diff --git a/README.md b/README.md
index ab200c25..94afd624 100644
--- a/README.md
+++ b/README.md
@@ -19,9 +19,11 @@ See the Getting Started page in the [documentation](https://nichollsh.github.io/
## Repository structure
* `agni.jl` - AGNI executable
* `LICENSE.txt` - License for use and re-use
-* `doc/` - Further documentation
-* `out/` - Output files
-* `res/` - Resources (configuration files, etc.)
+* `deps/` - Package build scripts
+* `docs/` - Documentation source files
+* `misc/` - Miscellaneous files
+* `out/` - Model output
+* `res/` - Resources (configs, thermodynamic data, etc.)
* `src/` - Package source code
* `test/` - Package tests
* `tutorials/` - Notebooks and tutorials
diff --git a/docs/src/model/index.md b/docs/src/model/index.md
index 882f3a67..d8bfb511 100644
--- a/docs/src/model/index.md
+++ b/docs/src/model/index.md
@@ -2,7 +2,7 @@
AGNI models a planetary atmosphere by treating it as a single column (1D) and splitting it up into levels of finite thickness. These levels are defined in pressure-space, and are arranged logarithmically between the surface and the top of the atmosphere. The atmosphere is assumed to be plane-parallel. Quantities such as pressure and temperature are calculated at level-centres and level-edges, while energy fluxes are calculated only at the edges, and thermodynamic properties (e.g. heat capacity) are calculated only at their centres.
## Radiative transfer
-Radiative transfer (RT) refers to the transport of radiation energy through a medium subject to the characteristics of the medium. Radiation passing through an atmosphere is absorbed, emitted, scattered, and reflected. In the context of planetary atmospheres, we also have to handle their surfaces, cloud formation, and radiation from the host star.
+Radiative transfer (RT) refers to the transport of radiation energy through a medium subject to the characteristics of the medium. Radiation passing through an atmosphere is absorbed, emitted, scattered, and reflected. In the context of planetary atmospheres, we also have to handle their surfaces, cloud formation, and radiation from the host star.
AGNI simulates RT using SOCRATES, a numerical code written by the UK Met Office which solves the RT equation using a two-stream solution. SOCRATES is accessed using a Julia interface originally written by Stuart Daines. The atmosphere is assumed to be hydrostatically supported and to behave as an ideal gas. Opacity is handle using the correlated-k approximation, with either random overlap or equivalent extinction used to account for overlapping absorption in mixtures of gases.
@@ -10,6 +10,7 @@ The model uses k-terms fitted to spectral absorption cross-section data from [DA
```@raw html
```
+Surface reflectivity is modelled either as a greybody or with a wavelength-depedent spectral albedo.
## Convection
Convection is a turbulent process which occurs across more than one spatial dimension, so it must be parameterised within 1D models like AGNI. In fact, it is typically parameterised inside 3D global circulation models as resolving convection is numerically expensive. AGNI uses mixing length theory (MLT) to parameterise convection. This is in contrast to convective adjustment, which forcibly adjusts a convectively unstable region of the atmosphere to the corresponding adiabat while ensuring that enthalpy is conserved.
@@ -29,7 +30,7 @@ heating rate. This is then integrated (from the TOA downwards) to provide a late
Latent heats are temperature-dependent, using values derived from Coker (2007) and Wagner & Pruß (2001).
## Solar flux
-A key input to the radiation model is the shortwave downward-directed flux from the star at the top of the atmosphere. This is quantified by the bolometric instellation flux, a scale factor, a grey planetary albedo, and a zenith angle. All of these may be provided to the model through the configuration file. The model also requires a stellar spectrum scaled to the top of the atmosphere.
+A key input to the radiation model is the shortwave downward-directed flux from the star at the top of the atmosphere. This is quantified by the bolometric instellation flux, a scale factor, an artificial additional albedo factor, and a zenith angle. All of these may be provided to the model through the configuration file. The model also requires a stellar spectrum scaled to the top of the atmosphere.
## Obtaining a solution
diff --git a/tutorials/03_rce-runaway.ipynb b/misc/rce-runaway.ipynb
similarity index 100%
rename from tutorials/03_rce-runaway.ipynb
rename to misc/rce-runaway.ipynb
diff --git a/misc/selsis.ipynb b/misc/selsis.ipynb
new file mode 100644
index 00000000..13a9fc0c
--- /dev/null
+++ b/misc/selsis.ipynb
@@ -0,0 +1,1165 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Replicate temperature profile from Selsis et al., 2023"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import system packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import system packages\n",
+ "using Printf\n",
+ "using DelimitedFiles\n",
+ "using Plots\n",
+ "using LaTeXStrings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "35.6"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "selsis_data = readdlm(\"../res/literature_data/profile/OTR10000.txt\", Float64; header=false, skipstart=1)\n",
+ "sel_p = selsis_data[:,1]*1.0e-5\n",
+ "sel_t = selsis_data[:,7]\n",
+ "sel_s0 = 35.6"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1360.0"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "F_sun = 1.36e3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Setup AGNI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"/Users/nichollsh/Projects/AGNI/socrates\""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Set directory\n",
