| --- |
| license: cc-by-4.0 |
| pretty_name: TorNet |
| tags: |
| - weather |
| - radar |
| - tornado |
| - nexrad |
| - dual-pol |
| - meteorology |
| - severe-weather |
| - earth-science |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - image-classification |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train/*/*.parquet |
| - split: test |
| path: data/test/*/*.parquet |
| - config_name: catalog |
| data_files: |
| - split: train |
| path: catalog.parquet |
| - config_name: "2013" |
| data_files: |
| - split: train |
| path: data/train/2013/*.parquet |
| - split: test |
| path: data/test/2013/*.parquet |
| - config_name: "2014" |
| data_files: |
| - split: train |
| path: data/train/2014/*.parquet |
| - split: test |
| path: data/test/2014/*.parquet |
| - config_name: "2015" |
| data_files: |
| - split: train |
| path: data/train/2015/*.parquet |
| - split: test |
| path: data/test/2015/*.parquet |
| - config_name: "2016" |
| data_files: |
| - split: train |
| path: data/train/2016/*.parquet |
| - split: test |
| path: data/test/2016/*.parquet |
| - config_name: "2017" |
| data_files: |
| - split: train |
| path: data/train/2017/*.parquet |
| - split: test |
| path: data/test/2017/*.parquet |
| - config_name: "2018" |
| data_files: |
| - split: train |
| path: data/train/2018/*.parquet |
| - split: test |
| path: data/test/2018/*.parquet |
| - config_name: "2019" |
| data_files: |
| - split: train |
| path: data/train/2019/*.parquet |
| - split: test |
| path: data/test/2019/*.parquet |
| - config_name: "2020" |
| data_files: |
| - split: train |
| path: data/train/2020/*.parquet |
| - split: test |
| path: data/test/2020/*.parquet |
| - config_name: "2021" |
| data_files: |
| - split: train |
| path: data/train/2021/*.parquet |
| - split: test |
| path: data/test/2021/*.parquet |
| - config_name: "2022" |
| data_files: |
| - split: train |
| path: data/train/2022/*.parquet |
| - split: test |
| path: data/test/2022/*.parquet |
| --- |
| |
| # TorNet |
|
|
| TorNet is a benchmark dataset of storm-centered NEXRAD WSR-88D dual-polarisation radar samples |
| for tornado detection, released by MIT Lincoln Laboratory (Veillette et al., 2024). This is the |
| complete v1.1 dataset, all ten years and both partitions, repackaged as Parquet so it loads |
| with `datasets` by year and partition without downloading or extracting per-year archives. |
| Nothing is dropped or altered: every field of every netCDF sample is here, the radar values in |
| their physical units. |
|
|
| - **203,133 samples**, 2013 to 2022: 171,666 `train`, 31,467 `test` (the authors' partitions). |
| - **Four scans** per sample, about five minutes apart, the newest last. |
| - **Six products** per scan on a polar grid of 120 azimuths × 240 range gates × 2 tilts |
| (0.5° and 0.9°): reflectivity `DBZ` (dBZ), radial velocity `VEL` (m/s), specific differential |
| phase `KDP` (°/km), correlation coefficient `RHOHV`, differential reflectivity `ZDR` (dB), |
| spectrum width `WIDTH` (m/s). Empty gates are NaN. |
| - **Three categories**: `TOR` (confirmed tornado, with EF rating), `WRN` (tornado-warned storm |
| with no tornado), `NUL` (random non-tornadic storm). |
| - **Per-scan labels** (`frame_labels`): 1 if a tornado is present in that scan, so a sample's |
| four labels read like `0011` for a tornado that appears in the third scan. |
|
|
| ## Layout |
|
|
| ``` |
| data/train/<year>/part-NNNNN-of-MMMMM.parquet 256 samples per shard, zstd |
| data/test/<year>/part-NNNNN-of-MMMMM.parquet |
