---
language: en
pretty_name: "MUR SST ML Benchmark (Pacific, Zarr)"
license: "other"
tags:
  - climate
  - sea-surface-temperature
  - zarr
  - benchmark
task_categories:
  - time-series-forecasting
---

# MUR SST ML Benchmark (Pacific, Zarr)

Machine-learning friendly **Zarr subset** of NASA/JPL GHRSST **MUR SST**.

- **Upstream source (public, no auth)**: `s3://mur-sst/zarr`
- **Subset**:
  - **Region**: Pacific (20–50°N, 180–240°E); longitude is stored as **0–360°E**
  - **Time**: 2018-01-01 → 2019-12-30 (729 daily frames; upstream coverage for this slice ends on 2019-12-30)
  - **Variable**: `analysed_sst` only (**float32, °C**)
- **Chunking for ML**: `(time, lat, lon) = (7, 256, 256)` (weekly windows)

Why `mur-sst/zarr-v1` during extraction?
- `mur-sst/zarr` is chunked with the **entire time axis in one chunk**, making time subsetting extremely inefficient.
- `mur-sst/zarr-v1` is time-chunked and enables practical extraction. The output dataset here is the requested ML rechunk.

## Notes (Hub viewer)

- The **Dataset Viewer is expected to be unavailable** because this repo contains a tar archive of a Zarr store (not a `datasets`-native format with named splits).

## Files in this dataset repo

Because Hugging Face dataset repos + Git LFS handle a **single large file** much more reliably than tens of thousands of tiny chunk files, the Zarr store is published as:

- `pacific_sst.zarr.tar` (a tar archive of the `pacific_sst.zarr/` directory)

To use it locally:

```bash
tar -xf pacific_sst.zarr.tar
```

## SST forecasting task definition

We define a next-week forecasting task:

- **Input**: 7 daily SST frames, shape `(7, H, W)`
- **Target**: next 7 daily SST frames, shape `(7, H, W)`
- **Goal**: learn a function that predicts the next week from the previous week

Windows are created from the `time` axis; you can use overlapping or non-overlapping windows (benchmark scripts default to non-overlapping).

## Train/val/test splits

Time-contiguous splits (no leakage):

- **Train**: 2018-01-01 → 2018-12-30
- **Val**: 2018-12-31 → 2019-06-30
- **Test**: 2019-07-01 → 2019-12-30

## Streaming code example

Local:

```python
import xarray as xr
ds = xr.open_zarr("pacific_sst.zarr", consolidated=True)
print(ds)
```

Remote (Hugging Face, after download):

```python
import xarray as xr

# 1) Download pacific_sst.zarr.tar from the Hub
# 2) tar -xf pacific_sst.zarr.tar
ds = xr.open_zarr("pacific_sst.zarr", consolidated=True)
print(ds)
```

## Benchmark results

Run:

```bash
tar -xf pacific_sst.zarr.tar
python bench/throughput_benchmark.py --local pacific_sst.zarr --s3-root mur-sst/zarr-v1
```

Measured on this machine (see `bench/throughput_benchmark.py` for details):

| mode | samples/sec | MB/sec | first_batch_sec |
|---|---:|---:|---:|
| local | 0.366 | 351.922 | 3.598 |
| streaming_s3 | 0.109 | 104.646 | 9.505 |