Limb Scatter Ozone Retrieval

Here we set up a limb scatter ozone retrieval. The core ideas here are that we use the skr.observation.SimulatedLimbObservation to define our observation, and that instead of using the full spectrum in the retrieval we define discrete wavelength “triplets” as the measurement vector.

import numpy as np
import skretrieval as skr


# Measurement tangent altitudes
tan_alts = np.arange(10000, 66000, 1000)

# Triplets
triplets = {
    "uv_1": {
        "wavelength": [302, 351],
        "weights": [1, -1],
        "altitude_range": [45000, 60000],
        "normalization_range": [60000, 65000],
    },
    "uv_2": {
        "wavelength": [312, 351],
        "weights": [1, -1],
        "altitude_range": [40000, 60000],
        "normalization_range": [60000, 65000],
    },
    "uv_3": {
        "wavelength": [322, 351],
        "weights": [1, -1],
        "altitude_range": [30000, 50000],
        "normalization_range": [60000, 65000],
    },
    "vis": {
        "wavelength": [525, 600, 675],
        "weights": [-0.5, 1, -0.5],
        "altitude_range": [0, 35000],
        "normalization_range": [35000, 40000],
    },
}

# 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={"o3": 1.5},  # Simulate with 1.5x the ozone of the prior
)

# 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"]
    )

# 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),
        "absorbers": {
            "o3": {
                "prior_influence": 1e-1,
                "tikh_factor": 1e-1,
                "log_space": False,
                "min_value": 0,
                "max_value": 1,
                "prior": {"type": "mipas"},
            },
        },
        "aerosols": {},
    },
)

# Do the retrieval
results = ret.retrieve()

# Plot the results
skr.plotting.plot_state(results, "o3_vmr", show=True)