+ "ROOT_DIR = abspath(joinpath(pwd(),\"../\"))\n",
+ "ENV[\"RAD_DIR\"] = joinpath(ROOT_DIR,\"socrates\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling AGNI [ede838c1-9ec3-4ebe-8ae8-da4091b3f21c]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Import AGNI\n",
+ "using AGNI\n",
+ "import AGNI.atmosphere as atmosphere\n",
+ "import AGNI.solver as nl\n",
+ "import AGNI.dump as dump\n",
+ "import AGNI.plotting as plotting\n",
+ "\n",
+ "# Disable logging from AGNI module\n",
+ "AGNI.setup_logging(\"\",0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"/Users/nichollsh/Projects/AGNI/out/\""
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Configuration options\n",
+ "instellation = 50*F_sun # Stellar flux [W m-2]\n",
+ "gravity = 9.81\n",
+ "s0_fact = 0.375 \n",
+ "zenith_degrees = 48.19 \n",
+ "albedo_b = 0.0\n",
+ "radius = 6.37e6 \n",
+ "nlev_centre = 40\n",
+ "p_surf = 270.0 # bar\n",
+ "p_top = 1e-5 # bar \n",
+ "mole_fractions = Dict([(\"H2O\", 1.0)])\n",
+ "condensates = [\"H2O\"]\n",
+ "tmp_surf = 3000.0\n",
+ "thermo = false\n",
+ "\n",
+ "spectral_file = joinpath(ROOT_DIR,\"res/spectral_files/Oak/318/Oak.sf\")\n",
+ "star_file = joinpath(ROOT_DIR,\"res/stellar_spectra/sun.txt\")\n",
+ "output_dir = joinpath(ROOT_DIR,\"out/\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create output directory\n",
+ "rm(output_dir,force=true,recursive=true)\n",
+ "mkdir(output_dir);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Setup atmosphere\n",
+ "atmos = atmosphere.Atmos_t()\n",
+ "atmosphere.setup!(atmos, ROOT_DIR, output_dir, \n",
+ " spectral_file,\n",
+ " instellation, s0_fact, albedo_b, zenith_degrees,\n",
+ " tmp_surf,\n",
+ " gravity, radius,\n",
+ " nlev_centre, p_surf, p_top,\n",
+ " mole_fractions, \"\",\n",
+ " flag_gcontinuum=true,\n",
+ " flag_rayleigh=true,\n",
+ " thermo_functions=thermo,\n",
+ " condensates=condensates\n",
+ " )\n",
+ "atmosphere.allocate!(atmos, star_file)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fill!(atmos.tmp, atmos.tmp_surf);\n",
+ "fill!(atmos.tmpl, atmos.tmp_surf);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Solver success? true\n"
+ ]
+ }
+ ],
+ "source": [
+ "solver_success = nl.solve_energy!(atmos, \n",
+ " sol_type=3, # Conserve energy, but with fixed surface temperature\n",
+ " sens_heat=true, # Do not include sensible heat transport\n",
+ " latent=true, # Include condensation\n",
+ " method=1, # Use the Newton-Raphson method\n",
+ " dx_max=400.0, # Allow large step sizes because of the poor initial guess\n",
+ " linesearch=true , # Enable Linesearch\n",
+ " save_frames=false, modplot=1, # disable plotting \n",
+ " conv_atol=0.1\n",
+ " )\n",
+ "println(\"Solver success? $solver_success\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running model for 9 samples... \n",
+ "Running model for instellation = 5.00e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 4.50e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 4.00e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 3.50e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 3.00e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 2.50e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 2.00e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 1.50e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Running model for instellation = 1.00e+01 F_sun \n",
+ "--------------------------------- \n",
+ "Done!\n"
+ ]
+ }
+ ],
+ "source": [
+ "ins_arr = collect(range(start=50, stop=10, step=-5))\n",
+ "atm_arr = atmosphere.Atmos_t[]\n",
+ "\n",
+ "@printf(\"Running model for %d samples... \\n\", length(ins_arr))\n",
+ "\n",
+ "for (i,ins) in enumerate(ins_arr)\n",
+ " @printf(\"Running model for instellation = %.2e F_sun \\n\",ins)\n",
+ "\n",
+ " atmos.instellation = F_sun * ins\n",
+ " \n",
+ " # Run model\n",
+ " solver_success = nl.solve_energy!(atmos, \n",
+ " sol_type=3, \n",
+ " sens_heat=true, \n",
+ " latent=true, \n",
+ " method=1, \n",
+ " dx_max=500.0, # Smaller steps\n",
+ " linesearch=true , \n",
+ " save_frames=false, modplot=1,\n",
+ " modprint=0,\n",
+ " conv_atol=0.1 \n",
+ " )\n",
+ "\n",
+ " # Store result\n",
+ " push!(atm_arr, deepcopy(atmos))\n",
+ "\n",
+ " @printf(\"--------------------------------- \\n\")\n",
+ "end \n",
+ "println(\"Done!\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "\"Times New Roman\""
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "fs=12\n",
+ "lw=1.5\n",
+ "ff=\"Times New Roman\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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",
+ "image/svg+xml": [
+ "\n",
+ "\n"
+ ],
+ "text/html": [
+ "\n",
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "\"/Users/nichollsh/Projects/AGNI/out/profiles.pdf\""
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr_P = atmos.pl .* 1.0e-5 # Convert Pa to bar\n",
+ "ylims = (arr_P[1], arr_P[end])\n",
+ "yticks = 10.0 .^ round.(Int,range( log10(ylims[1]), stop=log10(ylims[2]), step=1))\n",
+ "\n",
+ "plt = plot(framestyle=:box, size=(700,400), dpi=300, \n",
+ " leg=:topright, legcolumn=-1,\n",
+ " tickfontsize=fs, tickfontfamily=ff,\n",
+ " guidefontsize=fs, guidefontfamily=ff,\n",
+ " legendfontfamily=ff, legendfontsize=fs, \n",
+ " legendtitlefontfamily=ff, legendtitlefontsize=fs,\n",
+ " leg_title=L\"S$_{\\odot}$, $\\alpha_b$\")\n",
+ " \n",
+ "\n",
+ "p = Plots.palette(:viridis, length(ins_arr))\n",
+ "for (i,this_atm) in enumerate(atm_arr)\n",
+ " plot!(plt, this_atm.tmpl, this_atm.pl* 1.0e-5, lc=p[i], linewidth=lw, label=@sprintf(\"%d, %.2f\",this_atm.instellation/F_sun, this_atm.flux_u_sw[1]/this_atm.flux_d_sw[1]))\n",