| catalog.parquet one row per sample, every scalar field |
| ``` |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("jakeryderv/tornet", "2015") # one year, train and test splits |
| ds = load_dataset("jakeryderv/tornet") # all ten years |
| ds = load_dataset("jakeryderv/tornet", data_files={"train": ["data/train/2013/*.parquet", |
| "data/train/2015/*.parquet"], |
| "test": "data/test/2014/*.parquet"}) # a fold |
| catalog = load_dataset("jakeryderv/tornet", "catalog", split="train").to_pandas() # 1 s, no arrays |
| |
| sample = ds["train"].with_format("numpy")[0] |
| sample["VEL"].shape # (4, 120, 240, 2): scan, azimuth, range, tilt |
| sample["frame_labels"] # array([0, 0, 1, 1]) |
| ``` |
|
|
| Radar arrays are stored as float16 (the netCDF values are exactly representable; the round |
| trip is lossless) and come back as float32 under `with_format("numpy")`. The range-folded mask |
| is uint8. Selecting a year or partition downloads only those shards. |
|
|
| ## Fields |
|
|
| | field | type | meaning | |
| |---|---|---| |
| | `DBZ`, `VEL`, `KDP`, `RHOHV`, `ZDR`, `WIDTH` | float16 (4, 120, 240, 2) | the products, physical units, NaN where empty | |
| | `range_folded_mask` | uint8 (4, 120, 240, 2) | 1 where `VEL` / `WIDTH` are range-folded | |
| | `filename` | string | the netCDF name in the original release, e.g. `train/2015/TOR_150425_...nc` | |
| | `partition`, `year` | string, int16 | the authors' split and the sample's year | |
| | `category` | string | `TOR`, `WRN` or `NUL` | |
| | `ef_number` | int8 | EF rating for `TOR`, −1 otherwise | |
| | `event_id`, `episode_id` | int64 | NWS storm event and episode ids | |
| | `site` | string | NEXRAD site, e.g. `KTLX` | |
| | `lat`, `lon` | float32 | storm location | |
| | `label` | int8 | the newest scan's label | |
| | `frame_labels` | int8[4] | tornado present in each scan | |
| | `time` | int64[4] | each scan's time, Unix seconds | |
| | `az_lower`, `az_upper` | float32 | azimuth limits of the chip, degrees | |
| | `rng_lower`, `rng_upper` | float32 | range limits of the chip, metres | |
| | `tornado_start_time`, `tornado_end_time` | int64 | the tornado's interval, Unix seconds (v1.1); 0 when none | |
|
|
| The CoordConv coordinates used by the published detector are derived from the azimuth and |
| range limits; they are not stored. |
|
|
| ## Source and license |
|
|
| Original release: Veillette, M. S., et al., *TorNet: A benchmark dataset for tornado detection |
| and prediction using full-resolution polarimetric weather radar data*, Artificial Intelligence |
| for the Earth Systems, 2024, <https://doi.org/10.1175/AIES-D-24-0006.1>. Data on Zenodo, |
| <https://doi.org/10.5281/zenodo.12636522> (per-year records), code at |
| <https://github.com/mit-ll/tornet>, pretrained detector at |
| <https://huggingface.co/tornet-ml/tornado_detector_baseline_v1>. |
|
|
| The dataset is distributed under CC BY 4.0, as released. This repackaging changes the container |
| format only; cite the paper and the Zenodo record when you use it. |
|
|
| ```bibtex |
| @article{veillette2024tornet, |
| title = {TorNet: A Benchmark Dataset for Tornado Detection and Prediction Using Full-Resolution Polarimetric Weather Radar Data}, |
| author = {Veillette, Mark S. and Kurdzo, James M. and Stepanian, Phillip M. and McDonald, Joseph and Samsi, Siddharth and Cho, John Y. N.}, |
| journal = {Artificial Intelligence for the Earth Systems}, |
| year = {2024}, |
| doi = {10.1175/AIES-D-24-0006.1} |
| } |
| ``` |
|
|
