Clean up, document LES driven forcing.

CliMA · Nov 3, 2021 · 075bdd7 · 075bdd7
1 parent 04dadf6
commit 075bdd7
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Showing 4 changed files with 38 additions and 23 deletions.
diff --git a/integration_tests/utils/Cases.jl b/integration_tests/utils/Cases.jl
@@ -1592,8 +1592,6 @@ function CasesBase(case::LES_driven_SCM, namelist, grid::Grid, param_set, Sur, F
     Fo.apply_coriolis = false
     Fo.coriolis_param = 0.376e-4 # s^{-1}
     Fo.apply_subsidence = true
-    Fo.apply_coriolis = false
-    Fo.apply_subsidence = true
     Fo.nudge_tau = namelist["forcing"]["nudging_timescale"]
     return TC.CasesBase(case; inversion_option, Sur, Fo, Rad, LESDat)
 end
@@ -1620,14 +1618,6 @@ function initialize_profiles(self::CasesBase{LES_driven_SCM}, grid::Grid, gm, st
     NC.Dataset(self.LESDat.les_filename, "r") do data
         imin = self.LESDat.imin
         imax = self.LESDat.imax
-        t = data.group["profiles"]["t"][:]
-        # define time interval
-        t_interval_from_end_s = 6 * 3600
-        t_from_end_s = Array(t) .- t[end]
-        # find inds within time interval
-        time_interval_bool = findall(>(-t_interval_from_end_s), t_from_end_s)
-        imin = time_interval_bool[1]
-        imax = time_interval_bool[end]
 
         zc_les = Array(TC.get_nc_data(data, "zc"))
         parent(prog_gm.θ_liq_ice) .=

diff --git a/src/Forcing.jl b/src/Forcing.jl
@@ -19,9 +19,9 @@ function initialize(self::ForcingBase{ForcingLES}, grid, LESDat::LESData)
 
         zc_les = Array(get_nc_data(data, "zc"))
 
-        self.dtdt_hadv = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dtdt_hadv", imin, imax))
-        self.dtdt_nudge = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dtdt_nudge", imin, imax))
-        self.dtdt_fluc = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dtdt_fluc", imin, imax))
+        self.dTdt_hadv = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dtdt_hadv", imin, imax))
+        self.dTdt_nudge = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dtdt_nudge", imin, imax))
+        self.dTdt_fluc = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dtdt_fluc", imin, imax))
         self.dqtdt_hadv = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dqtdt_hadv", imin, imax))
         self.dqtdt_nudge = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dqtdt_nudge", imin, imax))
         self.dqtdt_fluc = pyinterp(grid.zc, zc_les, mean_nc_data(data, "profiles", "dqtdt_fluc", imin, imax))

diff --git a/src/Turbulence_PrognosticTKE.jl b/src/Turbulence_PrognosticTKE.jl
@@ -71,9 +71,9 @@ function compute_gm_tendencies!(edmf::EDMF_PrognosticTKE, grid, state, Case, gm,
         end
 
         if force_type(Case.Fo) <: ForcingLES
-            H_horz_adv = Case.Fo.dtdt_hadv[k] / Π
-            H_nudge = Case.Fo.dtdt_nudge[k] / Π
-            H_fluc = Case.Fo.dtdt_fluc[k] / Π
+            H_horz_adv = Case.Fo.dTdt_hadv[k] / Π
+            H_nudge = Case.Fo.dTdt_nudge[k] / Π
+            H_fluc = Case.Fo.dTdt_fluc[k] / Π
 
             gm_U_nudge_k = (Case.Fo.u_nudge[k] - prog_gm.u[k]) / Case.Fo.nudge_tau
             gm_V_nudge_k = (Case.Fo.v_nudge[k] - prog_gm.v[k]) / Case.Fo.nudge_tau

diff --git a/src/types.jl b/src/types.jl
@@ -30,7 +30,7 @@ end
 """
     MoistureDeficitEntr
 
-My entrainment detrainment model
+Entrainment detrainment model from Cohen et al (2020)
 
 $(DocStringExtensions.FIELDS)
 """
@@ -417,24 +417,49 @@ Base.@kwdef mutable struct LESData
     les_filename::String = nothing
 end
 
+"""
+    ForcingBase
+
+LES-driven forcing
+
+$(DocStringExtensions.FIELDS)
+"""
 Base.@kwdef mutable struct ForcingBase{T}
+    "Large-scale subsidence"
     subsidence::AbstractArray{Float64, 1} = zeros(1)
-    dTdt::AbstractArray{Float64, 1} = zeros(1) # horizontal advection temperature tendency
-    dqtdt::AbstractArray{Float64, 1} = zeros(1) # horizontal advection moisture tendency
-    dtdt_hadv::AbstractArray{Float64, 1} = zeros(1)
-    dtdt_nudge::AbstractArray{Float64, 1} = zeros(1)
-    dtdt_fluc::AbstractArray{Float64, 1} = zeros(1)
+    "Large-scale temperature tendency"
+    dTdt::AbstractArray{Float64, 1} = zeros(1)
+    "Large-scale moisture tendency"
+    dqtdt::AbstractArray{Float64, 1} = zeros(1)
+    "Horizontal advection of temperature"
+    dTdt_hadv::AbstractArray{Float64, 1} = zeros(1)
+    "Temperature tendency due to relaxation to large-scale"
+    dTdt_nudge::AbstractArray{Float64, 1} = zeros(1)
+    "Vertical turbulent advection of temperature"
+    dTdt_fluc::AbstractArray{Float64, 1} = zeros(1)
+    "Horizontal advection of moisture"
     dqtdt_hadv::AbstractArray{Float64, 1} = zeros(1)
+    "Moisture tendency due to relaxation to large-scale"
     dqtdt_nudge::AbstractArray{Float64, 1} = zeros(1)
+    "Vertical turbulent advection of moisture"
     dqtdt_fluc::AbstractArray{Float64, 1} = zeros(1)
+    "Reference u profile for relaxation tendency"
     u_nudge::AbstractArray{Float64, 1} = zeros(1)
+    "Reference v profile for relaxation tendency"
     v_nudge::AbstractArray{Float64, 1} = zeros(1)
+    "Boolean specifying whether Coriolis forcing is applied"
     apply_coriolis::Bool = false
+    "Boolean specifying whether subsidence forcing is applied"
     apply_subsidence::Bool = false
+    "Coriolis parameter"
     coriolis_param::Float64 = 0
+    "Geostrophic u velocity"
     ug::AbstractArray{Float64, 1} = zeros(1)
+    "Geostrophic v velocity"
     vg::AbstractArray{Float64, 1} = zeros(1)
-    nudge_tau::Float64 = 0.0 # default is set to a value that will break
+    "Momentum relaxation timescale"
+    nudge_tau::Float64 = 0.0
+    "Conversion function from forcing to prognostic"
     convert_forcing_prog_fp::Function = x -> x
 end