lidarpy examples

Synthetic elastic and Raman signals

import numpy as np

from lidarpy.retrieval.synthetic.generator import synthetic_signals

ranges = np.arange(30.0, 6000.0, 30.0)
elastic, raman, params = synthetic_signals(
    ranges,
    wavelengths=(532, 607),
    ae=1.0,
    lr=50.0,
    apply_overlap=False,
    number_of_initial_nan_values=0,
)

The synthetic generators create compact elastic, Raman and depolarization cases that can be inspected visually before using them in retrieval tests.

Synthetic elastic quicklook example — Elastic synthetic quicklook for the 532 nm channel.

Synthetic Raman quicklook example — Raman synthetic quicklook for the 607 nm channel.

Synthetic LPDR quicklook example — Synthetic LPDR quicklook built from the depolarization signal generator.

Klett retrieval against synthetic signal

from lidarpy.retrieval.klett import klett_rcs
from lidarpy.utils.utils import signal_to_rcs

rcs = signal_to_rcs(elastic.values, ranges)
particle_beta = klett_rcs(
    rcs,
    ranges,
    params["molecular_beta"],
    reference=(3000.0, 3500.0),
    lr_part=50.0,
)

Forward iterative retrieval with boundary condition

from scipy.integrate import cumulative_trapezoid
from lidarpy.retrieval.klett import iterative_beta_forward

start_height = 600.0
start_idx = abs(ranges - start_height).argmin()
initial_aod = cumulative_trapezoid(
    params["particle_alpha"],
    ranges,
    initial=0.0,
)[start_idx]

particle_beta = iterative_beta_forward(
    rcs,
    calibration_factor=1e11,
    range_profile=ranges,
    params=params,
    lr_part=50.0,
    start_height=start_height,
    height_top=2400.0,
    initial_particle_optical_depth=float(initial_aod),
)

Retrieval validation with synthetic truth

Use the generated truth fields to verify whether the retrieval is recovering the expected aerosol structure and where boundary or reference assumptions limit the comparison.