+ "\n",
+ " if (i < length(ins_arr))\n",
+ " if (ins_arr[i] < sel_s0 < ins_arr[i+1]) || (ins_arr[i+1] < sel_s0 < ins_arr[i])\n",
+ " plot!(plt, sel_t, sel_p, lc=\"black\", label=\"Selsis\", linewidth=lw*2.0)\n",
+ " end \n",
+ " end \n",
+ "end \n",
+ "\n",
+ "xlabel!(plt, \"Temperature [K]\")\n",
+ "xaxis!(plt, minorgrid=true)\n",
+ "ylabel!(plt, \"Pressure [bar]\")\n",
+ "yflip!(plt)\n",
+ "yaxis!(plt, yscale=:log10, ylims=ylims, yticks=yticks)\n",
+ "display(plt)\n",
+ "savefig(plt,joinpath(output_dir,\"profiles.pdf\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for (i, atm) in enumerate(atm_arr)\n",
+ " dump.write_ncdf(atm, joinpath(atm.OUT_DIR, \"$i.nc\"))\n",
+ "end"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Julia 1.9.1",
+ "language": "julia",
+ "name": "julia-1.9"
+ },
+ "language_info": {
+ "file_extension": ".jl",
+ "mimetype": "application/julia",
+ "name": "julia",
+ "version": "1.9.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/res/config/55cnce_chem.toml b/res/config/55cnce_chem.toml
index 392c9417..67357692 100644
--- a/res/config/55cnce_chem.toml
+++ b/res/config/55cnce_chem.toml
@@ -6,8 +6,8 @@ title = "Roughly 55 Cancri e @ fO2=IW"
instellation = 3.3231e6
albedo_b = 0.0
s0_fact = 0.6652
- zenith_angle = 60.0
- albedo_s = 0.0
+ zenith_angle = 60.0
+ surface_material= "res/surface_albedo/c9mb29.tab"
radius = 1.1959e7
gravity = 22.304
tmp_int = 0.0
@@ -15,14 +15,13 @@ title = "Roughly 55 Cancri e @ fO2=IW"
wind_speed = 2.0
[files]
- input_sf = "res/spectral_files/nogit/Dayspring/256/Dayspring.sf"
+ input_sf = "res/spectral_files/Dayspring/256/Dayspring.sf"
input_star = "res/stellar_spectra/hd97658.txt"
output_dir = "out/"
[composition]
p_top = 1e-7
- # p_dict = { H2O=0.206, CO2=14.184, N2 =16.303, H2 =0.462, CO =211.539 }
- p_dict = {H2O = 0.308,CO2 = 15.516,N2 = 16.065,H2 = 0.691,CO = 231.415 }
+ p_dict = {H2O = 0.308,CO2 = 15.516,N2 = 16.065,H2 = 0.691,CO = 231.415 }
include_all = true
chemistry = 1
condensates = []
diff --git a/res/config/condense.toml b/res/config/condense.toml
index a71f3bc3..bd1f8df0 100644
--- a/res/config/condense.toml
+++ b/res/config/condense.toml
@@ -6,7 +6,8 @@ title = "Condensation test"
instellation = 2500.0
albedo_b = 0.0
s0_fact = 0.375
- zenith_angle = 48.19
+ zenith_angle = 48.19
+ surface_material= "blackbody"
albedo_s = 0.2
radius = 6.37e6
gravity = 9.81
@@ -19,7 +20,7 @@ title = "Condensation test"
[files]
clean_output = true
- input_sf = "res/spectral_files/nogit/Dayspring/16/Dayspring.sf"
+ input_sf = "res/spectral_files/Dayspring/16/Dayspring.sf"
input_star = "res/stellar_spectra/sun.txt"
output_dir = "out/"
diff --git a/res/config/default.toml b/res/config/default.toml
index 340c8945..8c7d0b8f 100644
--- a/res/config/default.toml
+++ b/res/config/default.toml
@@ -5,13 +5,14 @@
title = "Default" # Name for this configuration file
[planet]
- tmp_surf = 2000.0 # Surface temperature [kelvin]
- instellation = 44000.0 # Stellar flux at planet's orbital distance [W m-2]
- albedo_b = 0.18 # Pseudo bond-albedo which downscales the stellar flux by 1-this_value
- s0_fact = 0.6652 # Stellar flux scale factor which accounts for planetary rotation (c.f. Cronin+13)
- zenith_angle = 60.0 # Characteristic zenith angle for incoming stellar radiation [degrees]
- albedo_s = 0.0 # Surface albedo
- radius = 6.37e6 # Planet radius at the surface [m]
+ tmp_surf = 2000.0 # Surface temperature [kelvin].
+ instellation = 44000.0 # Stellar flux at planet's orbital distance [W m-2].
+ albedo_b = 0.18 # Pseudo bond-albedo which downscales the stellar flux by 1-this_value.
+ s0_fact = 0.6652 # Stellar flux scale factor which accounts for planetary rotation (c.f. Cronin+13).
+ zenith_angle = 60.0 # Characteristic zenith angle for incoming stellar radiation [degrees].
+ surface_material= "blackbody" # Surface material (can be "blackbody" or path to data file).
+ albedo_s = 0.3 # Grey surface albedo when material=blackbody.
+ radius = 6.37e6 # Planet radius at the surface [m].
gravity = 9.81 # Gravitational acceleration at the surface [m s-2]
skin_d = 0.01 # Conductive skin thickness [m]. Used when sol_type=2.
skin_k = 2.0 # Conductive skin conductivity [W m-1 K-1]. Used when sol_type=2.
@@ -22,20 +23,20 @@ title = "Default" # Name for this configuration file
[files]
input_sf = "res/spectral_files/Frostflow/256/Frostflow.sf" # Path to SOCRATES spectral file.
- input_star = "res/stellar_spectra/sun.txt" # Path to stellar spectrum.
+ input_star = "res/stellar_spectra/trappist-1.txt" # Path to stellar spectrum.
output_dir = "out/" # Path to output directory.
[composition]
- p_surf = 270.0 # Total surface pressure [bar]
- p_top = 1e-5 # Total top-of-atmosphere pressure [bar]
- vmr_dict = { H2O = 1.0} # Volatile volume mixing ratios (=mole fractions)
+ p_surf = 270.0 # Total surface pressure [bar].
+ p_top = 1e-5 # Total top-of-atmosphere pressure [bar].
+ vmr_dict = { H2O=1.0 } # Volatile volume mixing ratios (=mole fractions).
vmr_path = "" # Path to input volume mixing ratios. Not required if planet.vmr is passed.
- include_all = false # Track extra gases, even when their mixing ratio is zero
- chemistry = 0 # Chemistry type (see wiki)
+ include_all = false # Track extra gases, even when their mixing ratio is zero.
+ chemistry = 0 # Chemistry type (see wiki).
condensates = [] # List of volatiles which are allowed to condense.
[execution]
- clean_output = true # Clean the output folder at model startup
+ clean_output = true # Clean the output folder at model startup.
verbosity = 1 # Log level (0: none, 1: normal, 2: debug)
max_steps = 20000 # Maximum number of solver steps.
max_runtime = 400 # Maximum wall-clock solver runtime [s].
