Comparison of the ExoJAX AM cloud model with VIRGA
==================================================

March 9th in 2024, Hajime Kawahara

Here, we try to compare our cloud implementation using Ackerman and
Marley Model with VIRGA. We consider the enstatite (MgSiO3) cloud.

.. code:: ipython3

    import jax.numpy as jnp
    import matplotlib.pyplot as plt
    import numpy as np
    import virga.justdoit as jdi

.. code:: ipython3

    # set common values
    from exojax.utils.astrofunc import gravity_jupiter
    fsed = 3.0
    gravity = gravity_jupiter(1.0,1.0)
    mu=2.2
    
    import virga.justplotit as jpi
    import astropy.units as u
    fsed = 3
    miedir = "/home/kawahara/exojax/documents/tutorials/.database/particulates/virga"
    #miedir = "/home/exoplanet01/exojax/documents/tutorials/.database/particulates/virga"
    #miedir = "/home/exoplanet01/exojax/tests/integration/comparison/clouds/.database/particulates/virga"
    a = jdi.Atmosphere(["MgSiO3"], fsed = fsed, mh=1.0, mmw=mu)
    a.gravity(gravity=gravity, gravity_unit=u.Unit('cm/(s**2)'))
    a.ptk(df = jdi.hot_jupiter())
    all_out = jdi.compute(a, as_dict=True, directory = miedir)
    
    pressure = all_out["pressure"]
    temperature = all_out["temperature"]

.. code:: ipython3

    #comparison w/ kawashima's vapor pressure code
    def T_MgSiO3(P,metaldex):
        return 1e4 / (6.26 - 0.35 * np.log10(P) - 0.70 * metaldex) # Visscher et al. (2010)
    def T_MnS(P,metaldex):
        return 1e4 / (7.447 - 0.42 * np.log10(P) - 0.84 * metaldex) # Morley et al. (2012)

Setting a simple atmopheric model. We need the density of atmosphere.

.. code:: ipython3

    from exojax.utils.constants import kB, m_u
    from exojax.atm.atmprof import pressure_layer_logspace
    
    #Parr, dParr, k = pressure_layer_logspace(log_pressure_top=-5., log_pressure_btm=4.0, nlayer=100)
    #alpha = 0.097
    #T0 = 1200.
    #Tarr = T0 * (Parr)**alpha
    
    mu = 2.0  # mean molecular weight
    R = kB / (mu * m_u)
    rho = pressure / (R * temperature)
    


We import ``pdb`` as the particulate database class.

.. code:: ipython3

    from exojax.spec.pardb import PdbCloud
    
    pdb = PdbCloud("MgSiO3", path=miedir)
    #pdb.generate_miegrid()
    #pdb.load_miegrid()
    #print(len(pdb.rg_arr), len(pdb.sigmag_arr))



.. parsed-literal::

    /home/kawahara/exojax/documents/tutorials/.database/particulates/virga/virga.zip  exists. Remove it if you wanna re-download and unzip.
    Refractive index file found:  /home/kawahara/exojax/documents/tutorials/.database/particulates/virga/MgSiO3.refrind
    Miegrid file does not exist at  /home/kawahara/exojax/documents/tutorials/.database/particulates/virga/miegrid_lognorm_MgSiO3.mg.npz
    Generate miegrid file using pdb.generate_miegrid if you use Mie scattering


The solar abundance can be obtained using utils.zsol.nsol. Here, we
assume a maximum VMR for MgSiO3 from solar abundance.

.. code:: ipython3

    from exojax.utils.zsol import nsol
    
    n = nsol()  #solar abundance
    MolMR_enstatite = np.min([n["Mg"], n["Si"], n["O"] / 3])

Vapor saturation pressures can be obtained using
``saturation_pressure()`` instance in ``pdb`` (or one can directly call
``atm.psat`` instead).

