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Drift Velocity — WI937

3-D Ionospheric Drift Velocity from a VIPIR RIQ File

Estimate the full [Vx, Vy, Vz] drift vector from line-of-sight velocity measurements extracted by EchoExtractor, using height-binned weighted least-squares with iterative sigma-clipping.

Script: examples/vipir/drift_velocity_wi937.py

Physics background

Each ionospheric echo returns one line-of-sight (LOS) velocity measurement:

\[ V^*_i = l_i \, V_x + m_i \, V_y + n_i \, V_z \]

The direction cosines are computed from the echo's own echolocation parameters (XL, YL, virtual height R′):

\[ l = \frac{X_L}{R'} \quad (\text{East}), \qquad m = \frac{Y_L}{R'} \quad (\text{North}), \qquad n = \sqrt{1 - l^2 - m^2} \quad (\text{Up}) \]

Stacking many echoes with geometrically diverse arrival directions gives the overdetermined system A v = V* which is solved by weighted least-squares. Iterative sigma-clipping removes echoes whose LOS velocity is inconsistent with the bulk solution.

Steps

1 — Load and extract echoes

from pynasonde.vipir.riq.echo import EchoExtractor
from pynasonde.vipir.riq.parsers.read_riq import VIPIR_VERSION_MAP, RiqDataset

riq = RiqDataset.create_from_file(
    "examples/data/WI937_2022233235902.RIQ",
    unicode="latin-1",
    vipir_config=VIPIR_VERSION_MAP.configs[1],  # vipir_version=0 / data_type=1
)

extractor = EchoExtractor(
    sct=riq.sct,
    pulsets=riq.pulsets,
    snr_threshold_db=3.0,
    min_height_km=60.0,
    max_height_km=1000.0,
    min_rx_for_direction=3,
    max_echoes_per_pulset=5,
)
extractor.extract()

min_rx_for_direction=3 ensures XL, YL (and therefore direction cosines) are available. WI937 has 8 receivers so this is always satisfied.

2 — Whole-sounding fit

df_whole = extractor.fit_drift_velocity(
    height_bin_km=None,   # single fit over all echoes
    min_echoes=6,
    snr_weight=True,
    n_sigma=2.5,
    max_ep_deg=None,      # skip EP pre-filter
)

height_bin_km=None produces one row with the bulk [Vx, Vy, Vz] for the entire sounding — useful as a sanity check before height-resolved analysis.

Sample output:

=== Whole-sounding drift velocity ===
  Vx =  +32.4 m/s  (East)
  Vy =  -18.7 m/s  (North)
  Vz =   -5.1 m/s  (Vertical)
  RMS LOS residual :  41.3 m/s
  Condition number :   6.2
  Echoes used      :  312  (rejected: 24)

3 — Height-binned fit

df_bins = extractor.fit_drift_velocity(
    height_bin_km=10.0,   # 10 km bins
    min_echoes=6,
    snr_weight=True,
    n_sigma=2.5,
    max_ep_deg=None,
)

Returns one row per height bin that had ≥ min_echoes valid echoes with finite XL/YL/V*.

Output columns:

Column Description
height_bin_km Bin centre altitude (km)
vx_mps East component (m/s)
vy_mps North component (m/s)
vz_mps Vertical component (m/s)
residual_mps Mean absolute LOS residual after sigma-clipping
condition_number Condition number of the weighted design matrix
n_echoes Echoes used in the final fit
n_rejected Echoes removed by sigma-clipping

4 — Output figure

A 2-panel figure is saved to docs/examples/figures/drift_velocity_wi937.png:

Panel Contents
(A) Drift components vs height Vx (blue), Vy (orange), Vz (green) as lines; dotted verticals show the whole-sounding estimate
(B) Fit quality vs height RMS LOS residual (bar, purple) + echo count per bin (line, red) on dual x-axes

Drift velocity WI937

WI937 2022-233 23:59 UT. Height-binned drift velocity (10 km bins) with whole-sounding estimates shown as dotted verticals.

Run

cd /home/chakras4/Research/CodeBase/pynasonde
python examples/vipir/drift_velocity_wi937.py

Notes

  • Condition number: values above ~50 indicate near-singular geometry (echoes all arriving from similar directions). Discard those bins for Vx/Vy and trust only Vz.
  • snr_weight=True: echoes with higher SNR contribute more to the fit; useful when the echo cloud has a wide dynamic range.
  • n_sigma=2.5: controls sigma-clipping aggressiveness. Lower values (e.g. 2.0) remove more outliers but risk rejecting legitimate oblique echoes in disturbed conditions.
  • For a direct comparison of raw vs. filtered drift velocity see Full Analysis — WI937.