diff --git a/res/config/hotdry.toml b/res/config/hotdry.toml
index 3236d2c0..38e3cc01 100644
--- a/res/config/hotdry.toml
+++ b/res/config/hotdry.toml
@@ -6,7 +6,8 @@ title = "Hot and dry"
instellation = 44000.0
albedo_b = 0.18
s0_fact = 0.6652
- zenith_angle = 60.0
+ zenith_angle = 60.0
+ surface_material= "blackbody"
albedo_s = 0.0
radius = 6.37e6
gravity = 9.81
diff --git a/res/config/selsis.toml b/res/config/selsis.toml
deleted file mode 100644
index 117329f9..00000000
--- a/res/config/selsis.toml
+++ /dev/null
@@ -1,68 +0,0 @@
-# AGNI configuration file
-title = "Comparison with Selsis+23"
-
-[planet]
- tmp_surf = 2000.0
- instellation = 40000
- albedo_b = 0.0
- s0_fact = 0.375
- zenith_angle = 48.19
- albedo_s = 0.0
- radius = 6.37e6
- gravity = 9.81
- tmp_int = 0.0
- turb_coeff = 1.0e-4
- wind_speed = 10.0
-
-[files]
- clean_output = true
- input_sf = "res/spectral_files/Frostflow/256/Frostflow.sf"
- input_star = "res/stellar_spectra/sun.txt"
- output_dir = "out/"
-
-
-[composition]
- p_surf = 270.0
- p_top = 1e-5
- vmr_dict = { H2O = 1.0}
- vmr_path = ""
- include_all = false
- chemistry = 0
- condensates = ["H2O"]
-
-[execution]
- clean_output = true
- verbosity = 1
- max_steps = 200
- max_runtime = 400
- num_levels = 40
- continua = true
- rayleigh = true
- cloud = false
- aerosol = false
- overlap_method = 4
- thermo_funct = false
- sensible_heat = true
- latent_heat = true
- convection_type = "mlt"
- condensates = []
- chemistry = 0
- solution_type = 3
- solvers = ["newton"]
- dx_max = 400.0
- initial_state = ["ncdf","nogit_debug/selsis_ff/40/atm.nc"]
- linesearch = false
- converge_atol = 1.0e-2
- converge_rtol = 1.0e-4
-
-
-[plots]
- at_runtime = true
- temperature = true
- fluxes = true
- contribution = true
- emission = true
- albedo = true
- mixing_ratios = true
- animate = true
-
diff --git a/res/stellar_spectra/_readme.txt b/res/stellar_spectra/_readme.txt
new file mode 100644
index 00000000..d5b1ba48
--- /dev/null
+++ b/res/stellar_spectra/_readme.txt
@@ -0,0 +1,8 @@
+All spectra scaled to 1 AU.
+
+Solar spectrum from Gueymard
+ https://www.nrel.gov/grid/solar-resource/spectra.html
+
+All other spectra from MUSCLES
+ https://archive.stsci.edu/prepds/muscles/#dataaccess
+
diff --git a/res/surface_albedo/_readme.txt b/res/surface_albedo/_readme.txt
new file mode 100644
index 00000000..08432e71
--- /dev/null
+++ b/res/surface_albedo/_readme.txt
@@ -0,0 +1,17 @@
+Surface reflectance (albedo) lookup data. These are passed to SOCRATES `rho_alb`
+variable, assuming no angular dependence of the reflectivity.
+
+All data must be in columns of:
+ wavelength [nm]
+ reflectance [dimensionless]
+
+In order of ascending wavelength.
+
+c9mb29.tab
+ Lunar Meteorite, Igneous, Gabbroic
+ https://pds-speclib.rsl.wustl.edu/measurement.aspx?lid=urn:nasa:pds:relab:data_reflectance:c9mb29
+
+c1bf01.tab
+ Terrestrial Sample, Igneous, Basaltic
+ https://pds-speclib.rsl.wustl.edu/measurement.aspx?lid=urn:nasa:pds:relab:data_reflectance:c1bf01
+
diff --git a/res/surface_albedo/c1bf01.tab b/res/surface_albedo/c1bf01.tab
new file mode 100644
index 00000000..c2b12c1b
--- /dev/null
+++ b/res/surface_albedo/c1bf01.tab
@@ -0,0 +1,224 @@
+320.0 0.08862
+330.0 0.09686
+340.0 0.10489
+350.0 0.11280
+360.0 0.12213
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+600.0 0.26293
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+650.0 0.27115
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+680.0 0.27689
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+730.0 0.28755
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+1250.0 0.31974
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diff --git a/res/surface_albedo/c9mb29.tab b/res/surface_albedo/c9mb29.tab
new file mode 100644
index 00000000..5fd59ac4
--- /dev/null
+++ b/res/surface_albedo/c9mb29.tab
@@ -0,0 +1,461 @@
+300.0 0.02854 0.00277
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diff --git a/res/thermo/C2H4.nc b/res/thermodynamics/C2H4.nc
similarity index 100%
rename from res/thermo/C2H4.nc
rename to res/thermodynamics/C2H4.nc
diff --git a/res/thermo/CH3.nc b/res/thermodynamics/CH3.nc
similarity index 100%
rename from res/thermo/CH3.nc
rename to res/thermodynamics/CH3.nc
diff --git a/res/thermo/CH4.nc b/res/thermodynamics/CH4.nc
similarity index 100%
rename from res/thermo/CH4.nc
rename to res/thermodynamics/CH4.nc
diff --git a/res/thermo/CO.nc b/res/thermodynamics/CO.nc
similarity index 100%
rename from res/thermo/CO.nc
rename to res/thermodynamics/CO.nc
diff --git a/res/thermo/CO2.nc b/res/thermodynamics/CO2.nc
similarity index 100%
rename from res/thermo/CO2.nc
rename to res/thermodynamics/CO2.nc
diff --git a/res/thermo/FeS.nc b/res/thermodynamics/FeS.nc
similarity index 100%
rename from res/thermo/FeS.nc
rename to res/thermodynamics/FeS.nc
diff --git a/res/thermo/H2.nc b/res/thermodynamics/H2.nc
similarity index 100%
rename from res/thermo/H2.nc
rename to res/thermodynamics/H2.nc
diff --git a/res/thermo/H2O.nc b/res/thermodynamics/H2O.nc
similarity index 100%
rename from res/thermo/H2O.nc
rename to res/thermodynamics/H2O.nc
diff --git a/res/thermo/H2O2.nc b/res/thermodynamics/H2O2.nc
similarity index 100%
rename from res/thermo/H2O2.nc
rename to res/thermodynamics/H2O2.nc
diff --git a/res/thermo/H2S.nc b/res/thermodynamics/H2S.nc
similarity index 100%
rename from res/thermo/H2S.nc
rename to res/thermodynamics/H2S.nc
diff --git a/res/thermo/H2SO4.nc b/res/thermodynamics/H2SO4.nc
similarity index 100%
rename from res/thermo/H2SO4.nc
rename to res/thermodynamics/H2SO4.nc
diff --git a/res/thermo/HCN.nc b/res/thermodynamics/HCN.nc
similarity index 100%
rename from res/thermo/HCN.nc
rename to res/thermodynamics/HCN.nc
diff --git a/res/thermo/HNO3.nc b/res/thermodynamics/HNO3.nc