.. code:: ipython3

    #from exojax.atm.psat import psat_enstatite_AM01
    #P_enstatite = psat_enstatite_AM01(temperature)
    P_enstatite = pdb.saturation_pressure(temperature)


Compute a cloud base pressure.

.. code:: ipython3

    
    from exojax.atm.amclouds import compute_cloud_base_pressure
    Pbase_enstatite = compute_cloud_base_pressure(pressure, P_enstatite, MolMR_enstatite)


.. code:: ipython3

    from bokeh.io import output_notebook
    from bokeh.plotting import show, figure
    from bokeh.palettes import Colorblind
    output_notebook()
    
    metallicity = 1 #atmospheric metallicity relative to Solar
    
    #get virga recommendation for which gases to run
    recommended = jdi.recommend_gas(pressure, temperature, metallicity,mu,
                    #Turn on plotting
                     plot=True)
    #print the results
    print(recommended)




.. raw:: html

    <div class="bk-root">
            <a href="https://bokeh.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a>
            <span id="1002">Loading BokehJS ...</span>
        </div>






.. raw:: html

    
    <div class="bk-root" id="23447e48-ca48-4922-bd4c-33022fad9515" data-root-id="1003"></div>





.. parsed-literal::

    ['Cr', 'Mg2SiO4', 'MgSiO3', 'MnS']


The cloud base is located at the intersection of a TP profile and the
vapor saturation puressure devided by VMR.

It’s consistent with VIRGA and Kawashima-san’s private code:

.. code:: ipython3

    plt.plot(temperature, pressure, color="black", ls="dashed", label="T - P profile")
    plt.plot(temperature,
             P_enstatite / MolMR_enstatite,
             label="$P_{sat}/\\xi$ (enstatite)",
             color="gray")
    parr=np.logspace(-3,2,100)
    plt.plot(T_MgSiO3(parr,0.0),parr,label="enstatite (kawashima)")
    plt.plot(T_MnS(parr,0.0),parr,label="MnS (kawashima)")
    
    plt.axhline(Pbase_enstatite, color="gray", alpha=0.7, ls="dotted")
    plt.text(500, Pbase_enstatite * 0.8, "cloud base (enstatite)", color="gray")
    
    plt.yscale("log")
    plt.ylim(1.e-3, 1.e2)
    plt.xlim(0, 3000)
    plt.gca().invert_yaxis()
    plt.legend()
    plt.xlabel("Temperature (K)")
    plt.ylabel("Pressure (bar)")
    plt.savefig("pbase.pdf", bbox_inches="tight", pad_inches=0.0)
    plt.savefig("pbase.png", bbox_inches="tight", pad_inches=0.0)
    plt.show()



.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_17_0.png


.. code:: ipython3

    #for key, val in all_out.items():
    #    print(key)

.. code:: ipython3

    con_mmr = all_out["condensate_mmr"]
    #con_mmr
    show(jpi.condensate_mmr(all_out))



.. raw:: html

    
    <div class="bk-root" id="1fa4f171-4659-46f1-a8af-019c0e451ad2" data-root-id="1691"></div>





Computes MMRs of clouds.

.. code:: ipython3

    from exojax.atm.amclouds import mixing_ratio_cloud_profile
    from exojax.atm.mixratio import vmr2mmr
    from exojax.spec.molinfo import molmass_isotope
    molmass_enstatite = molmass_isotope("MgSiO3", db_HIT=False)
    
    MMRbase_enstatite = vmr2mmr(MolMR_enstatite, molmass_enstatite, mu) 
    MMRc_enstatite = mixing_ratio_cloud_profile(pressure, Pbase_enstatite, fsed, MMRbase_enstatite)


.. code:: ipython3

    molmass_enstatite




.. parsed-literal::

    100.3887



Here is the MMR distribution.

