Data quality#

imdtrack mirrors the IMD workbook faithfully, including its occasional data-entry errors. Two conservative, non-destructive checks flag them — the source values are never altered:

  • pos_suspect — a fix whose coordinates imply an impossible jump (an isolated position spike), e.g. a corrupted latitude.

  • date_suspect — a day/month-transposed date, e.g. Nargis (2008) where May 1–3 were stored as “01/05, 02/05, 03/05” (Jan/Feb/Mar 5), scattering the storm across months.

bt = imd.load()
bt.observations.query("pos_suspect or date_suspect")   # inspect flagged fixes

Cleaning on demand#

clean() returns a cleaned copy — the source is left untouched:

bt.clean()                       # drop position spikes
bt.clean(how="mask")             # keep the rows but null lat/lon
bt.clean(fix_dates=True)         # also swap day/month back and re-order the track

These catch the common, well-defined cases; a few storms have messier date corruption that is left as-is (visible as an implausibly long start_time–end_time span).

Caveats#

  • Data © India Meteorological Department. This library only reformats it; verify against IMD for operational use. The most recent season is tentative until IMD’s post-season review.

  • Older years often lack storm names and some fields (e.g. central pressure); those appear as NaN.