Create README.md

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-regression
+  - image-to-image
+tags:
+  - meteorology
+  - weather
+  - ERA5
+  - ECMWF
+  - reanalysis
+  - gridded-data
+  - patches
+size_categories:
+  - 100K<n<1M
+---
+
+# ERA5 Patchified Dataset
+
+Patches extracted from ECMWF ERA5 reanalysis on a 0.25° global grid, tiled into 128×128 non-overlapping patches with float16 normalized channels. Designed for ML training — use alongside [IFS HRES open data](https://huggingface.co/datasets/meteolibre-dev/weather_ifs_hres_128_0dot025) at inference time for a train-on-reanalysis / infer-on-forecast workflow.
+
+## Data Structure
+
+Files are stored as Parquet, named:
+
+```
+era5_{first_snapshot}_{region}_patches_{group_idx:04d}_{file_idx:04d}.parquet
+```
+
+### Columns
+
+| Column | Type | Description |
+|---|---|---|
+| `ifs_data` | bytes | Raw float16 bytes of the (T, C, H, W) patch tensor |
+| `ifs_shape` | list[int] | Shape tuple, e.g. `[3, 77, 128, 128]` |
+| `ifs_dtype` | str | `"e"` (numpy half / float16) |
+| `channel_names` | list[str] | Ordered channel names (see below) |
+| `channel_offsets` | list[float] | Per-channel normalization offset |
+| `channel_scales` | list[float] | Per-channel normalization scale |
+| `elevation_data` | bytes | Float16 elevation patch (128, 128) |
+| `elevation_shape` | list[int] | `(128, 128)` |
+| `elevation_dtype` | str | `"e"` (float16) |
+| `epsg` | int | CRS, always `4326` |
+| `lon` | float | Center longitude of patch |
+| `lat` | float | Center latitude of patch |
+| `patch_x_idx` | int | X index in the regional grid |
+| `patch_y_idx` | int | Y index in the regional grid |
+| `region` | str | Region name (e.g. `europe`, `global`) |
+| `snapshot_labels` | list[str] | ISO labels of the T snapshots |
+| `time_spacing_hours` | int | Hours between snapshots (`6`) |
+| `resolution` | float | Grid resolution in degrees (`0.25`) |
+| `patch_size` | int | Spatial patch size (`128`) |
+| `source` | str | Always `"era5"` |
+
+### Recovering the Tensor
+
+```python
+import numpy as np
+import pyarrow.parquet as pq
+
+table = pq.read_table("era5_2024-06-01T0000Z_europe_patches_0000_0000.parquet")
+row = table.slice(0, 1).to_pydict()
+
+# Reconstruct tensor
+tensor = np.frombuffer(row["ifs_data"][0], dtype=row["ifs_dtype"][0]).reshape(row["ifs_shape"][0])
+# tensor shape: (T, C, 128, 128), float16
+
+# De-normalize
+for ci, (offset, scale) in enumerate(zip(row["channel_offsets"][0], row["channel_scales"][0])):
+    if scale != 0:
+        tensor[:, ci, :, :] = tensor[:, ci, :, :].astype(np.float32) * scale + offset
+```
+
+## Channels (77 total)
+
+### Surface (13 channels)
+
+| # | Name | Description | Unit | Offset | Scale |
+|---|---|---|---|---|---|
+| 1 | `mucape` | Convective available potential energy (surface-based) | J kg⁻¹ | 0 | 500 |
+| 2 | `2t` | 2m temperature | K | 273.15 | 40 |
+| 3 | `2d` | 2m dewpoint temperature | K | 273.15 | 30 |
+| 4 | `10u` | 10m U wind component | m s⁻¹ | 0 | 30 |
+| 5 | `10v` | 10m V wind component | m s⁻¹ | 0 | 30 |
+| 6 | `100u` | 100m U wind component | m s⁻¹ | 0 | 40 |
+| 7 | `100v` | 100m V wind component | m s⁻¹ | 0 | 40 |
+| 8 | `tp` | Total precipitation | m | 0 | 0.05 |
+| 9 | `sp` | Surface pressure | Pa | 101325 | 5000 |
+| 10 | `msl` | Mean sea level pressure | Pa | 101325 | 5000 |
+| 11 | `tcwv` | Total column water vapour | kg m⁻² | 0 | 50 |
+| 12 | `tcc` | Total cloud cover | (0–1) | 0 | 1 |
+| 13 | `lsm` | Land-sea mask | (0–1) | 0 | 1 |
+
+### Pressure Levels × 8 variables = 64 channels
+
+Levels: **1000, 925, 850, 700, 500, 300, 250, 200 hPa**
+
+| # | Prefix | Description | Unit | Offset | Scale |
+|---|---|---|---|---|---|
+| 14–21 | `t_{level}` | Temperature | K | 273.15 | 50 |
+| 22–29 | `u_{level}` | U wind component | m s⁻¹ | 0 | 60 |
+| 30–37 | `v_{level}` | V wind component | m s⁻¹ | 0 | 60 |
+| 38–45 | `q_{level}` | Specific humidity | kg kg⁻¹ | 0 | 0.02 |
+| 46–53 | `w_{level}` | Vertical velocity | Pa s⁻¹ | 0 | 5 |
+| 54–61 | `gh_{level}` | Geopotential height | m | 5000 | 30000 |
+| 62–69 | `vo_{level}` | Relative vorticity | s⁻¹ | 0 | 5×10⁻⁴ |
+| 70–77 | `r_{level}` | Relative humidity | % | 50 | 50 |
+
+Full channel name example: `t_850` = temperature at 850 hPa.