.. code:: ipython3

    plt.figure()
    plt.gca().get_xaxis().get_major_formatter().set_powerlimits([-3, 3])
    plt.plot(MMRc_enstatite, pressure, color="gray", label="MMR (enstatite)")
    plt.plot(con_mmr,pressure, label="virga")
    plt.yscale("log")
    plt.ylim(1.e-7, 10000)
    plt.gca().invert_yaxis()
    plt.legend()
    plt.xlabel("MMR (clouds)")
    plt.ylabel("Pressure (bar)")
    plt.savefig("mmrcloud.pdf", bbox_inches="tight", pad_inches=0.0)
    plt.savefig("mmrcloud.png", bbox_inches="tight", pad_inches=0.0)
    plt.show()



.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_24_0.png


Compute dynamic viscosity in H2 atmosphere (cm/g/s)

.. code:: ipython3

    from exojax.atm.viscosity import eta_Rosner, calc_vfactor
    
    T = np.logspace(np.log10(1000), np.log10(2000))
    vfactor, Tr = calc_vfactor("H2")
    eta = eta_Rosner(T, vfactor)


.. code:: ipython3

    plt.plot(T, eta)
    plt.xscale("log")
    plt.yscale("log")
    plt.xlabel("Temperature (K)")
    plt.ylabel("Dynamic viscosity (cm/g/s)")
    plt.show()



.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_27_0.png


The pressure scale height can be computed using atm.atmprof.Hatm.

.. code:: ipython3

    from exojax.atm.atmprof import pressure_scale_height
    T = 1000  #K
    print("scale height=", pressure_scale_height(1.e5, T, mu), "cm")



.. parsed-literal::

    scale height= 415722.99317937146 cm


We need a substance density of condensates.

.. code:: ipython3

    from exojax.atm.condensate import condensate_substance_density, name2formula
    deltac_enstatite = condensate_substance_density[name2formula["enstatite"]]
    
    mu = molmass_isotope("H2")


Let’s compute the terminal velocity. We can compute the terminal
velocity of cloud particle using atm.vterm.vf. vmap is again applied to
vf.

.. code:: ipython3

    from exojax.atm.viscosity import calc_vfactor, eta_Rosner
    from exojax.atm.vterm import terminal_velocity
    from jax import vmap
    
    vfactor, trange = calc_vfactor(atm="H2")
    rarr = jnp.logspace(-6, 0, 2000)  #cm
    drho = deltac_enstatite - rho
    eta_fid = eta_Rosner(temperature, vfactor)
    
    #g = 1.e5
    vf_vmap = vmap(terminal_velocity, (None, None, 0, 0, 0))
    vfs = vf_vmap(rarr, gravity, eta_fid, drho, rho)


.. code:: ipython3

    Kz_virga = all_out["kz"]
    mpf = all_out["mean_particle_r"]
    deff = all_out["droplet_eff_r"]
    
    fig = plt.figure(figsize=(15,5))
    ax = fig.add_subplot(131)
    plt.gca().get_xaxis().get_major_formatter().set_powerlimits([-3, 3])
    plt.plot(Kz_virga,pressure)
    plt.yscale("log")
    plt.ylim(1.e-7, 10000)
    plt.gca().invert_yaxis()
    plt.legend()
    plt.xlabel("Kz "+str(all_out["kz_unit"]))
    plt.ylabel("Pressure (bar)")
    
    
    ax = fig.add_subplot(132)
    plt.gca().get_xaxis().get_major_formatter().set_powerlimits([-3, 3])
    plt.plot(mpf,pressure)
    plt.yscale("log")
    plt.ylim(1.e-7, 10000)
    plt.gca().invert_yaxis()
    plt.legend()
    plt.xlabel("mean particle radius " +str(all_out["r_units"]))
    
    ax = fig.add_subplot(133)
    plt.gca().get_xaxis().get_major_formatter().set_powerlimits([-3, 3])
    plt.plot(deff,pressure)
    plt.yscale("log")
    plt.ylim(1.e-7, 10000)
    plt.gca().invert_yaxis()
    plt.legend()
    plt.xlabel("droplet effective radius " +str(all_out["r_units"]))
    
    plt.show()


