| # ISCCP_HXG Convective Systems Dataset Description |
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| Formal description of the ISCCP_HXG convection tracking dataset: source data, tracking configuration, data concepts, and statistical characteristics. |
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| This document describes the scientific content of the dataset. For the distributed Parquet format, schema tables, and usage examples, see [`../data/README.md`](../data/README.md). For the complete CF-1.8 metadata specification, see [`PARQUET_METADATA.md`](PARQUET_METADATA.md). |
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| --- |
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| ## 1. Overview |
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| The ISCCP (International Satellite Cloud Climatology Project) HXG dataset is built from cloud observations by multiple geostationary and polar-orbiting satellites. Deep convective systems were identified, segmented, and tracked in the infrared brightness temperature (TB) fields using the **tobac (Tracking and Object-Based Analysis of Clouds)** Python package. |
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| - **Temporal coverage**: July 1983 - June 2017 (34-year record; 1983 contains Jul-Dec only, 2017 contains Jan-Jun only) |
| - **Temporal resolution**: 3-hourly |
| - **Spatial coverage**: latitude -60 to +60 degrees, longitude 0 to 360 degrees east |
| - **Convection detection threshold**: TB < 245 K |
| - **Distributed volume**: 67,257,982 records, 11.8 GB (35 year-partitioned Parquet files) |
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| The source data were produced as annual CSV files (54 columns, approximately 1.2 GB per year). The distributed Parquet dataset preserves all original columns and adds three year-prefixed global identifier columns (57 documented columns in total). |
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| ## 2. Core Concepts |
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| ### Feature (convective system) |
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| A convective system identified at a single time step; the basic unit of the dataset. Each feature represents one convective event at a specific time and location. |
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| ### Cell (convective family) |
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| A set of convective systems linked in time by the tracker. A single `cell` ID represents the temporal evolution trajectory of one convective system. Based on the 2017 January-June partition: |
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| - Mean lifetime: 12.4 hours |
| - Maximum lifetime: 114 hours (4.75 days) |
| - Mean number of systems per family: 3.7 |
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| ### Frame (time step) |
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| One 3-hourly time slice, used for computing the elliptical geometry parameters. |
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| ## 3. Tracking Configuration (tobac) |
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| Parameter values used in the original tracking runs. Confirm against the accompanying manuscript Methods section before citing. |
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| ### Feature detection |
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| ```python |
| target = 'minimum' # detect TB minima (coldest cloud tops) |
| threshold = [245, 220] # dual detection thresholds (K) |
| n_min_threshold = 2 # minimum pixel count |
| position_threshold = 'weighted_diff' # centroid positioning method |
| sigma_threshold = 1.5 # smoothing parameter |
| n_erosion_threshold = 2 # erosion iterations |
| ``` |
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| ### Segmentation |
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| ```python |
| target = 'minimum' |
| threshold = 245 # segmentation threshold (K) |
| ``` |
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| ### Linking |
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| ```python |
| method_linking = 'predict' # predictive linking |
| v_max = 30 # maximum velocity (m/s) |
| adaptive_stop = 2 |
| adaptive_step = 0.95 |
| stubs = 2 # minimum number of linked time steps |
| subnetwork_size = 20 |
| time_cell_min = 300 # minimum duration (seconds) |
| ``` |
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| ## 4. Column Groups |
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| The 57 documented columns fall into the following groups (full schema in [`../data/README.md`](../data/README.md)): |
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| | Group | Columns | Content | |
| |---|---|---| |
| | Identifiers | 6 | `frame`, `feature`, `cell` and year-prefixed global variants | |
| | Temporal | 9 | timestamp components, elapsed time, lifetime | |
| | Spatial | 9 | centroid, bounding box, ellipse center, surface type | |
| | Brightness temperature | 15 | min/mean/max, percentiles, standard deviation, gradient | |
| | Convective intensity | 6 | pixel counts, deep-convective fractions, optical thickness | |
| | Wind | 3 | speed, direction (degrees and 16-point compass) | |
| | Geometry | 5 | ellipse axes, inclination, eccentricity, spatial correlation | |
| | Tracking quality | 4 | overlap percentages, family system count | |
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| ## 5. Statistical Characteristics (2017 January-June partition) |
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| The following statistics were computed from the 2017 partition (1,042,051 records, 283,434 cell families) and are representative of the dataset. |
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| ### System size |
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| ``` |
| Pixel count: median 102, mean 510, maximum 90,731 (strongly right-skewed) |
| Radius: median 58 km, mean 90 km, maximum 1,622 km |
| ``` |
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| ### Convective intensity |
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| ``` |
| minTB_feature: median 218 K, mean 217 K, minimum 160 K (extreme deep convection) |
