature_mean"] = 9.952426550825461e-5
all_best_mse["DYCOMS_RF01"]["thetal_mean"] = 9.87217703679611e-5
all_best_mse["DYCOMS_RF01"]["Hvar_mean"] = 1280.4368757021853
all_best_mse["DYCOMS_RF01"]["QTvar_mean"] = 514.6927662562823
#
all_best_mse["DYCOMS_RF02"] = OrderedCollections.OrderedDict()
all_best_mse["DYCOMS_RF02"]["qt_mean"] = 0.05056590150581544
all_best_mse["DYCOMS_RF02"]["ql_mean"] = 6.336796993150167
all_best_mse["DYCOMS_RF02"]["qr_mean"] = 19.97305871931919
all_best_mse["DYCOMS_RF02"]["updraft_area"] = 29.01240970754742
all_best_mse["DYCOMS_RF02"]["updraft_w"] = 10.135949039750885
all_best_mse["DYCOMS_RF02"]["updraft_qt"] = 4.670130026185394
all_best_mse["DYCOMS_RF02"]["updraft_thetal"] = 40.54533121446533
all_best_mse["DYCOMS_RF02"]["v_mean"] = 43.2616530285498
all_best_mse["DYCOMS_RF02"]["u_mean"] = 19.89367854311002
all_best_mse["DYCOMS_RF02"]["tke_mean"] = 11.42675955924606
all_best_mse["DYCOMS_RF02"]["temperature_mean"] = 2.269408833544109e-5
all_best_mse["DYCOMS_RF02"]["thetal_mean"] = 1.8352463020519586e-5
all_best_mse["DYCOMS_RF02"]["Hvar_mean"] = 1181.3758853785073
all_best_mse["DYCOMS_RF02"]["QTvar_mean"] = 266.52549543793253
all_best_mse["DYCOMS_RF02"]["qt_mean"] = 0.05056588788630901
all_best_mse["DYCOMS_RF02"]["ql_mean"] = 6.336794509488111
all_best_mse["DYCOMS_RF02"]["qr_mean"] = 19.973054743673107
all_best_mse["DYCOMS_RF02"]["updraft_area"] = 29.012403773925637
all_best_mse["DYCOMS_RF02"]["updraft_w"] = 10.13593995461283
all_best_mse["DYCOMS_RF02"]["updraft_qt"] = 4.670129974866
all_best_mse["DYCOMS_RF02"]["updraft_thetal"] = 40.54533121433962
all_best_mse["DYCOMS_RF02"]["v_mean"] = 43.26165302656899
all_best_mse["DYCOMS_RF02"]["u_mean"] = 19.893678545593183
all_best_mse["DYCOMS_RF02"]["tke_mean"] = 11.426758746065664
all_best_mse["DYCOMS_RF02"]["temperature_mean"] = 2.2694092652791435e-5
all_best_mse["DYCOMS_RF02"]["thetal_mean"] = 1.8352469201567945e-5
all_best_mse["DYCOMS_RF02"]["Hvar_mean"] = 1181.3758938793158
all_best_mse["DYCOMS_RF02"]["QTvar_mean"] = 266.5254456011906
#
all_best_mse["GABLS"] = OrderedCollections.OrderedDict()
all_best_mse["GABLS"]["updraft_thetal"] = 1.4983854789511822e-6
Expand All @@ -101,69 +101,69 @@ all_best_mse["life_cycle_Tan2018"]["Hvar_mean"] = 1295.718719387864
all_best_mse["life_cycle_Tan2018"]["QTvar_mean"] = 464.46621701576004
#
all_best_mse["Nieuwstadt"] = OrderedCollections.OrderedDict()
all_best_mse["Nieuwstadt"]["updraft_area"] = 98.88939550400438
all_best_mse["Nieuwstadt"]["updraft_w"] = 14.18844868379456
all_best_mse["Nieuwstadt"]["updraft_thetal"] = 117.60593865198229
all_best_mse["Nieuwstadt"]["u_mean"] = 13.553474251428796
all_best_mse["Nieuwstadt"]["tke_mean"] = 283.6256560613786
all_best_mse["Nieuwstadt"]["temperature_mean"] = 1.1368533295612513e-5
all_best_mse["Nieuwstadt"]["thetal_mean"] = 1.1156555761368827e-5
all_best_mse["Nieuwstadt"]["Hvar_mean"] = 717.9986421476253
all_best_mse["Nieuwstadt"]["updraft_area"] = 98.84436806524585
all_best_mse["Nieuwstadt"]["updraft_w"] = 14.186333903874987
