4. Scattering calculations

This example demonstrates the basic principles of performing scattering calcualtions with ARTS. Populations of particles that scatter radiation are referred to as scattering species. Each scattering species defines a mapping from one or several fields of scattering species properties to scattering properties which form the input to the actual scattering calculation.

Set up a 1D atmosphere

import numpy as np
import pyarts

ws = pyarts.Workspace()
ws.surface_fieldSetPlanetEllipsoid(option="Earth")
ws.surface_field[pyarts.arts.SurfaceKey("t")] = 295.0
ws.atmospheric_fieldRead(
    toa=100e3, basename="planets/Earth/afgl/tropical/", missing_is_zero=1
)
ws.atmospheric_fieldIGRF(time="2000-03-11 14:39:37")

Add a field of scattering species properties

For this example, we will use scatterers with a Henyey-Greenstein phase function to represent ice particles. We will use two scattering species properties to represent the scattering species ice: The extinction and the single-scattering albed (SSA). The ice extinction and SSA thus become atmospheric fields. Below we define scattering-species-property object that identify these fields.

Ice

ice_extinction = pyarts.arts.ScatteringSpeciesProperty("ice", pyarts.arts.ParticulateProperty("Extinction"))
ice_ssa = pyarts.arts.ScatteringSpeciesProperty("ice", pyarts.arts.ParticulateProperty("SingleScatteringAlbedo"))

We then define a GriddedField3 representing the ice extinction and single-scattering albedo and add it to atm_field of the workspace.

grids = ws.atmospheric_field["t"].data.grids
z = grids[0]
ice_extinction_profile = np.zeros_like(z)
ice_extinction_profile[(z > 5e3) * (z < 15e3)] = 1.0
ice_extinction_profile = pyarts.arts.GriddedField3(data=ice_extinction_profile[..., None, None], grids=grids)
ws.atmospheric_field[ice_extinction] = ice_extinction_profile

ice_ssa_profile = np.zeros_like(z)
ice_ssa_profile[(z > 5e3) * (z < 15e3)] = 0.5
ice_ssa_profile = pyarts.arts.GriddedField3(data=ice_ssa_profile[..., None, None], grids=grids)
ws.atmospheric_field[ice_ssa] = ice_ssa_profile

Rain

rain_extinction = pyarts.arts.ScatteringSpeciesProperty("rain", pyarts.arts.ParticulateProperty("Extinction"))
rain_ssa = pyarts.arts.ScatteringSpeciesProperty("rain", pyarts.arts.ParticulateProperty("SingleScatteringAlbedo"))

rain_extinction_profile = np.zeros_like(z)
rain_extinction_profile[z < 5e3] = 1.0
rain_extinction_profile = pyarts.arts.GriddedField3(data=rain_extinction_profile[..., None, None], grids=grids)
ws.atmospheric_field[rain_extinction] = rain_extinction_profile

rain_ssa_profile = np.zeros_like(z)
rain_ssa_profile[z < 5e3] = 0.5
rain_ssa_profile = pyarts.arts.GriddedField3(data=rain_ssa_profile[..., None, None], grids=grids)
ws.atmospheric_field[rain_ssa] = rain_ssa_profile

Create the scattering species

from pyarts.arts import HenyeyGreensteinScatterer, ArrayOfScatteringSpecies

hg_ice = HenyeyGreensteinScatterer(ice_extinction, ice_ssa, 0.5)
hg_rain = HenyeyGreensteinScatterer(rain_extinction, rain_ssa, 0.0)
scattering_species = ArrayOfScatteringSpecies()
scattering_species.add(hg_ice)
scattering_species.add(hg_rain)

Extracting scattering properties

We can now extract bulk scattering properties from the scattering species.

Ice

We can extract the ice phase function from the combind scattering species by calculating the bulk-scattering properties at an altitude above 5 km. To check the consistency of the Henyey-Greenstein scatterer, we extract the data in gridded and spectral representation and ensure that they are the same when both are converted to gridded representation.

atm_pt = ws.atmospheric_field.at(11e3, 0.0, 0.0)
f_grid = np.array([89e9])
bsp = scattering_species.get_bulk_scattering_properties_tro_spectral(atm_pt, f_grid, 32, 1)
pm_spectral = bsp.phase_matrix

za_scat_grid = pm_spectral.to_gridded().get_za_scat_grid()
bsp = scattering_species.get_bulk_scattering_properties_tro_gridded(atm_pt, f_grid, za_scat_grid, 1)
pm_gridded = bsp.phase_matrix

import matplotlib.pyplot as plt
plt.plot(pm_spectral.to_gridded().flatten(), label="Converted from Spectral")
plt.plot(pm_gridded.flatten(), ls="--", label="Gridded")
plt.title("Henyey-Greenstein phase function")
plt.legend()

<matplotlib.legend.Legend at 0x7f0fecade900>

Rain

Similarly, we can extract the phase function for rain by calculating the bulk-scattering properties at a lower point in the atmosphere.

atm_pt = ws.atmospheric_field.at(4e3, 0.0, 0.0)
bsp = scattering_species.get_bulk_scattering_properties_tro_spectral(atm_pt, f_grid, 32, 1)
pm_spectral = bsp.phase_matrix

za_scat_grid = pm_spectral.to_gridded().get_za_scat_grid()
bsp = scattering_species.get_bulk_scattering_properties_tro_gridded(atm_pt, f_grid, za_scat_grid, 1)
pm_gridded = bsp.phase_matrix

import matplotlib.pyplot as plt
plt.plot(pm_spectral.to_gridded().flatten(), label="Converted from Spectral")
plt.plot(pm_gridded.flatten(), ls="--", label="Gridded")
plt.title("Henyey-Greenstein phase function")
plt.legend()

<matplotlib.legend.Legend at 0x7f0fc173e030>