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README.md

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+---
+license: other
+license_name: sdrbench
+license_link: https://sdrbench.github.io/
+pretty_name: "SDRBench: SCALE-LETKF"
+tags:
+- scientific-data
+- lossy-compression
+- sdrbench
+- hpc
+- sz3
+---
+
+# SDRBench — SCALE-LETKF
+
+Weather/climate simulation from the Local Ensemble Transform Kalman Filter (LETKF) data assimilation package for the SCALE-RM model. 13 fields of 98x1200x1200, single precision, little-endian; the _log variant holds log-transformed fields.
+
+This repository is an **unmodified mirror** of the SCALE-LETKF dataset from
+[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). `metadata/` holds the original SDRBench property and template
+files. The mirror is kept in sync automatically by
+[szcompressor/sdrbench](https://github.com/szcompressor/sdrbench).
+
+- **Data source:** RIKEN; contact Guo-Yuan Lien (guoyuan.lien@gmail.com)
+- **Total size:** 13.5 GB
+- **Format:** raw little-endian binary, C order (slowest dimension first)
+
+## 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: 98x1200x1200
+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}")
+```
+
+Other variants: `sdrbench.dataset("scale-letkf", "98x1200x1200_log")`.
+
+Files are downloaded on first use and cached; if Hugging Face is unreachable the package falls
+back to the original archive on Globus.
+
+Without the package, any file can be read with `huggingface_hub` and numpy:
+
+```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")
+x = np.fromfile(p, dtype="<f4").reshape((98, 1200, 1200))
+```
+
+## Contents
+
+
+### `98x1200x1200` (default)
+
+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).
+
+| 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` |
+
+### `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).
+
+| 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
+
+If you use this dataset, please cite SDRBench:
+
+```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 under the same terms as on the [SDRBench website](https://sdrbench.github.io/datasets.html).
+Rights remain with the original data providers listed above.