similarity index 100%
rename from res/thermo/HNO3.nc
rename to res/thermodynamics/HNO3.nc
diff --git a/res/thermo/N2.nc b/res/thermodynamics/N2.nc
similarity index 100%
rename from res/thermo/N2.nc
rename to res/thermodynamics/N2.nc
diff --git a/res/thermo/N2O.nc b/res/thermodynamics/N2O.nc
similarity index 100%
rename from res/thermo/N2O.nc
rename to res/thermodynamics/N2O.nc
diff --git a/res/thermo/NH3.nc b/res/thermodynamics/NH3.nc
similarity index 100%
rename from res/thermo/NH3.nc
rename to res/thermodynamics/NH3.nc
diff --git a/res/thermo/NO.nc b/res/thermodynamics/NO.nc
similarity index 100%
rename from res/thermo/NO.nc
rename to res/thermodynamics/NO.nc
diff --git a/res/thermo/O2.nc b/res/thermodynamics/O2.nc
similarity index 100%
rename from res/thermo/O2.nc
rename to res/thermodynamics/O2.nc
diff --git a/res/thermo/O3.nc b/res/thermodynamics/O3.nc
similarity index 100%
rename from res/thermo/O3.nc
rename to res/thermodynamics/O3.nc
diff --git a/res/thermo/OCS.nc b/res/thermodynamics/OCS.nc
similarity index 100%
rename from res/thermo/OCS.nc
rename to res/thermodynamics/OCS.nc
diff --git a/res/thermo/OH.nc b/res/thermodynamics/OH.nc
similarity index 100%
rename from res/thermo/OH.nc
rename to res/thermodynamics/OH.nc
diff --git a/res/thermo/S2.nc b/res/thermodynamics/S2.nc
similarity index 100%
rename from res/thermo/S2.nc
rename to res/thermodynamics/S2.nc
diff --git a/res/thermo/S8.nc b/res/thermodynamics/S8.nc
similarity index 100%
rename from res/thermo/S8.nc
rename to res/thermodynamics/S8.nc
diff --git a/res/thermo/SO.nc b/res/thermodynamics/SO.nc
similarity index 100%
rename from res/thermo/SO.nc
rename to res/thermodynamics/SO.nc
diff --git a/res/thermo/SO2.nc b/res/thermodynamics/SO2.nc
similarity index 100%
rename from res/thermo/SO2.nc
rename to res/thermodynamics/SO2.nc
diff --git a/res/thermo/SiO.nc b/res/thermodynamics/SiO.nc
similarity index 100%
rename from res/thermo/SiO.nc
rename to res/thermodynamics/SiO.nc
diff --git a/res/thermo/SiO2.nc b/res/thermodynamics/SiO2.nc
similarity index 100%
rename from res/thermo/SiO2.nc
rename to res/thermodynamics/SiO2.nc
diff --git a/res/thermo/_readme.txt b/res/thermodynamics/_readme.txt
similarity index 100%
rename from res/thermo/_readme.txt
rename to res/thermodynamics/_readme.txt
diff --git a/res/thermo/standard.txt b/res/thermodynamics/standard.txt
similarity index 100%
rename from res/thermo/standard.txt
rename to res/thermodynamics/standard.txt
diff --git a/src/AGNI.jl b/src/AGNI.jl
index 5a7670a0..292178c4 100755
--- a/src/AGNI.jl
+++ b/src/AGNI.jl
@@ -205,17 +205,26 @@ module AGNI
# Copy configuration file
cp(cfg_path, joinpath(output_dir, "agni.cfg"), force=true)
- # Read REQUIRED configuration options from dict
+ # Read configuration options from dict
@info "Using configuration '$(cfg["title"])'"
# planet stuff
tmp_surf::Float64 = cfg["planet"]["tmp_surf"]
instellation::Float64 = cfg["planet"]["instellation"]
albedo_b::Float64 = cfg["planet"]["albedo_b"]
- albedo_s::Float64 = cfg["planet"]["albedo_s"]
asf_sf::Float64 = cfg["planet"]["s0_fact"]
radius::Float64 = cfg["planet"]["radius"]
zenith::Float64 = cfg["planet"]["zenith_angle"]
gravity::Float64 = cfg["planet"]["gravity"]
+
+ albedo_s::Float64 = 0.0
+ surface_mat::String = cfg["planet"]["surface_material"]
+ if surface_mat == "blackbody"
+ if !haskey(cfg["planet"],"albedo_s")
+ @error "Misconfiguration: surface is blackbody , so `albedo_s` must be provided"
+ return false
+ end
+ albedo_s = cfg["planet"]["albedo_s"]
+ end
# composition stuff
p_top::Float64 = cfg["composition"]["p_top"]
@@ -235,7 +244,7 @@ module AGNI
mf_path = cfg["files"]["input_vmr"]
else
@error "Misconfiguration: if providing p_surf, must also provide VMRs"
- exit(1)
+ return false
end
p_surf = cfg["composition"]["p_surf"]
@@ -251,12 +260,12 @@ module AGNI
else
@error "Misconfiguration: must provide either p_dict or p_surf+VMRs"
- exit(1)
+ return false
end
if chem_type in [1,2,3]
if length(condensates)>0
@error "Misconfiguration: FastChem coupling is incompatible with AGNI condensation scheme"
- exit(1)
+ return false
else
mkdir(dir_fastchem)
end
@@ -337,7 +346,9 @@ module AGNI
overlap_method=overlap,
skin_d=skin_d, skin_k=skin_k, tmp_magma=tmp_magma,
target_olr=target_olr,
- tmp_int=tmp_int, albedo_s=albedo_s,
+ tmp_int=tmp_int,
+ surface_material=surface_mat,
+ albedo_s=albedo_s,
thermo_functions=thermo_funct,
C_d=turb_coeff, U=wind_speed,
use_all_gases=use_all_gases
@@ -489,7 +500,6 @@ module AGNI
# Save plots
@info "Plotting results"
- plt_alb = plt_alb && (flag_cld || flag_ray)
flag_cld && plotting.plot_cloud(atmos, joinpath(atmos.OUT_DIR,"plot_cloud.png"))
diff --git a/src/atmosphere.jl b/src/atmosphere.jl
index 5c3a311f..5f223abd 100644
--- a/src/atmosphere.jl
+++ b/src/atmosphere.jl
@@ -67,11 +67,15 @@ module atmosphere
spectral_file::String # Path to spectral file
star_file::String # Path to star spectrum
albedo_b::Float64 # Enforced bond albedo
- albedo_s::Float64 # Surface albedo
zenith_degrees::Float64 # Solar zenith angle [deg]
toa_heating::Float64 # Derived downward shortwave radiation flux at topmost level [W m-2]
instellation::Float64 # Solar flux at top of atmopshere [W m-2]
s0_fact::Float64 # Scale factor to instellation (cronin+14)
+
+ surface_material::String # Surface material file path
+ albedo_s::Float64 # Grey surface albedo when surface=blackbody
+ albedo_s_arr::Array{Float64,1} # Spectral surface albedo passed to SOCRATES
+
tmp_surf::Float64 # Surface brightness temperature [K]
tmp_int::Float64 # Effective temperature of the planet [K]
grav_surf::Float64 # Surface gravity [m s-2]
@@ -246,8 +250,9 @@ module atmosphere
- `mf_path::String` path to file containing VMRs at each level.
