more review comments, add CF to new diagnostics

examples/hybrid/driver.jl

@@ -177,8 +177,8 @@ if config.parsed_args["check_precipitation"]
         )
 
         # cloud fraction diagnostics
-        @assert !any(isnan, sol.prob.p.precomputed.cloud_fraction[colidx])
-        @test minimum(sol.prob.p.precomputed.cloud_fraction[colidx]) >= FT(0)
-        @test maximum(sol.prob.p.precomputed.cloud_fraction[colidx]) <= FT(1)
+        @assert !any(isnan, sol.prob.p.precomputed.ᶜcloud_fraction[colidx])
+        @test minimum(sol.prob.p.precomputed.ᶜcloud_fraction[colidx]) >= FT(0)
+        @test maximum(sol.prob.p.precomputed.ᶜcloud_fraction[colidx]) <= FT(1)
     end
 end

src/cache/cloud_fraction.jl

@@ -3,7 +3,7 @@ import ClimaCore.RecursiveApply: rzero, ⊞, ⊠
 
 # TODO: write a test with scalars that are linear with z
 """
-    Diagnose horizntal covariances based on vertical gradients
+    Diagnose horizontal covariances based on vertical gradients
     (i.e. taking turbulence production as the only term)
 """
 function covariance_from_grad(coeff, ᶜmixing_length, ᶜ∇Φ, ᶜ∇Ψ)
@@ -15,16 +15,16 @@ end
 """
 function set_cloud_fraction!(Y, p, ::DryModel)
     FT = eltype(Y)
-    (; cloud_fraction) = p.precomputed
-    @. cloud_fraction = FT(0)
+    (; ᶜcloud_fraction) = p.precomputed
+    @. ᶜcloud_fraction = FT(0)
 end
 function set_cloud_fraction!(Y, p, ::Union{EquilMoistModel, NonEquilMoistModel})
     (; SG_quad, params) = p
 
     FT = eltype(params)
     thermo_params = CAP.thermodynamics_params(params)
     ᶜdz = Fields.Δz_field(axes(Y.c))
-    (; ᶜts, ᶜp, cloud_fraction) = p.precomputed
+    (; ᶜts, ᶜp, ᶜcloud_fraction) = p.precomputed
 
     ᶜqₜ = p.scratch.ᶜtemp_scalar
     ᶜθ = p.scratch.ᶜtemp_scalar_2
@@ -33,7 +33,7 @@ function set_cloud_fraction!(Y, p, ::Union{EquilMoistModel, NonEquilMoistModel})
 
     coeff = FT(2.1) # TODO - move to parameters
 
-    @. cloud_fraction = quad_loop(
+    @. ᶜcloud_fraction = quad_loop(
         SG_quad,
         ᶜp,
         ᶜqₜ,
@@ -70,7 +70,7 @@ where:
   - qt′qt′, θl′θl′, θl′qt′ are the (co)variances of q_tot and liquid ice potential temperature
   - thermo params are the thermodynamics parameters
 
-The function trnasforms and imposes additional limits on the quadrature points.
+The function transforms and imposes additional limits on the quadrature points.
 It returns cloud fraction computed as a sum over quadrature points.
 """
 function quad_loop(

src/cache/precomputed_quantities.jl

@@ -54,7 +54,7 @@ function precomputed_quantities(Y, atmos)
         ᶜh_tot = similar(Y.c, FT),
         sfc_conditions = Fields.Field(SCT, Spaces.level(axes(Y.f), half)),
     )
-    cloud_diagnostics = (; cloud_fraction = similar(Y.c, FT),)
+    cloud_diagnostics = (; ᶜcloud_fraction = similar(Y.c, FT),)
     advective_sgs_quantities =
         atmos.turbconv_model isa PrognosticEDMFX ?
         (;

src/callbacks/callbacks.jl

@@ -186,7 +186,7 @@ function common_diagnostics(p, ᶜu, ᶜts)
         specific_enthalpy = TD.specific_enthalpy.(thermo_params, ᶜts),
         buoyancy = CAP.grav(p.params) .* (p.core.ᶜρ_ref .- ᶜρ) ./ ᶜρ,
         density = TD.air_density.(thermo_params, ᶜts),
-        cloud_fraction_gm = p.precomputed.cloud_fraction,
+        cloud_fraction_gm = p.precomputed.ᶜcloud_fraction,
     )
     if !(p.atmos.moisture_model isa DryModel)
         diagnostics = (;

src/diagnostics/core_diagnostics.jl

@@ -210,6 +210,22 @@ add_diagnostic_variable!(
     end,
 )
 
+###
+# Cloud fraction (3d)
+###
+add_diagnostic_variable!(
+    short_name = "cf",
+    long_name = "Cloud fraction",
+    units = "%",
+    compute! = (out, state, cache, time) -> begin
+        FT = eltype(cache.precomputed.ᶜcloud_fraction)
+        if isnothing(out)
+            return copy(cache.precomputed.ᶜcloud_fraction) .* FT(100)
+        else
+            out .= cache.precomputed.ᶜcloud_fraction .* FT(100)
+        end
+    end,
+)
 
 ###
 # Relative humidity (3d)