all_best_mse["Nieuwstadt"]["updraft_thetal"] = 117.60593866095171
all_best_mse["Nieuwstadt"]["u_mean"] = 13.55346827865441
all_best_mse["Nieuwstadt"]["tke_mean"] = 283.62147778030925
all_best_mse["Nieuwstadt"]["temperature_mean"] = 1.1367382778385116e-5
all_best_mse["Nieuwstadt"]["thetal_mean"] = 1.1155407047485008e-5
all_best_mse["Nieuwstadt"]["Hvar_mean"] = 718.0186300173174
#
all_best_mse["Rico"] = OrderedCollections.OrderedDict()
all_best_mse["Rico"]["qt_mean"] = 1.148361614558268
all_best_mse["Rico"]["updraft_area"] = 479.5473352612418
all_best_mse["Rico"]["updraft_w"] = 82.670213352702
all_best_mse["Rico"]["updraft_qt"] = 17.228276064932476
all_best_mse["Rico"]["updraft_thetal"] = 133.87369324443438
all_best_mse["Rico"]["v_mean"] = 0.5100232407862202
all_best_mse["Rico"]["u_mean"] = 0.4236283814752767
all_best_mse["Rico"]["tke_mean"] = 154.94828856593267
all_best_mse["Rico"]["temperature_mean"] = 0.0005448065434597075
all_best_mse["Rico"]["ql_mean"] = 29736.34815326026
all_best_mse["Rico"]["qt_mean"] = 1.1412157988433447
all_best_mse["Rico"]["updraft_area"] = 479.6068859296478
all_best_mse["Rico"]["updraft_w"] = 82.71757535256751
all_best_mse["Rico"]["updraft_qt"] = 17.17250792059662
all_best_mse["Rico"]["updraft_thetal"] = 133.87359710199806
all_best_mse["Rico"]["v_mean"] = 0.5117244529755463
all_best_mse["Rico"]["u_mean"] = 0.42436510433163455
all_best_mse["Rico"]["tke_mean"] = 155.97544483542237
all_best_mse["Rico"]["temperature_mean"] = 0.0005439954218722927
all_best_mse["Rico"]["ql_mean"] = 29244.599889781333
all_best_mse["Rico"]["qi_mean"] = "NA"
all_best_mse["Rico"]["qr_mean"] = 668.5113333480476
all_best_mse["Rico"]["thetal_mean"] = 0.0005798423549005548
all_best_mse["Rico"]["Hvar_mean"] = 146080.9905513204
all_best_mse["Rico"]["QTvar_mean"] = 34789.82147560855
all_best_mse["Rico"]["qr_mean"] = 668.9618659601628
all_best_mse["Rico"]["thetal_mean"] = 0.0005780905983636465
all_best_mse["Rico"]["Hvar_mean"] = 161362.1964521471
all_best_mse["Rico"]["QTvar_mean"] = 36505.695563858135
#
all_best_mse["Soares"] = OrderedCollections.OrderedDict()
all_best_mse["Soares"]["qt_mean"] = 0.142280632665005
all_best_mse["Soares"]["updraft_area"] = 94.30518488450016
all_best_mse["Soares"]["updraft_w"] = 13.04117345064018
all_best_mse["Soares"]["updraft_qt"] = 23.634389966362473
all_best_mse["Soares"]["updraft_thetal"] = 65.72137582642573
all_best_mse["Soares"]["u_mean"] = 93.90053767068466
all_best_mse["Soares"]["tke_mean"] = 216.81644150059344
all_best_mse["Soares"]["temperature_mean"] = 1.3126483783491774e-5
all_best_mse["Soares"]["thetal_mean"] = 1.2051266758347125e-5
all_best_mse["Soares"]["Hvar_mean"] = 678.4111250018458
all_best_mse["Soares"]["qt_mean"] = 0.142219443200093
all_best_mse["Soares"]["updraft_area"] = 94.2923097607967
all_best_mse["Soares"]["updraft_w"] = 13.04176107775075
all_best_mse["Soares"]["updraft_qt"] = 23.63463116471562
all_best_mse["Soares"]["updraft_thetal"] = 65.72137555604021
all_best_mse["Soares"]["u_mean"] = 93.90032780776161
