Datasets:

NTU-CompHydroMet-Lab
/

ISCCP-H-CT

license: cc-by-4.0
pretty_name: A Global Multi-Decadal Convection Tracking Database from ISCCP-H (1983-2017)
language:
  - en
tags:
  - climate
  - atmospheric-science
  - earth-science
  - satellite
  - convection
  - cloud-tracking
  - mesoscale-convective-systems
  - brightness-temperature
  - tobac
  - ISCCP
size_categories:
  - 10M<n<100M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

A Global Multi-Decadal Convection Tracking Database from ISCCP-H (1983-2017)

ISCCP-H CT: a global, 34-year (July 1983 - June 2017) convection-tracking database derived from International Satellite Cloud Climatology Project H-series (ISCCP-H) infrared observations, produced with the Tracking and Object-Based Analysis of Clouds (tobac) framework.

This dataset accompanies the manuscript:

Luo, Z. J., Wang, L.-P., Selevich, Y., Takahashi, H., Wu, C.-L., Jhang, H., Lin, S.-C., van den Heever, S. C., Machado, L. A., Rossow, W., and Freeman, S. (2026). A Global Multi-Decadal Convection Tracking Database from ISCCP-H: Dataset Description and Convective Lifecycle Analysis. Earth and Space Science (in review).

Archival copy with DOI: https://doi.org/10.5281/zenodo.21505419 (activates on Zenodo publication). Please cite the Zenodo DOI, not this repository.

Description

Convective cloud systems were detected and tracked in ISCCP-H infrared brightness temperature (TB) fields (3-hourly temporal resolution, 10-km spatial resolution) using the tobac framework. Detection used TB minima with dual thresholds of 245 K and 220 K, segmentation at 245 K, and predictive linking with a maximum velocity of 30 m/s.

Each record is one observation of one convective system (feature) at one 3-hourly time step, linked into cell families by the tracker so that the full Lagrangian lifecycle of each system can be reconstructed. For every tracked system the dataset provides position and elliptical geometry, brightness temperature statistics (minimum, mean, maximum, standard deviation, percentiles), size measures (pixel counts, equivalent radius, deep-convective pixel fractions), lifecycle timing, tracking-quality diagnostics, and environmental context (surface type, wind, cloud optical thickness).

Coverage and Volume

Property	Value
Temporal coverage	July 1983 - June 2017 (34-year record; 1983 is Jul-Dec only, 2017 is Jan-Jun only)
Temporal resolution	3-hourly
Spatial coverage	Global, 60S - 60N, 0 - 360E
Source resolution	10 km (ISCCP-H infrared)
Records	67,257,982 system-level observations
Columns	57 documented variables
Format	Apache Parquet (Snappy), one file per calendar year
Total size	approximately 11.8 GB (35 files)
Metadata	CF-1.8 attributes embedded in each Parquet file

Usage

Query directly from the Hub without downloading everything.

DuckDB (SQL over all years):

import duckdb

duckdb.sql("""
    SELECT year, COUNT(*) AS n_obs, ROUND(AVG(minTB_feature), 1) AS mean_min_tb_K
    FROM 'hf://datasets/NTU-CompHydroMet-Lab/ISCCP-H-CT/data/*.parquet'
    GROUP BY year ORDER BY year
""").show()

Polars (lazy, with predicate pushdown). Note: Polars' native hf:// scanner currently fails on namespaced repos ("url-encoded slash" error, upstream Polars bug), so route through HfFileSystem and PyArrow:

import polars as pl
import pyarrow.dataset as ds
from huggingface_hub import HfFileSystem

fs = HfFileSystem()
files = fs.glob("datasets/NTU-CompHydroMet-Lab/ISCCP-H-CT/data/*.parquet")
dataset = ds.dataset(files, filesystem=fs, format="parquet")

