Limb Scatter Cloud Retrieval

Here we demonstrate the retrieval of cloud height, width, and optical depth. Similar to other examples we must provide information on the optical parameters.

import numpy as np
import sasktran2 as sk
import skretrieval as skr

In this example we use a Mie distribution with a fixed particle size to define our cloud optical properties.

@skr.Retrieval.register_optical_property("cloud")
def cloud_optical_prop(*args, **kwargs):
    refrac = sk.mie.refractive.H2SO4()
    dist = sk.mie.distribution.LogNormalDistribution().freeze(
        mode_width=1.6, median_radius=80.0,
    )

    db = sk.database.MieDatabase(
        dist,
        refrac,
        np.array([469.0, 750.0]),
    )

    return db

We configure the state vector for the three parameters (vertical_optical_depth, width_fwhm_m, height_m) required by the Gaussian extinction profile constituent.

# Measurement tangent altitudes
tan_alts = np.arange(10000, 45000, 1000)

# Triplets
triplets = {
    "745": {
        "wavelength": [745],
        "weights": [1],
        "altitude_range": [0, 40000],
        "normalization_range": [40000, 45000],
    },
}

# Get the wavelengths required for the triplets
wavel = np.unique(np.concatenate([triplets[t]["wavelength"] for t in triplets])).astype(float)

# Set up a simulated observation with our tangent altitudes, wavelengths, and use 1.5x the initial guess for ozone
obs = skr.observation.SimulatedLimbObservation(
    cos_sza=0.2,
    relative_azimuth=0,
    observer_altitude=200000,
    reference_latitude=20,
    reference_longitude=20,
    tangent_altitudes=tan_alts,
    sample_wavelengths=wavel,
    state_adjustment_factors={
        "cloud": {
            "vertical_optical_depth": {"scale": 1.5},
            "width_fwhm_m": {"scale": 1.5},
            "height_m": {"scale": 1.5},
        }
    },
)

# Construct our measurement vectors
meas_vec = {}
for name, t in triplets.items():
    meas_vec[name] = skr.measvec.Triplet(
        t["wavelength"], t["weights"], t["altitude_range"], t["normalization_range"], normalize=False
    )

# Set up the retrieval object
ret = skr.Retrieval(
    obs,
    measvec=meas_vec,
    minimizer="rodgers",
    target_kwargs={"rescale_state_space": True},
    state_kwargs={
        "altitude_grid": np.arange(0, 70000, 1000),
        "aerosols": {
            "cloud": {
                "type": "gaussian_extinction_profile",
                "nominal_wavelength": 745,
                "retrieved_quantities": {
                    "vertical_optical_depth": {
                        "prior_influence": 1e0,
                        "tikh_factor": 1e-2,
                        "min_value": 1e-9,
                        "max_value": 10
                    },
                    "width_fwhm_m": {
                        "prior_influence": 1e0,
                        "tikh_factor": 1e-2,
                        "min_value": 1e-9,
                        "max_value": 20000
                    },
                    "height_m": {
                        "prior_influence": 1e0,
                        "tikh_factor": 1e-2,
                        "min_value": 0,
                        "max_value": 25000
                    },
                },
                "prior": {
                    "vertical_optical_depth": {"type": "constant", "value": 0.05},
                    "width_fwhm_m": {"type": "constant", "value": 5000},
                    "height_m": {"type": "constant", "value": 15000},
                }
            },
        },
    },
)

# Do the retrieval
results = ret.retrieve()

# Display the results
print(results["state"])

<xarray.Dataset> Size: 656B
Dimensions:                                        (altitude: 70)
Coordinates:
  * altitude                                       (altitude) int64 560B 0 .....
Data variables:
    cloud_vertical_optical_depth                   float64 8B 0.07498
    cloud_vertical_optical_depth_prior             float64 8B 0.05
    cloud_vertical_optical_depth_1sigma_error      float64 8B 0.002789
    cloud_vertical_optical_depth_averaging_kernel  float64 8B 0.9986
    cloud_width_fwhm_m                             float64 8B 7.5e+03
    cloud_width_fwhm_m_prior                       float64 8B 5e+03
    cloud_width_fwhm_m_1sigma_error                float64 8B 42.78
    cloud_width_fwhm_m_averaging_kernel            float64 8B 1.0
    cloud_height_m                                 float64 8B 2.25e+04
    cloud_height_m_prior                           float64 8B 1.5e+04
    cloud_height_m_1sigma_error                    float64 8B 85.9
    cloud_height_m_averaging_kernel                float64 8B 0.9998