| Upper bound: 245 K (detection threshold) |
| ``` |
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| ### Lifetime |
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| ``` |
| Cell family lifetime: median 9 h, mean 12.4 h, maximum 114 h |
| Systems per family: mean 3.7 |
| ``` |
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| ### Environment |
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| - Ocean: 71.5 percent of observations |
| - Land: 28.5 percent of observations |
| - Dominant wind directions: W (13.0 percent), WNW (10.1 percent), WSW (10.0 percent) |
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| ## 6. Missing Data |
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| Verified missing-value rates for the 2017 January-June partition: |
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| | Column group | Missing rate | Cause | |
| |---|---|---| |
| | Optical thickness (`avg_optical_thickness`, `max_optical_thickness`) | 60.2 percent | Satellite retrieval limitations | |
| | Wind (`wind_speed`, `wind_dir`, `wind_dir_letter`) | 27.2 percent | Reanalysis matching gaps | |
| | Overlap (`percent_overlap`, `percent_non_overlap`) | 36.5 percent | Undefined at the first time step of each track | |
| | Geometry parameters | 8.8 percent | Require multiple pixels | |
| | Basic convective parameters | 6.9 percent | Very small system detection limits | |
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| Missing values are stored as IEEE 754 NaN; no sentinel values are used and no imputation is performed. |
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| ## 7. Usage Guidance |
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| ### Quality control recommendations |
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| 1. Filter marginal detections: `pixel_count < 5` may be unreliable. |
| 2. Screen outliers: inspect `minTB_feature < 160 K` or `radius > 1500 km` before use. |
| 3. Handle missing data explicitly: analyze optical thickness on the valid subset; exclude or impute wind fields as appropriate. |
| 4. Tracking continuity: `cell` tracks can break when systems split or merge. |
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| ### Suggested feature sets for clustering |
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| Low-missing-rate base set: |
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| ```python |
| features = [ |
| 'pixel_count', 'radius', 'minTB_feature', 'avgTB_feature', |
| 'std_dev_tb', 'lifetime_hours', 'eccentricity', 'land_water_mask', |
| ] |
| ``` |
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| Extended set (requires missing-value handling): |
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| ```python |
| features = [ |
| 'pixel_count', 'radius', 'minTB_feature', |
| 'convective_fraction', 'pixels_below_220', |
| 'lifetime_hours', 'lifetime_num_cs', |
| 'wind_speed', 'wind_dir', |
| 'avg_optical_thickness', |
| 'semi_major', 'semi_minor', 'eccentricity', |
| ] |
| ``` |
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| ### Example science questions |
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| 1. Convective typology: short-lived versus long-lived, weak versus intense, isolated versus organized systems. |
| 2. Environmental contrasts: ocean versus land convection; convective organization under different wind regimes. |
| 3. Spatiotemporal evolution: diurnal cycle, seasonal patterns, regional climate differences. |
| 4. Extremes: long-lived systems (> 24 h), very cold cloud tops (TB < 200 K), large systems (radius > 500 km). |
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| ## 8. Known Limitations |
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| 1. 3-hourly sampling cannot resolve rapid convective evolution. |
| 2. Source pixel resolution is approximately 10 km (varying with latitude and satellite). |
| 3. Track continuity breaks when complex systems split or merge. |
| 4. No coverage poleward of 60 degrees latitude. |
| 5. Cell IDs reset annually; systems alive across the year boundary receive new IDs in January (see `../data/README.md`). |
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| ## 9. Quick Start |
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| ```python |
| import polars as pl |
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| DATASET = "Dataset/data/ISCCP_HXG_1983_2017_partitioned" |
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| df = ( |
| pl.scan_parquet(DATASET) |
| .filter( |
| (pl.col("year") == 2017) |
| & (pl.col("minTB_feature") < 220) |
| ) |
| .select(["global_cell_id", "datetime", "latitude", "longitude", |
| "minTB_feature", "radius", "lifetime_hours"]) |
| .collect() |
| ) |
| print(f"Records: {len(df):,}") |
| ``` |
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| ## 10. References and Contacts |
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| - tobac: https://tobac.readthedocs.io , https://github.com/tobac-project/tobac |
| - Machado, L.A.T., and W.B. Rossow, 1993: Structural characteristics and radiative properties of tropical cloud clusters. Mon. Wea. Rev., 121, 3234-3260. |
| - Machado, L.A.T., W.B. Rossow, R.L. Guedes, and A.W. Walker, 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126, 1630-1654. |
| - Schiffer, R.A., and W.B. Rossow, 1983: The International Satellite Cloud Climatology Project (ISCCP). Bull. Amer. Meteor. Soc., 64, 779-784. |
| - Young, A. H., et al., 2018: The International Satellite Cloud Climatology Project H-Series climate data record product. Earth Syst. Sci. Data, 10, 583-593. |
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| Contacts: |
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| - Yuliya Selevich: yuliya.selevich@gmail.com |
| - Zhengzhao Johnny Luo: z.johnny.luo@gmail.com |
| - Hanii Takahashi: hanii.takahashi@jpl.nasa.gov |
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| --- |
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| **Document version**: 2.0 (English translation and revision of ISCCP_HXG_數據說明.md v1.0) |
| **Last updated**: 2026-07-23 |
| **Applies to**: ISCCP_HXG 1983-2017 partitioned Parquet dataset |
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