imdtrack 0.2.6

A Python library for the IMD RSMC New Delhi North Indian Ocean cyclone best-track record, kept up to date automatically.

imdtrack

The India Meteorological Department (RSMC New Delhi) cyclone best-track record — every depression and cyclonic storm in the North Indian Ocean (Bay of Bengal & Arabian Sea) since 1982 — as a tidy pandas DataFrame or a CF-style xarray Dataset, kept up to date automatically.

IMD publishes the record as a single, hand-maintained Excel workbook. imdtrack turns it into clean, analysis-ready tables. The parsed dataset is committed to this repo (under data/) and refreshed by a monthly GitHub Action, so imd.load() just downloads the pre-parsed data — no Excel parsing on your side.

Install

pip install imdtrack            # core: pandas + pyarrow (reads the published parquet)
pip install imdtrack[xarray]    # + xarray/numpy for .to_xarray()
pip install imdtrack[plot]      # + cartopy/matplotlib for map plotting
pip install imdtrack[all]       # xarray + numpy + openpyxl

Or with uv:

uv add imdtrack                 # into a project  (or: uv pip install imdtrack)

Extra	Adds	For
(none)	pandas, pyarrow	loading the data as DataFrames
xarray	xarray, numpy	bt.to_xarray()
plot	cartopy, matplotlib	imd.plot_track() map plots
pipeline	openpyxl	parsing the IMD workbook yourself (source="imd")

cartopy has no Python 3.14 wheel yet, so [plot] needs Python ≤ 3.13 (or a conda/system GEOS+PROJ to build it). Conda packaging is on the way — see conda/.

Usage

import imdtrack as imd

bt = imd.load()                 # pre-parsed dataset from GitHub (cached)
df = bt.observations            # tidy DataFrame: one row per 3-hourly fix
ds = bt.to_xarray()             # (storm, step) xarray.Dataset

bt = imd.load(update=True)      # re-download only if the repo published new data

bt.storm("2020-001")            # one storm's track (AMPHAN)
bt.storms                       # one row per storm (peak grade, max wind, ...)
bt.remarks                      # landfall / weakening notes, linked by storm_id

The tidy observations frame

column	meaning
storm_id	stable id, "<year>-<serial>" e.g. 2020-001
year, serial	year and serial number of the system within that year
basin	BOB (Bay of Bengal), ARB (Arabian Sea), or LAND
name	cyclone name (blank for unnamed / older systems)
time	observation time (UTC, datetime64)
lat, lon	position (°N, °E)
ci_no	Dvorak CI / T-number
pressure	estimated central pressure (hPa)
wind	maximum sustained surface wind (knots)
pressure_drop	pressure drop / ΔP (hPa)
grade	ordered category: D < DD < CS < SCS < VSCS < ESCS < SuCS
oci, oci_diameter	outermost closed isobar pressure (hPa) & diameter (°)
step	0-based fix index within the storm

The xarray Dataset

Laid out like IBTrACS: a ragged track becomes a 2-D (storm, step) grid. Per-fix variables span both dims; storm-level metadata (name, basin, year, …) are storm coordinates.

ds = bt.to_xarray()
ds.sel(storm="2020-001")["wind"].max()      # AMPHAN peak intensity
ds.where(ds.basin == "ARB", drop=True)      # Arabian Sea storms only

See the documentation for a full walkthrough and a North Indian Ocean climatology example.

Staying up to date

A monthly GitHub Action re-fetches the IMD workbook and, only if it parses and passes validation, updates data/ — a broken upload can never overwrite the good published data. You normally don't have to do anything; imd.load(update=True) pulls the latest.

Data quality

The library 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 (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).

bt = imd.load()
bt.observations.query("pos_suspect or date_suspect")   # inspect flagged fixes
bt.clean()                        # drop position spikes (or clean(how="mask"))
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).

Notes & 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.

Citation

Please cite imdtrack via its Zenodo Concept DOI — stable across releases, always resolving to the latest version:

Syed, H. A. imdtrack: A Python library for the IMD North Indian Ocean cyclone best-track record. https://doi.org/10.5281/zenodo.21301659

GitHub's "Cite this repository" button reads CITATION.cff.

License

BSD 3-Clause. See LICENSE.