all_best_mse["Soares"]["tke_mean"] = 216.82379097833908
all_best_mse["Soares"]["temperature_mean"] = 1.3127136322547566e-5
all_best_mse["Soares"]["thetal_mean"] = 1.2051608872189155e-5
all_best_mse["Soares"]["Hvar_mean"] = 679.1402581405645
#
all_best_mse["TRMM_LBA"] = OrderedCollections.OrderedDict()
all_best_mse["TRMM_LBA"]["qt_mean"] = 2.1407483216101886
all_best_mse["TRMM_LBA"]["updraft_area"] = 1254.2030410826526
all_best_mse["TRMM_LBA"]["updraft_w"] = 9839.438142805242
all_best_mse["TRMM_LBA"]["updraft_qt"] = 263.65651619424244
all_best_mse["TRMM_LBA"]["updraft_thetal"] = 541.3706668528961
all_best_mse["TRMM_LBA"]["v_mean"] = 71.06040571570847
all_best_mse["TRMM_LBA"]["u_mean"] = 30.399741076429066
all_best_mse["TRMM_LBA"]["tke_mean"] = 48605.30351976951
all_best_mse["TRMM_LBA"]["temperature_mean"] = 0.000568889806329039
all_best_mse["TRMM_LBA"]["ql_mean"] = 248042.12933542198
all_best_mse["TRMM_LBA"]["qt_mean"] = 2.140748311962583
all_best_mse["TRMM_LBA"]["updraft_area"] = 1254.2030362859591
all_best_mse["TRMM_LBA"]["updraft_w"] = 9839.438196474672
all_best_mse["TRMM_LBA"]["updraft_qt"] = 263.65651738219054
all_best_mse["TRMM_LBA"]["updraft_thetal"] = 541.3706668645273
all_best_mse["TRMM_LBA"]["v_mean"] = 71.06040567071743
all_best_mse["TRMM_LBA"]["u_mean"] = 30.399741077951205
all_best_mse["TRMM_LBA"]["tke_mean"] = 48605.30323834375
all_best_mse["TRMM_LBA"]["temperature_mean"] = 0.0005688898055651253
all_best_mse["TRMM_LBA"]["ql_mean"] = 248042.12631836807
all_best_mse["TRMM_LBA"]["qi_mean"] = "NA"
all_best_mse["TRMM_LBA"]["qr_mean"] = "NA"
all_best_mse["TRMM_LBA"]["qs_mean"] = "NA"
all_best_mse["TRMM_LBA"]["thetal_mean"] = 0.0005009943303753257
all_best_mse["TRMM_LBA"]["Hvar_mean"] = 446334.4571983643
all_best_mse["TRMM_LBA"]["QTvar_mean"] = 6279.991172736763
all_best_mse["TRMM_LBA"]["thetal_mean"] = 0.0005009943307719285
all_best_mse["TRMM_LBA"]["Hvar_mean"] = 446334.5543949141
all_best_mse["TRMM_LBA"]["QTvar_mean"] = 6279.991116973818
#
all_best_mse["LES_driven_SCM"] = OrderedCollections.OrderedDict()
all_best_mse["LES_driven_SCM"]["qt_mean"] = 0.18120985929824446
all_best_mse["LES_driven_SCM"]["v_mean"] = 0.32300454969676684
all_best_mse["LES_driven_SCM"]["u_mean"] = 0.0755050984362397
all_best_mse["LES_driven_SCM"]["temperature_mean"] = 5.493450606740618e-5
all_best_mse["LES_driven_SCM"]["ql_mean"] = 27470.026298802994
all_best_mse["LES_driven_SCM"]["thetal_mean"] = 5.9887641720678635e-5
all_best_mse["LES_driven_SCM"]["qt_mean"] = 0.1809994648114718
all_best_mse["LES_driven_SCM"]["v_mean"] = 0.3230961648090686
all_best_mse["LES_driven_SCM"]["u_mean"] = 0.07553927533768831
all_best_mse["LES_driven_SCM"]["temperature_mean"] = 5.4877160276794674e-5
all_best_mse["LES_driven_SCM"]["ql_mean"] = 27438.0665544269
all_best_mse["LES_driven_SCM"]["thetal_mean"] = 5.979480643194291e-5
#
#################################
#################################
Expand Down
33 changes: 8 additions & 25 deletions src/closures/entr_detr.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ function compute_inverse_timescale(εδ_model, b_up::FT, b_en::FT, w_up::FT, w_e
