Add dataset card

README.md

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+---
+license: mit
+task_categories:
+  - time-series-forecasting
+tags:
+  - air-quality
+  - atmospheric-science
+  - pm25
+  - pm10
+  - east-asia
+  - cmaq
+  - weather-forecasting
+  - world-model
+pretty_name: PLUME - East Asia Air Quality (0.25 deg, hourly, 2016-2024)
+size_categories:
+  - 100B<n<1T
+---
+
+# PLUME — East Asia air-quality dataset
+
+**0.25° · hourly · 2016–2024 · 117 × 222 grid**
+
+The preprocessed archive behind **PLUME**, *A Physically Conditioned Decision-State World Model for Air-Quality Exceedance Forecasting*.
+
+📦 **Code:** [github.com/2na-97/PLUME](https://github.com/2na-97/PLUME)
+🔁 **Predecessor:** [`2na-97/FAKER-Air`](https://huggingface.co/datasets/2na-97/FAKER-Air) — the same region at 27 km
+
+---
+
+## What is in here
+
+Two independent trees on one shared grid, plus the trained model weights.
+
+| Path | Contents | Compressed | Raw |
+|---|---|---|---|
+| `cmaq_asia_1h_0p25/YYYY.tar.zst` | CMAQ reanalysis — 12-species speciated aerosol and gas fields, surface proxy stack | ~8.7 GB/yr | ~17 GB/yr |
+| `obs_asia_1h_0p25/YYYY.tar.zst` | Ground-station observations gridded to the same cells, with validity and region masks | ~0.4 GB/yr | ~5 GB/yr |
+| `checkpoints/` | Trained PLUME weights (multiple seeds) | ~130 MB | — |
+
+Years available: **2016 – 2024**. One shard per (tree, year), so you can fetch only what you need.
+
+---
+
+## Quick start
+
+```bash
+pip install huggingface_hub
+git clone https://github.com/2na-97/PLUME.git && cd PLUME
+
+# evaluation years only -- enough to reproduce every reported number (~44 GB raw)
+python scripts/download_data.py --years 2023 2024
+
+# the full training record
+python scripts/download_data.py --all
+
+# one tree, one year
+python scripts/download_data.py --years 2023 --tree obs
+
+# the trained weights
+python scripts/download_data.py --checkpoints
+```
+
+The helper downloads, extracts and cleans up the shard. Re-running is safe — a year already extracted is skipped and the Hub download resumes.
+
+<details>
+<summary><b>Without the helper script</b></summary>
+
+```python
+from huggingface_hub import hf_hub_download
+
+shard = hf_hub_download(
+    repo_id="2na-97/PLUME",
+    repo_type="dataset",
+    filename="obs_asia_1h_0p25/2023.tar.zst",
+)
+```
+
+then
+
+```bash
+mkdir -p data/obs_asia_1h_0p25
+tar --use-compress-program=unzstd -xf "$shard" -C data/obs_asia_1h_0p25
+```
+
+Several shards at once:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="2na-97/PLUME", repo_type="dataset",
+    allow_patterns=["*/2023.tar.zst", "*/2024.tar.zst"],
+    local_dir="downloads",
+)
+```
+</details>
+
+---
+
+## Layout after extraction
+
+```
+data/
+├── cmaq_asia_1h_0p25/YYYY/MM/DD/NIER_27_01/
+│   ├── YYYYMMDD_x_aurora_surf.npy     # [25, 13, 117, 222] float32 surface stack (memory-mapped)
+│   ├── YYYYMMDD_x_cmaq_cams_sp12.npy  # [25, 12, 117, 222] float32 speciated aerosol + gas
+│   ├── YYYYMMDD_x_cmaq_cams.npz       # full [25, 24, 1, 117, 222] concentration stack
+│   ├── YYYYMMDD_meta.json
+│   └── cams_lat.npy, cams_lon.npy, cams_grid_meta.json
+└── obs_asia_1h_0p25/YYYY/MM/DD/NIER_27_01/
+    ├── YYYYMMDD_y_obs_cams.npz        # gridded station observations + validity mask
+    ├── YYYYMMDD_y_obs_cams_pmvals.npy
+    ├── YYYYMMDD_y_obs_cams_region.npy # region ID per cell (19 KR provinces + CN regions)
+    └── cams_lat.npy, cams_lon.npy, cams_grid_meta.json
+```
+
+The leading `T = 25` axis is a day's 00–23 hourly slots plus one wrap slot.