Optional arguments:
- - `condensates::Array{String,1}` list of condensates (names)
- - `albedo_s::Float64` surface albedo.
+ - `condensates::Array{String,1}` list of condensates (gas names).
+ - `surface_material::String` surface material (default is "blackbody", but can point to file instead).
+ - `albedo_s::Float64` grey surface albedo used when `surface_material="blackbody"`.
- `tmp_floor::Float64` temperature floor [K].
- `C_d::Float64` turbulent heat exchange coefficient [dimensionless].
- `U::Float64` surface wind speed [m s-1].
@@ -279,6 +284,7 @@ module atmosphere
mf_dict::Dict{String, Float64}, mf_path::String;
condensates::Array{String,1} = String[],
+ surface_material::String = "blackbody",
albedo_s::Float64 = 0.0,
tmp_floor::Float64 = 2.0,
C_d::Float64 = 0.001,
@@ -324,7 +330,7 @@ module atmosphere
# Set the parameters (and make sure that they're reasonable)
atmos.ROOT_DIR = abspath(ROOT_DIR)
atmos.OUT_DIR = abspath(OUT_DIR)
- atmos.THERMO_DIR = joinpath(atmos.ROOT_DIR, "res", "thermo")
+ atmos.THERMO_DIR = joinpath(atmos.ROOT_DIR, "res", "thermodynamics")
atmos.spectral_file = abspath(spfile)
atmos.all_channels = all_channels
atmos.overlap_method = overlap_method
@@ -344,6 +350,7 @@ module atmosphere
atmos.tmp_int = tmp_int
atmos.grav_surf = max(1.0e-7, gravity)
atmos.zenith_degrees = max(min(zenith_degrees,89.8), 0.2)
+ atmos.surface_material= surface_material
atmos.albedo_s = max(min(albedo_s, 1.0 ), 0.0)
atmos.instellation = max(instellation, 0.0)
atmos.albedo_b = max(min(albedo_b,1.0), 0.0)
@@ -793,7 +800,7 @@ module atmosphere
- `atmos::Atmos_t` the atmosphere struct instance to be used.
- `boundary_scale::Float64=1.0e-4` scale factor for the thickness of the bottom-most cell.
"""
- function generate_pgrid!(atmos::atmosphere.Atmos_t; boundary_scale::Float64=1.0e-4)
+ function generate_pgrid!(atmos::atmosphere.Atmos_t; boundary_scale::Float64=1.0e-6)
boundary_scale = max(min(boundary_scale, 1.0-1.0e-8), 1.0e-8)
@@ -977,18 +984,27 @@ module atmosphere
atmos.dimen.nd_subcol_gen = 1
atmos.dimen.nd_subcol_req = 1
atmos.dimen.nd_aerosol_mode = 1
+
+ # Set to true to enable custom surface emission through the
+ # variables `planck%flux_ground(l)` and `d_planck_flux_surface`.
+ # atmos.control.l_flux_ground = false
SOCRATES.allocate_atm( atmos.atm, atmos.dimen, atmos.spectrum)
+ SOCRATES.allocate_cld( atmos.cld, atmos.dimen, atmos.spectrum)
SOCRATES.allocate_aer( atmos.aer, atmos.dimen, atmos.spectrum)
SOCRATES.allocate_bound(atmos.bound, atmos.dimen, atmos.spectrum)
- atmos.bound.rho_alb[:, SOCRATES.rad_pcf.ip_surf_alb_diff, :] .= atmos.albedo_s # Set surface albedo.
+ # This defines the surface emission, once a custom value is enabled.
+ # fill!(atmos.bound.flux_ground, 100.0)
- # atm sizes and coordinates
- atmos.atm.n_layer = npd_layer
- atmos.atm.n_profile = 1
- atmos.atm.lat[1] = 0.0
- atmos.atm.lon[1] = 0.0
+
+ ###########################################
+ # Number of profiles, and profile coordinates
+ ###########################################
+ atmos.atm.n_layer = npd_layer
+ atmos.atm.n_profile = 1
+ atmos.atm.lat[1] = 0.0
+ atmos.atm.lon[1] = 0.0
###########################################
# Range of bands
@@ -1006,7 +1022,7 @@ module atmosphere
end
SOCRATES.allocate_control(atmos.control, atmos.spectrum)
-
+
if n_channel == 1
atmos.control.map_channel[1:atmos.spectrum.Basic.n_band] .= 1
elseif n_channel == atmos.spectrum.Basic.n_band
@@ -1112,8 +1128,8 @@ module atmosphere
end
- # Calc layer properties using initial temperature profile
- # Can generate weird issues since the TOA temperature can be large
+ # Calc layer properties using initial temperature profile.
+ # Can generate weird issues since the TOA temperature may be large
# large but pressure small, which gives it a low density. With the
# hydrostatic integrator, this can cause dz to blow up, especially
# with a low MMW gas. Should be okay as long as the T(p) provided
@@ -1161,7 +1177,6 @@ module atmosphere
atmos.control.i_cloud = SOCRATES.rad_pcf.ip_cloud_off # 5 (clear sky)
end
- SOCRATES.allocate_cld( atmos.cld, atmos.dimen, atmos.spectrum)
SOCRATES.allocate_cld_prsc(atmos.cld, atmos.dimen, atmos.spectrum)
if atmos.control.l_cloud
@@ -1177,6 +1192,48 @@ module atmosphere
atmos.control.i_angular_integration = SOCRATES.rad_pcf.ip_two_stream
+ ###########################################
+ # Surface properties
+ ###########################################
+ atmos.albedo_s_arr = zeros(Float64, atmos.nbands)
+
+ if atmos.surface_material == "blackbody"
+ # grey albedo
+ fill!(atmos.albedo_s_arr, atmos.albedo_s)
+
+ else
+ # spectral albedo
+
+ # try to find a matching file
+ atmos.surface_material = abspath(atmos.surface_material)
+ if !isfile(atmos.surface_material)
+ error("Could not find surface albedo file '$(atmos.surface_material)'")
+ end
+
+ # read data from file
+ _alb_data::Array = readdlm(atmos.surface_material, Float64)
+ _alb_w::Array{Float64, 1} = _alb_data[:,1] # wavelength [nm]
+ _alb_a::Array{Float64, 1} = _alb_data[:,2] # albedo [dimensionless]
+
+ # extrapolate to 0 wavelength, with constant value
+ pushfirst!(_alb_w, 0.0)
+ pushfirst!(_alb_a, _alb_a[1])
+
+ # extrapolate to large wavelength, with constant value
+ push!(_alb_w, 1e10)
+ push!(_alb_a, _alb_a[end])
+
+ # create interpolator
+ _alb_itp::Interpolator = Interpolator(_alb_w, _alb_a)
+
+ # use interpolator to fill band values
+ for i in 1:atmos.nbands
+ # evaluate at band centre, converting from m to nm
+ atmos.albedo_s_arr[i] = _alb_itp(0.5 * (atmos.bands_min[i] + atmos.bands_max[i]) * 1.0e9)
+ end
+
+ end
+
#######################################
# Output arrays
#######################################
diff --git a/src/dump.jl b/src/dump.jl
index 56ed9dcb..b5436af5 100644
--- a/src/dump.jl
+++ b/src/dump.jl
@@ -135,7 +135,6 @@ module dump
var_tmax = defVar(ds, "tceiling", Float64, (), attrib = OrderedDict("units" => "K")) # Maximum temperature
var_plrad = defVar(ds, "planet_radius", Float64, (), attrib = OrderedDict("units" => "m")) # Value taken for planet radius
var_gsurf = defVar(ds, "surf_gravity", Float64, (), attrib = OrderedDict("units" => "m s-2")) # Surface gravity
- var_albsurf = defVar(ds, "surf_albedo", Float64, ()) # Surface albedo
var_fray = defVar(ds, "flag_rayleigh", Char, ()) # Includes rayleigh scattering?