Δw = get_Δw(εδ_model, w_up, w_en)
c_λ = εδ_params(εδ_model).c_λ

l_1 = c_λ * abs(Δb / sqrt(tke + 1e-8))
l_1 = c_λ * abs(Δb / (sqrt(abs(tke)) + eps(FT)))
l_2 = abs(Δb / Δw)
l = SA.SVector(l_1, l_2)
return lamb_smooth_minimum(l, FT(0.1), FT(0.0005))
Expand Down Expand Up @@ -45,6 +45,7 @@ function entrainment_inv_length_scale(
return (λ / Δw)
end


function entrainment_inv_length_scale(
εδ_model,
b_up::FT,
Expand Down Expand Up @@ -331,35 +332,17 @@ function compute_phys_entr_detr!(
# fractional dynamical entrainment from prognostic state
ε_nondim, δ_nondim = prog_up[i].ε_nondim[k], prog_up[i].δ_nondim[k]
mean_model = εδ_closure.mean_model
ε_dyn, δ_dyn = εδ_dyn(
mean_model,
εδ_model_vars,
BuoyVelEntrDimScale(),
BuoyVelEntrDimScale(),
ε_nondim,
δ_nondim,
)
ε_dyn, δ_dyn =
εδ_dyn(mean_model, εδ_model_vars, edmf.entr_dim_scale, edmf.detr_dim_scale, ε_nondim, δ_nondim)
# turbulent & mean nondimensional entrainment
ε_nondim, δ_nondim = non_dimensional_function(mean_model, εδ_model_vars)
ε_dyn, δ_dyn = εδ_dyn(
mean_model,
εδ_model_vars,
BuoyVelEntrDimScale(),
BuoyVelEntrDimScale(),
ε_nondim,
δ_nondim,
)
ε_dyn, δ_dyn =
εδ_dyn(mean_model, εδ_model_vars, edmf.entr_dim_scale, edmf.detr_dim_scale, ε_nondim, δ_nondim)
else
# fractional, turbulent & nondimensional entrainment
ε_nondim, δ_nondim = non_dimensional_function(εδ_closure, εδ_model_vars)
ε_dyn, δ_dyn = εδ_dyn(
εδ_closure,
εδ_model_vars,
BuoyVelEntrDimScale(),
BuoyVelEntrDimScale(),
ε_nondim,
δ_nondim,
)
ε_dyn, δ_dyn =
εδ_dyn(εδ_closure, εδ_model_vars, edmf.entr_dim_scale, edmf.detr_dim_scale, ε_nondim, δ_nondim)
end
aux_up[i].entr_sc[k] = ε_dyn
aux_up[i].detr_sc[k] = δ_dyn
Expand Down
3 changes: 2 additions & 1 deletion src/types.jl
View file
Original file line number Diff line number Diff line change
Expand Up @@ -620,6 +620,7 @@ function EDMFModel(::Type{FT}, namelist, precip_model, rain_formation_model) whe
β_lim = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "area_limiter_power")
c_γ = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "turbulent_entrainment_factor")
c_δ = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "detrainment_factor")
smin_rm = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "smin_rm")
Π_norm = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "pi_norm_consts")
Π_norm = SA.SVector{length(Π_norm), FT}(Π_norm)

Expand Down Expand Up @@ -713,7 +714,7 @@ function EDMFModel(::Type{FT}, namelist, precip_model, rain_formation_model) whe
Ri_c = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "Ri_crit"),
Le = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "Lewis_number"; default = 1.0),
smin_ub = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "smin_ub"),
smin_rm = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "smin_rm"),
smin_rm = smin_rm,
l_max = parse_namelist(namelist, "turbulence", "EDMF_PrognosticTKE", "l_max"; default = 1.0e6),
)

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