---
license: other
license_name: sdrbench
license_link: https://sdrbench.github.io/
pretty_name: "SDRBench: SCALE-LETKF"
tags:
- scientific-data
- lossy-compression
- sdrbench
- hpc
- sz3
configs:
- config_name: files
  data_files: files.jsonl
  default: true
---

# SDRBench — SCALE-LETKF

Weather simulation from the SCALE-RM model with LETKF data assimilation (RIKEN): 12 variables on 98 x 1200 x 1200 (z, y, x), single precision, little-endian.

> **Raw binary arrays, not an Arrow/Parquet dataset.** `datasets.load_dataset("sdrbench/scale-letkf")` returns the table of files (`files.jsonl`: path, dtype, shape, sha256). Read the arrays with the `sdrbench` package or `hf_hub_download` + numpy as shown below.

This repository is an **unmodified mirror** of the SCALE-LETKF data of [SDRBench](https://sdrbench.github.io/datasets.html), the
Scientific Data Reduction Benchmark. The originals are hosted by Argonne National Laboratory on Globus;
every archive was unpacked and its files uploaded byte-for-byte (sha256 verified) (revision `18665ddfb9`). `metadata/` holds
the original SDRBench property and template files. Kept in sync automatically by
[szcompressor/sdrbench](https://github.com/szcompressor/sdrbench).

- **Data provider:** RIKEN; contact Guo-Yuan Lien (guoyuan.lien@gmail.com)
- **Size:** 13.5 GB in 2 variant(s)
- **Format:** raw little-endian binary, C order (slowest dimension first); dtype and shape per field below

## Usage

```bash
pip install "sdrbench[sz3]"     # sdrbench + pysz (SZ3)
```

```python
import numpy as np
import sdrbench
from pysz import sz, szConfig, szErrorBoundMode

ds = sdrbench.dataset("scale-letkf")                # default variant "original"
print(ds.fields)
x = ds["PRES"]                     # numpy array, dtype <f4, shape (98, 1200, 1200)

# compress with SZ3 (pysz) at a 1e-3 value-range-relative error bound
conf = szConfig()
conf.errorBoundMode = szErrorBoundMode.REL
conf.relErrorBound = 1e-3
compressed, ratio = sz.compress(np.ascontiguousarray(x), conf)
y, _ = sz.decompress(compressed, x.dtype.type, x.shape)
max_err, psnr, nrmse = sz.verify(np.asarray(x), y)
print(f"ratio {ratio:.1f}x, PSNR {psnr:.1f} dB, max error {max_err:.3g}")
```

Files are fetched on first use (in parallel with `ds.download("data/")`) and cached, pinned to the revision
of the installed `sdrbench` release; if Hugging Face is unreachable the package falls back to the original
archive on Globus. Existing local SDRBench copies can be used with `sdrbench.dataset("scale-letkf", root="/path")`.

Without the package, with `huggingface_hub` and numpy only:

```python
from huggingface_hub import hf_hub_download
import numpy as np
p = hf_hub_download("sdrbench/scale-letkf", "98x1200x1200/PRES-98x1200x1200.f32", repo_type="dataset", revision="18665ddfb989801761218b81e665072f998a23ff")
x = np.fromfile(p, dtype="<f4").reshape((98, 1200, 1200))
```

## Variants and fields


### `original` (default)

`sdrbench.dataset("scale-letkf", "original")` — folder `98x1200x1200/` — 12 files, 6.8 GB, from [SDRBENCH-SCALE-98x1200x1200.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/SCALE_LETKF/SDRBENCH-SCALE-98x1200x1200.tar.gz).

All variables.

| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `PRES` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/PRES-98x1200x1200.f32` |
| `QC` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QC-98x1200x1200.f32` |
| `QG` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QG-98x1200x1200.f32` |
| `QI` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QI-98x1200x1200.f32` |
| `QR` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QR-98x1200x1200.f32` |
| `QS` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QS-98x1200x1200.f32` |
| `QV` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/QV-98x1200x1200.f32` |
| `RH` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/RH-98x1200x1200.f32` |
| `T` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/T-98x1200x1200.f32` |
| `U` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/U-98x1200x1200.f32` |
| `V` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/V-98x1200x1200.f32` |
| `W` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200/W-98x1200x1200.f32` |

### `log`

`sdrbench.dataset("scale-letkf", "log")` — folder `98x1200x1200_log/` — 12 files, 6.8 GB, from [SDRBENCH-SCALE-98x1200x1200_log.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/SCALE_LETKF/SDRBENCH-SCALE-98x1200x1200_log.tar.gz).

The hydrometeor and vapour mixing ratios log10-transformed; the other variables unchanged.

| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `PRES` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/PRES-98x1200x1200.f32` |
| `QC_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QC-98x1200x1200.log10.f32` |
| `QG_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QG-98x1200x1200.log10.f32` |
| `QI_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QI-98x1200x1200.log10.f32` |
| `QR_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QR-98x1200x1200.log10.f32` |
| `QS_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QS-98x1200x1200.log10.f32` |
| `QV_log10` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/QV-98x1200x1200.log10.f32` |
| `RH` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/RH-98x1200x1200.f32` |
| `T` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/T-98x1200x1200.f32` |
| `U` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/U-98x1200x1200.f32` |
| `V` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/V-98x1200x1200.f32` |
| `W` | `<f4` | 98 x 1200 x 1200 | `98x1200x1200_log/W-98x1200x1200.f32` |

## Citation

Please cite SDRBench; `sdrbench cite scale-letkf` prints it:

```bibtex
@inproceedings{zhao2020sdrbench,
  title     = {{SDRBench}: Scientific Data Reduction Benchmark for Lossy Compressors},
  author    = {Zhao, Kai and Di, Sheng and Liang, Xin and Li, Sihuan and Tao, Dingwen and Chen, Zizhong and Cappello, Franck},
  booktitle = {2020 IEEE International Conference on Big Data (Big Data)},
  pages     = {2716--2724},
  year      = {2020}
}
```

## License and terms

The data are distributed by the SDRBench team for research use under the terms of the original
data providers listed above (see the [SDRBench website](https://sdrbench.github.io/datasets.html)); please acknowledge them as
requested. Rights remain with the data providers.