# scan/filter/group_by only build a lazy query plan; nothing is read yet.
# .collect() triggers execution. When scanning remotely, collect small
# results (aggregations, subsets); for bulk row-level extraction, download
# the files first and read locally (see below).
summary = (
    pl.scan_pyarrow_dataset(dataset)
    .filter((pl.col("year") == 2016) & (pl.col("minTB_feature") < 220))
    .group_by("land_water_mask")
    .agg(
        pl.len().alias("n_obs"),
        pl.col("radius").mean().alias("mean_radius_km"),
    )
    .collect()
)

Pandas (single year; pandas >= 2.0 recommended for Arrow-backed dtypes):

import pandas as pd

df = pd.read_parquet(
    "hf://datasets/NTU-CompHydroMet-Lab/ISCCP-H-CT/data/ISCCP-H_CT_2016.parquet",
    dtype_backend="pyarrow",  # lower memory, correct nullable types
)

For repeated analysis, download once and read locally (fastest):

hf download NTU-CompHydroMet-Lab/ISCCP-H-CT --repo-type dataset --include "data/*.parquet" --local-dir isccp_h_ct

import polars as pl

df = pl.scan_parquet("isccp_h_ct/data/*.parquet")  # all 35 years as one table

Requirements

pyarrow >= 15 (Parquet engine)
polars >= 1.17 or pandas >= 2.0 (pandas 1.x can read Parquet but lacks Arrow-backed dtypes and robust hf:// support)
duckdb >= 1.0 for hf:// SQL access
huggingface_hub for HfFileSystem and the hf CLI

Data Schema

Key columns (full schema in docs/DATA_DESCRIPTION.md and docs/PARQUET_METADATA.md):

Column	Description	Units
global_cell_id	Cell family ID, format YYYY_cellid (unique within year)	-
datetime	UTC timestamp	-
latitude / longitude	System centroid	degrees
minTB_feature	Minimum brightness temperature (deep convection < 220 K)	K
radius	Equivalent circular radius	km
lifetime_hours	Total cell lifetime	hours
eccentricity	Ellipse eccentricity (0 = circular)	-
land_water_mask	Surface type: "land" or "water"	flag
percent_overlap	Tracking quality (higher = better)	percent
pixel_count	System size in pixels	-

Missing values are IEEE 754 NaN; no imputation is performed.

Recommended Quality Control

percent_overlap > 50 (reliable tracking)
pixel_count > 10 (exclude marginal detections)
minTB_feature between 180 and 300 K (physically valid range)

Known Limitations

frame, feature, and cell IDs reset every year; use global_* variants for multi-year work. Cell families are not linked across the year boundary.
1983 covers July - December only; 2017 covers January - June only.
Each file contains a legacy pandas index column __index_level_0__ with no physical meaning; ignore it.
No coverage poleward of 60 degrees latitude.
The embedded flag_values attribute for wind_dir_letter lists 8 directions, but the data contain all 16 compass points.

Provenance

Source data: ISCCP H-series Climate Data Record (NOAA NCEI), https://doi.org/10.7289/V5QZ281S ; Young et al. (2018), https://doi.org/10.5194/essd-10-583-2018
Tracking software: tobac, Heikenfeld et al. (2019), https://doi.org/10.5194/gmd-12-4159-2019

Citation

Please cite both the dataset and the accompanying manuscript:

@dataset{isccp_h_ct_2026,
  title  = {A Global Multi-Decadal Convection Tracking Database from ISCCP-H (1983-2017)},
  author = {Luo, Zhengzhao J. and Wang, Li-Pen and Selevich, Yuliya and Takahashi, Hanii and Wu, Chun-Liang and Jhang, Heng and Lin, Sung-Che and van den Heever, Susan C. and Machado, Luiz A. and Rossow, William and Freeman, Sean},
  year   = {2026},
  publisher = {Zenodo},
  doi    = {10.5281/zenodo.21505419}
}

Contact

Corresponding author: Zhengzhao Johnny Luo, The City University of New York (zluo@ccny.cuny.edu)

Maintained by the NTU CompHydroMet Lab.