How-to guides

Quickstart

The main interface of Joseki is its make method. This method creates the atmospheric profile corresponding to a given identifier. For example, make the so-called AFGL US Standard atmospheric profile from Anderson et al (1986) with:

import joseki

ds = joseki.make(identifier="afgl_1986-us_standard")

Display the available identifiers with:

from joseki.profiles import factory

list(factory.registry.keys())

Use the to_dataset method to save the dataset to the disk as a netCDF file:

ds.to_netcdf("my_dataset.nc")

For other formats, refer to the xarray IO documentation.

Saving to CSV file

ds.to_dataframe().to_csv("my_dataset.csv")

Note that one drawback of saving to a CSV file in the above manner is that the information on the quantity units is lost.

Open the dataset again using xarray.open_dataset:

import xarray as xr

ds = xr.open_dataset("my_dataset.nc")

The datasets format is described here.

Altitude grid

You can specify the altitude grid for your atmospheric profile. If the source atmospheric profile is based on tabulated data, it is going to be interpolated on the specified altitude grid.

Example

import numpy as np

from joseki.units import ureg


ds = joseki.make(
    identifier="afgl_1986-us_standard",
    z=np.linspace(0, 100, 51) * ureg.km,
)

During interpolation, the column number densities associated to the different atmospheric constituents are likely going to be changed. In the example above, the ozone column number density is increased to 346.60 Dobson units compared to the atmospheric profile with the original altitude grid, which has an ozone column number density of 345.75 Dobson units. To ensure column densities are conserved during interpolation, set the conserve_column parameter to True.

Example

ds = joseki.make(
    identifier="afgl_1986-us_standard",
    z=np.linspace(0, 100, 51) * ureg.km,
    conserve_column=True,
)

Cells representation

To make an atmospheric profile where data variables are given in altitude cells instead of at altitude levels, set the parameter represent_in_cells to True:

ds = joseki.make(
    identifier="afgl_1986-us_standard",
    represent_in_cells=True,
)

The resulting dataset has a coordinate variable z that corresponds to the altitude cells center and a data variable z_bounds that indicate the altitude bounds of each altitude cell, i.e. atmospheric layer.

Note that moving a profile from the nodes-representation to the cells-representation is achieved by interpolating the nodes-represented profile. As a result, column densities are likely to be different in the cells-represented profile.

Similarly to when specifying the altitude grid, you can ensure that column densities are conserved with the conserve_column parameter.

Advanced options

The collection of atmospheric profiles defined by Anderson et al (1986) includes volume mixing ratio data for 28 molecules, where molecules 8-28 are described as additional. By default, these additional molecules are included in the atmospheric profile. To discard these additional molecules, set the additional_molecules parameter to False:

ds = joseki.make(
    identifier="afgl_1986-us_standard",
    represent_in_cells=True,
    additional_molecules=False,
)

The resulting dataset now includes only 7 molecules, instead of 28.

Derived quantities

You can compute various derived quantities from a thermophysical properties dataset produced by joseki, as illustrated by the examples below.

Column number density

ds = joseki.make(identifier="afgl_1986-us_standard")
ds.joseki.column_number_density["O3"].to("dobson_unit")

Column mass density

ds.joseki.column_mass_density["H2O"]

Number density at sea level

ds.joseki.number_density_at_sea_level["CO2"]

Mass density at sea level

ds.joseki.mass_density_at_sea_level["CH4"]

For further details on these methods, refer to the API reference.

Rescaling

You can modify the amount of a given set of molecules in your thermophysical properties dataset by applying a rescale transformation.

Example

ds = joseki.make(identifier="afgl_1986-us_standard")
rescaled = ds.joseki.rescale(
   factors={
      "H2O": 0.5,
      "CO2": 1.5,
      "CH4": 1.1,
   }
)

In the example above, the amount of water vapor is halfed whereas the amount of carbon dioxide and methane is increased by 150% and 110%, respectively. When a rescale transformation has been applied to a dataset, its history attribute is updated to indicate what scaling factors were applied to what molecules.

Plotting

Note

For plotting, you will need to install the matplotlib library.

You can easily make a plot of any of the variables of a dataset, i.e., air pressure (p), air temperature (t), air number density (n) or volume fraction (x_*):

Pressure plot

import matplotlib.pyplot as plt 

ds = joseki.make(
   identifier="afgl_1986-us_standard",
   additional_molecules=False
)

ds.p.plot(
   figsize=(4, 8),
   ls="dotted",
   marker=".",
   y="z",
   xscale="log",
)
plt.show()

Temperature plot

ds.t.plot(
   figsize=(4, 8),
   ls="dotted",
   marker=".",
   y="z",
   xscale="linear",
)
plt.show()

Number density plot

ds.n.plot(
   figsize=(4, 8),
   ls="dotted",
   marker=".",
   y="z",
   xscale="log",
)
plt.show()

Volume fraction plot

plt.figure(figsize=(8, 8)) 

for m in ds.joseki.molecules:
   ds[f"x_{m}"].plot(
      ls="dotted",
      marker=".",
      y="z",
      xscale="log",
   )

plt.xlabel("volume fraction [dimensionless]")
plt.legend(ds.joseki.molecules)
plt.show()