+
+## Normalization
+
+Values are stored normalized as float16:
+
+```
+normalized = (raw_value - offset) / scale
+```
+
+Recover raw values with:
+
+```
+raw_value = normalized * scale + offset
+```
+
+Normalization constants are **identical to the IFS HRES dataset**, enabling seamless cross-training (train on ERA5, infer on IFS HRES) without re-normalization.
+
+## Temporal Structure
+
+Each patch contains **T consecutive analysis snapshots** spaced **6 hours** apart (cycles 00, 06, 12, 18 UTC). The default is T=3 (18h window).
+
+Consecutive patch groups stride by T×6 hours for continuous temporal coverage with no gaps:
+
+```
+Group 1: 00z → 06z → 12z
+Group 2: 18z → 00z(+1d) → 06z(+1d)
+Group 3: 12z(+1d) → 18z(+1d) → 00z(+2d)
+...
+```
+
+## Spatial Coverage
+
+| Region | Bounding Box (lon_min, lat_min, lon_max, lat_max) |
+|---|---|
+| `global` | (-180, -90, 180, 90) |
+| `europe` | (-30, 30, 45, 75) |
+| `north_atlantic` | (-80, 20, 0, 70) |
+| `north_america` | (-140, 15, -50, 75) |
+| `asia` | (50, 0, 160, 75) |
+
+Grid: 0.25° × 0.25° regular lat-lon (EPSG:4326). Patches are non-overlapping 128×128 grid cells (≈32° × 32° at 0.25° resolution).
+
+## Comparison with IFS HRES Patchified Dataset
+
+| | ERA5 (this dataset) | IFS HRES |
+|---|---|---|
+| **Type** | Reanalysis (best-estimate historical) | Operational analysis (near-real-time) |
+| **Temporal range** | 1940 → present | Rolling 2–3 days only |
+| **Latency** | ~5 days (ERA5T) / ~2 months (final) | Near real-time |
+| **Resolution** | 0.25° | 0.25° (open data) / 0.08° (licensed) |
+| **Consistency** | Reanalysis = physically consistent | Model upgrades cause breaks |
+| **CAPE** | Surface-based CAPE | Most-unstable CAPE |
+| **Channels** | 77 (no `tprate`) | 78 (includes `tprate`) |
+| **Geopotential** | Height (m) after ÷9.80665 | Height (m) |
+| **Normalization** | Same offsets/scales | Same offsets/scales |
+
+**Recommended workflow**: Train on ERA5 (years of consistent data), infer on IFS HRES (real-time availability). The shared normalization and channel naming makes this a drop-in switch.
+
+## Elevation
+
+Each patch includes a 128×128 float16 elevation map derived from a global DEM, reprojected to the same 0.25° grid. Elevation is stored raw (meters above sea level), not normalized.
+
+## Source
+
+Data downloaded from the [Copernicus Climate Data Store (CDS)](https://cds.climate.copernicus.eu) via the `cdsapi` Python client.
+
+- Surface: [`reanalysis-era5-single-levels`](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels)
+- Pressure levels: [`reanalysis-era5-pressure-levels`](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels)
+
+## License
+
+CC-BY-4.0 — please attribute ECMWF / Copernicus Climate Change Service as the data source. See the [CDS terms of use](https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf) and [ERA5 licence](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels/licence).