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.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_34_1.png


.. code:: ipython3

    plt.plot(temperature, pressure)
    plt.yscale("log")
    plt.ylim(1.e-7, 10000)
    plt.gca().invert_yaxis()




.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_35_0.png


Kzz/L will be used to calibrate :math:`r_w`. following Ackerman and
Marley 2001

.. code:: ipython3

    Kzz = 3.e10  #cm2/s
    sigmag = 2.0
    alphav = 1.3
    L = pressure_scale_height(gravity, 1500, mu)


.. code:: ipython3

    for i in range(0, len(temperature)):
        plt.plot(rarr, vfs[i, :], alpha=0.2, color="gray")
    plt.xscale("log")
    plt.yscale("log")
    plt.axhline(Kzz / L, label="Kzz/H", color="C2", ls="dotted")
    plt.ylabel("stokes terminal velocity (cm/s)")
    plt.xlabel("condensate size (cm)")




.. parsed-literal::

    Text(0.5, 0, 'condensate size (cm)')




.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_38_1.png


Find the intersection.

.. code:: ipython3

    from exojax.atm.amclouds import find_rw
    
    vfind_rw = vmap(find_rw, (None, 0, None), 0)
    rw = vfind_rw(rarr, vfs, Kzz / L)


Then, :math:`r_g` can be computed from :math:`r_w` and other quantities.

.. code:: ipython3

    from exojax.atm.amclouds import get_rg
    
    rg = get_rg(rw, fsed, alphav, sigmag)


.. code:: ipython3

    plt.plot(rg * 1.e4, pressure, label="$r=r_g$", color="black")
    plt.plot(rw * 1.e4, pressure, ls="dashed", label="$r=r_w$", color="black")
    plt.plot(mpf,pressure, label="mean particle radius")
    plt.plot(deff,pressure, label="droplet effective radius")
    
    plt.plot()
    plt.ylim(1.e-7, 10000)
    plt.xlabel("$r$ (micron)")
    plt.ylabel("Pressure (bar)")
    plt.yscale("log")
    plt.savefig("rgrw.png")
    plt.legend()




.. parsed-literal::

    <matplotlib.legend.Legend at 0x7f6b3260a140>




.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_43_1.png


This can be simply derived using ``AmpAmcloud``

.. code:: ipython3

    from exojax.atm.atmphys import AmpAmcloud
    
    amp = AmpAmcloud(pdb, bkgatm="H2")
    amp.check_temperature_range(temperature)
    fsed = 3.0
    sigmag = 2.0
    Kzz = 3.0e10
    
    rg_layer, MMR_enstatite = amp.calc_ammodel(pressure, temperature, mu, molmass_enstatite, gravity, fsed=fsed, sigmag=sigmag, Kzz=Kzz, MMR_base=MMRbase_enstatite)
    
    
    fig = plt.figure()
    ax = fig.add_subplot(121)
    plt.plot(rg_layer, pressure)
    plt.xlabel("rg (cm)")
    plt.ylabel("pressure (bar)")
    plt.yscale("log")
    ax.invert_yaxis()
    ax = fig.add_subplot(122)
    plt.plot(MMR_enstatite, pressure)
    plt.xlabel("condensate MMR")
    plt.yscale("log")
    # plt.xscale("log")
    ax.invert_yaxis()
    