var_fcon = defVar(ds, "flag_continuum",Char, ()) # Includes continuum absorption?
var_fcld = defVar(ds, "flag_cloud" ,Char, ()) # Includes clouds?
@@ -161,7 +160,6 @@ module dump
var_tmax[1] = atmos.tmp_ceiling
var_plrad[1] = atmos.rp
var_gsurf[1] = atmos.grav_surf
- var_albsurf[1] = atmos.albedo_s
if atmos.control.l_rayleigh
var_fray[1] = 'y'
@@ -246,6 +244,7 @@ module dump
var_bus = defVar(ds, "ba_U_SW", Float64, ("nbands","nlev_l"), attrib = OrderedDict("units" => "W m-2"))
var_bns = defVar(ds, "ba_N_SW", Float64, ("nbands","nlev_l"), attrib = OrderedDict("units" => "W m-2"))
var_cfn = defVar(ds, "contfunc", Float64, ("nbands","nlev_c"))
+ var_albs = defVar(ds, "albedo_s", Float64, ("nbands",))
# Store data
var_p[:] = atmos.p
@@ -275,8 +274,13 @@ module dump
end
end
+ # Clouds
var_cldl[:] = atmos.cloud_arr_l
+ # Kzz mixing
+ var_kzz[:] = atmos.Kzz
+
+ # Bolometric fluxes
var_fdl[:] = atmos.flux_d_lw
var_ful[:] = atmos.flux_u_lw
var_fnl[:] = atmos.flux_n_lw
@@ -297,8 +301,7 @@ module dump
var_hr[:] = atmos.heating_rate
var_fdiff[:] = atmos.flux_dif
- var_kzz[:] = atmos.Kzz
-
+ # Spectral fluxes
var_bmin[:] = atmos.bands_min
var_bmax[:] = atmos.bands_max
@@ -319,6 +322,9 @@ module dump
end
end
+ # Surface spectral albedo
+ var_albs[:] = atmos.albedo_s_arr
+
close(ds)
end # suppress output
@debug "ALL OUTPUT RESTORED"
diff --git a/src/energy.jl b/src/energy.jl
index 5282c341..0c486f3f 100644
--- a/src/energy.jl
+++ b/src/energy.jl
@@ -62,13 +62,16 @@ module energy
if !Bool(atmos.spectrum.Basic.l_present[2])
error("The spectral file contains no solar spectral data.")
end
+
+ # Downward SW flux at TOA
+ atmos.toa_heating = atmos.instellation * (1.0 - atmos.albedo_b) * atmos.s0_fact * cosd(atmos.zenith_degrees)
- atmos.bound.zen_0[1] = 1.0/cosd(atmos.zenith_degrees) # Convert the zenith angles to secants.
- atmos.bound.solar_irrad[1] = atmos.toa_heating / cosd(atmos.zenith_degrees)
+ # SOCRATES requires this to be passed as two variables, since it
+ # needs to know the angle of the direct beam.
+ atmos.bound.zen_0[1] = 1.0/cosd(atmos.zenith_degrees) # Convert the zenith angles to secants.
+ atmos.bound.solar_irrad[1] = atmos.instellation * (1.0 - atmos.albedo_b) * atmos.s0_fact
end
- atmos.bound.rho_alb[:, atmosphere.SOCRATES.rad_pcf.ip_surf_alb_diff, :] .= atmos.albedo_s
-
# Set the two-stream approximation to be used (-t f)
if lw
atmos.control.i_2stream = atmosphere.SOCRATES.rad_pcf.ip_elsasser
@@ -117,11 +120,10 @@ module energy
# IP_surface_char = 51, file suffix 'surf'
#####################################
- if atmos.control.i_angular_integration == atmosphere.SOCRATES.rad_pcf.ip_two_stream
- if !lw
- atmos.bound.rho_alb[:, atmosphere.SOCRATES.rad_pcf.ip_surf_alb_dir, :] .= atmos.bound.rho_alb[:, atmosphere.SOCRATES.rad_pcf.ip_surf_alb_diff, :]
- end
- end
+ # set albedos
+ fill!(atmos.bound.rho_alb, 0.0)
+ atmos.bound.rho_alb[1, atmosphere.SOCRATES.rad_pcf.ip_surf_alb_diff, :] .= atmos.albedo_s_arr
+ atmos.bound.rho_alb[1, atmosphere.SOCRATES.rad_pcf.ip_surf_alb_dir, :] .= atmos.albedo_s_arr
###################################################
# Cloud information
@@ -178,6 +180,7 @@ module energy
end
end
+
# Do radiative transfer
atmosphere.atmosphere.SOCRATES.radiance_calc(atmos.control, atmos.dimen, atmos.spectrum,
atmos.atm, atmos.cld, atmos.aer,
@@ -277,6 +280,7 @@ module energy
return nothing
end
+
# Calculate conductive fluxes
function conduct!(atmos::atmosphere.Atmos_t)
# top layer
@@ -293,8 +297,6 @@ module energy
end
-
-
"""
**Calculate dry convective fluxes using mixing length theory.**
diff --git a/src/plotting.jl b/src/plotting.jl
index 359dd7a9..57150fb6 100644
--- a/src/plotting.jl
+++ b/src/plotting.jl
@@ -496,12 +496,13 @@ module plotting
# x value - band centres [nm]
x[ba] = 0.5 * (atmos.bands_min[ba] + atmos.bands_max[ba]) * 1.0e9
- # y value - spectral albedo [dimensionless]
+ # y value - spectral albedo [percentage]
y[ba] = 100.0 * atmos.band_u_sw[1, ba]/atmos.band_d_sw[1, ba]
end
# Make plot
- plt = plot(dpi=dpi)
+ ylims = (0.0, 100.0)
+ plt = plot(dpi=dpi, ylims=ylims)
plot!(plt, x, y, label="", color="black")
@@ -519,7 +520,7 @@ module plotting
end
"""
- Combined plot
+ Combined plot used for tracking behaviour of the solver
"""
function combined(plt_pt, plt_fl, plt_mr, info::String, fname::String; dpi::Int=180, size_x::Int=800, size_y::Int=650)
diff --git a/test/runtests.jl b/test/runtests.jl
index 77c9efea..462a4cc2 100755