+
+### Grid
+
+| | |
+|---|---|
+| Resolution | 0.25° |
+| Shape | 117 (lat) × 222 (lon) |
+| Domain | 19.6 °N – 48.0 °N, 104.8 °E – 159.6 °E |
+| Grid name | `NIER_27_01` |
+| Cadence | hourly |
+
+Coordinates ship alongside every day as `cams_lat.npy` / `cams_lon.npy`.
+
+### Channel orders
+
+**Observations** (`OBS_SHORT_NAMES` in `plume/gridio.py`):
+
+```
+pm2p5, pm10, so2, no2, o3, co
+```
+
+**CMAQ concentrations** (`CMAQ_CONC_ORDER` in `plume/species.py`):
+
+```
+SO4_25, NH4_25, NO3_25, ORG_25, EC_25, MISC_25, PM2P5,
+tcso2, tcco, tcno2, O3, NO, NOx,
+SO4_10, NH4_10, NO3_10, ORG_10, EC_10, MISC_10, PM10,
+ISOPRENE, OLES, AROS, ALKS
+```
+
+**Surface proxy stack** (`*_x_aurora_surf.npy`):
+
+```
+2t, 10u, 10v, msl, pm1, pm2p5, pm10, tcco, tc_no, tcno2, gtco3, tcso2, source_mask
+```
+
+### Regions
+
+`*_y_obs_cams_region.npy` carries a region ID per cell: the 19 Korean provinces (`KR_SEOUL`, `KR_INCHEON`, … `KR_JEJU`) plus Chinese and other regions appended at runtime. The paper reports three strata — the broad East-Asian domain, Korea, and a prespecified Chinese dust-source corridor.
+
+---
+
+## Loading a day
+
+```python
+import numpy as np
+
+day = "data/obs_asia_1h_0p25/2023/03/21/NIER_27_01"
+obs = np.load(f"{day}/20230321_y_obs_cams.npz")
+print(obs.files)
+
+pm = np.load(f"{day}/20230321_y_obs_cams_pmvals.npy")   # [25, 6, 117, 222]
+region = np.load(f"{day}/20230321_y_obs_cams_region.npy")
+lat = np.load(f"{day}/cams_lat.npy")
+lon = np.load(f"{day}/cams_lon.npy")
+```
+
+In practice you want `DirectLeadDataset` from the code repository, which assembles issue-time inputs, history windows, validity masks and the motion estimate for you.
+
+---
+
+## Intended use, and one caveat that matters
+
+The task is **exceedance forecasting** from +6 h to +120 h in 6 h steps, at the public-guidance boundaries PM<sub>2.5</sub> > 35 and PM<sub>10</sub> > 80 µg/m³.
+
+> **CMAQ at the target valid time must never be a model input.**
+> In PLUME it appears only as a training-time teacher label; everything the model reads is available at issue time. The archive contains the full hourly record, so it is *possible* to build a leaking pipeline from it by accident. The `--causal-student` path in the reference code enforces the split — if you build your own loader, enforce it yourself, or your scores will not mean what you think they mean.
+
+Suggested protocol, matching the paper: train on **2016–2022**, select on **2023**, evaluate once on **2024**.
+
+---
+
+## Citation
+
+```bibtex
+@article{plume2025,
+  title   = {PLUME: A Physically Conditioned Decision-State World Model
+             for Air-Quality Exceedance Forecasting},
+  author  = {KAIST CVML Lab},
+  year    = {2025},
+  note    = {Code: https://github.com/2na-97/PLUME}
+}
+```
+
+## Acknowledgements
+
+Observations come from the Korean national air-quality monitoring network and regional partners; the reanalysis is CMAQ. Preprocessing and grid conventions began as a fork of the [Aurora](https://github.com/microsoft/aurora) stack.
+
+## License
+
+MIT for the packaging and derived arrays. Underlying observations and reanalysis remain subject to the terms of their original providers; check those before redistribution.