.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_45_0.png


Mie Scattering
~~~~~~~~~~~~~~

.. code:: ipython3

    rg = np.median(rg_layer)
    
    #mieQpar = pdb.miegrid_interpolated_values(rg, sigmag)
    
    
    from exojax.utils.grids import wavenumber_grid
    from exojax.spec.unitconvert import wav2nu
    
    N = 40000
    wavelength_start = 8500.0 #AA
    wavelength_end = 8800.0 #AA
    
    margin = 10  # cm-1
    nus_start = wav2nu(wavelength_end, unit="AA") - margin
    nus_end = wav2nu(wavelength_start, unit="AA") + margin
    nugrid, wav, res = wavenumber_grid(nus_start, nus_end, N, xsmode="lpf", unit="cm-1")
    
    
    from exojax.spec.opacont import OpaMie
    opa = OpaMie(pdb, nugrid)
    #beta0, betasct, g = opa.mieparams_vector(rg,sigmag) # if you've already generated miegrid 
    beta0, betasct, g = opa.mieparams_vector_direct_from_pymiescatt(rg,sigmag) # uses direct computation of Mie params using PyMieScatt



.. parsed-literal::

    xsmode =  lpf
    xsmode assumes ESLOG in wavenumber space: mode=lpf
    ======================================================================
    The wavenumber grid should be in ascending order.
    The users can specify the order of the wavelength grid by themselves.
    Your wavelength grid is in ***  descending  *** order
    ======================================================================


.. parsed-literal::

    100%|██████████| 5/5 [00:02<00:00,  1.99it/s]


.. code:: ipython3

    fig = plt.figure()
    ax = fig.add_subplot(311)
    plt.plot(nugrid, g, color="black")
    plt.xscale("log")
    plt.ylabel("asymmetric $g$")
    ax = fig.add_subplot(312)
    plt.plot(nugrid, betasct/beta0, label="single scattering albedo", color="black")
    plt.xscale("log")
    plt.ylabel("$\\omega$")
    ax = fig.add_subplot(313)
    plt.plot(nugrid, beta0, label="\\beta_0", color="black")
    plt.xscale("log")
    plt.yscale("log")
    plt.xlabel("wavenumber (cm-1)")
    plt.ylabel("$\\beta_0$")





.. parsed-literal::

    Text(0, 0.5, '$\\beta_0$')




.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_48_1.png


.. code:: ipython3

    from exojax.spec.layeropacity import layer_optical_depth_clouds_lognormal
    from exojax.spec.layeropacity import layer_optical_depth_cloudgeo
    
    # set dParr by hand...
    logp = jnp.log(pressure)
    dlogp = 0.34755
    dParr = dlogp * pressure
    
    dtau_cloud = layer_optical_depth_clouds_lognormal(
        dParr, beta0.real, deltac_enstatite, MMR_enstatite, rg, sigmag, gravity
    )
    
    dtau_geo = layer_optical_depth_cloudgeo(
        dParr, deltac_enstatite, MMR_enstatite, rg, sigmag, gravity
    )

.. code:: ipython3

    from exojax.plot.atmplot import plotcf, plottau
    plotcf(nugrid, dtau_cloud, temperature, pressure, dParr, unit="nm")
    plt.show()
    plottau(nugrid, dtau_cloud, temperature, pressure, unit="nm", vmin=-8,vmax=3)
    plt.show()




.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_50_0.png



.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_50_1.png


Let’s compare the optical depth profile with each other.

.. code:: ipython3

    #virga
    pressure_ = all_out['pressure']
    opd_by_gas_ = all_out['opd_by_gas']
    
    
    import matplotlib.pyplot as plt
    fig = plt.figure()
    ax = fig.add_subplot(111)
    #plt.plot(np.mean(dtau_cloud,axis=1),pressure)
    plt.plot(np.cumsum(np.mean(dtau_cloud,axis=1)),pressure, label="ExoJAX/Mie")
    plt.plot(np.cumsum(dtau_geo),pressure,label="ExoJAX/geometric",ls="dashed")
    plt.plot(opd_by_gas_,pressure_,label="VIRGA", ls="dotted")
    plt.legend()
    plt.xlim(1.e-8,1.e4)
    ax.invert_yaxis()
    plt.yscale("log")
    plt.xscale("log")
    plt.xlabel("(wavenumber mean) optical depth")
    plt.savefig("comp_with_virga.png")




.. image:: amclouds_comparison_virga_files/amclouds_comparison_virga_52_0.png