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -45,7 +45,7 @@ passing = true
# -------------
@info " "
@info "Testing heat capacity functions"
-data_H2O::phys.Gas_t = phys.load_gas("res/thermo/", "H2O", true)
+data_H2O::phys.Gas_t = phys.load_gas("res/thermodynamics/", "H2O", true)
c_expt::Array{Float64, 1} = [4.975, 35.22, 41.27 , 51.20 , 55.74 , 59.40 ] # Expected values of cp [J mol-1 K-1]
t_test::Array{Float64, 1} = [10.0, 500.0, 1000.0, 2000.0, 3000.0, 5000.0] # Tested values of temperature
cp_pass = true
@@ -62,6 +62,7 @@ else
@warn "Fail"
passing = false
end
+@info "--------------------------"
@@ -72,9 +73,9 @@ end
@info " "
@info "Testing composition"
-tmp_surf = 200.0 # Surface temperature [kelvin]
-toa_heating = 1000.00 # Instellation flux [W m-2]
-p_surf = 50.0 # bar
+tmp_surf = 200.0 # Surface temperature [kelvin]
+toa_heating = 10000.00 # Instellation flux [W m-2]
+p_surf = 1.0 # bar
theta = 65.0
mf_dict = Dict([
("H2O" , 0.5),
@@ -112,7 +113,7 @@ else
passing = false
end
atmosphere.deallocate!(atmos)
-
+@info "--------------------------"
@@ -123,7 +124,7 @@ atmosphere.deallocate!(atmos)
@info " "
@info "Testing instellation"
-tmp_surf = 200.0 # Surface temperature [kelvin]
+tmp_surf = 200.0 # Surface temperature [kelvin]
toa_heating = 1000.00 # Instellation flux [W m-2]
p_surf = 50.0 # bar
theta = 65.0
@@ -178,6 +179,9 @@ else
passing = false
end
atmosphere.deallocate!(atmos)
+@info "--------------------------"
+
+
# -------------
# Test greenhouse effect
@@ -192,7 +196,7 @@ theta = 45.0
mf_dict = Dict([
("H2O" , 1.0),
])
-spfile_name = joinpath(ROOT_DIR,"res/spectral_files/Oak/Oak.sf")
+spfile_name = joinpath(ROOT_DIR,"res/spectral_files/Oak/318/Oak.sf")
# Setup atmosphere
atmos = atmosphere.Atmos_t()
@@ -206,16 +210,18 @@ atmosphere.setup!(atmos, ROOT_DIR, output_dir,
flag_gcontinuum=true,
flag_rayleigh=false,
overlap_method=4,
- condensates=["H2O"]
+ condensates=["H2O"],
+ surface_material="blackbody",
+ albedo_s=0.5
)
atmosphere.allocate!(atmos,joinpath(ROOT_DIR,"res/stellar_spectra/sun.txt"))
setpt.prevent_surfsupersat!(atmos)
setpt.dry_adiabat!(atmos)
setpt.saturation!(atmos, "H2O")
atmosphere.calc_layer_props!(atmos)
-energy.radtrans!(atmos, true)
+energy.radtrans!(atmos, true) # LW only
-val_e = [270.0, 290.0]
+val_e = [270.0, 280.0]
val_o = atmos.flux_u_lw[1]
@info "Expected range = $(val_e) W m-2"
@info "Modelled value = $(val_o) W m-2"
@@ -225,8 +231,29 @@ else
@warn "Fail"
passing = false
end
-atmosphere.deallocate!(atmos)
+@info "--------------------------"
+
+
+# -------------
+# Test surface albedo
+# -------------
+@info " "
+@info "Testing surface albedo "
+energy.radtrans!(atmos, false) # SW only
+
+val_e = [20.0, 40.0] # known from previous tests
+val_o = atmos.flux_u_sw[end] # bottom level
+@info "Expected range = $(val_e) W m-2"
+@info "Modelled value = $(val_o) W m-2"
+if ( val_o > val_e[1]) && (val_o < val_e[2])
+ @info "Pass"
+else
+ @warn "Fail"
+ passing = false
+end
+atmosphere.deallocate!(atmos)
+@info "--------------------------"
# -------------
@@ -273,8 +300,7 @@ else
passing = false
end
atmosphere.deallocate!(atmos)
-
-
+@info "--------------------------"
# -------------
@@ -283,14 +309,14 @@ atmosphere.deallocate!(atmos)
@info " "
@info "Testing heating rates"
-tmp_surf = 2500.0 # Surface temperature [kelvin]
+tmp_surf = 2500.0 # Surface temperature [kelvin]
toa_heating = 1000.0 # Instellation flux [W m-2]
p_surf = 5.0 # bar
theta = 45.0
mf_dict = Dict([
("H2O" , 1.0)
])
-spfile_name = joinpath(ROOT_DIR,"res/spectral_files/Oak/Oak.sf")
+spfile_name = joinpath(ROOT_DIR,"res/spectral_files/Oak/318/Oak.sf")
# Setup atmosphere
atmos = atmosphere.Atmos_t()
@@ -325,6 +351,7 @@ else
passing = false
end
atmosphere.deallocate!(atmos)
+@info "--------------------------"
# -------------
diff --git a/tutorials/01_canonical-runaway.ipynb b/tutorials/01_canonical-runaway.ipynb
index 2bcd8d70..88add1a4 100644
--- a/tutorials/01_canonical-runaway.ipynb
+++ b/tutorials/01_canonical-runaway.ipynb
@@ -109,7 +109,7 @@
"mole_fractions = Dict([(\"H2O\", 1.0)])\n",
"run_len = 23\n",
"\n",
- "spectral_file = joinpath(ROOT_DIR,\"res/spectral_files/Oak/Oak.sf\")\n",
+ "spectral_file = joinpath(ROOT_DIR,\"res/spectral_files/Oak/318/Oak.sf\")\n",
"star_file = joinpath(ROOT_DIR,\"res/stellar_spectra/sun.txt\")\n",
"output_dir = joinpath(ROOT_DIR,\"out/\")"
]
@@ -248,6 +248,13 @@
"### Save these data to a CSV"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 8,