# Super Resolution Weather Eval

> Evaluates a model's ability to perform spatial super-resolution on global weather forecast data, specifically upscaling temperature and cloud coverage maps from 1° to 0.5° resolution. It measures pixel-wise reconstruction accuracy against high-resolution ground truth. Use when the user wants to benchmark on GraphCast-ERA5 Paired Dataset, or asks about evaluating this task. Reports MSE.

- Skill: `qhjqhj00/super-resolution-weather-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/super-resolution-weather-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/super-resolution-weather-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/super-resolution-weather-eval

---


# super-resolution-weather-eval

> Super Resolution On Global Weather Forecasts — Zhang et al. (2024) (arXiv:2409.11502, 2024)

## What this evaluates

Evaluates a model's ability to perform spatial super-resolution on global weather forecast data, specifically upscaling temperature and cloud coverage maps from 1° to 0.5° resolution. It measures pixel-wise reconstruction accuracy against high-resolution ground truth.

## Datasets

- **GraphCast-ERA5 Paired Dataset** — total 400; splits: train (-1), test (-1)

## Metrics

- `MSE` **(primary)** — range: other
  - Mean Squared Error: the average of the squared differences between predicted and ground truth pixel values. Formula: (1/N) * Σ(y_true - y_pred)^2.

## Input / output format

**Input**: Single-channel low-resolution (1°) weather map images representing temperature or cloud coverage.

**Output**: Single-channel high-resolution (0.5°) weather map images.

## Scoring recipe

```python
def compute_mse(predictions, ground_truth):
    # predictions and ground_truth are numpy arrays of identical shape
    diff = predictions - ground_truth
    mse = np.mean(diff ** 2)
    return mse
```

## Common pitfalls

- MSE heavily penalizes large pixel-wise errors but does not capture perceptual quality or meteorological utility (e.g., storm structure preservation).
- The evaluation uses a small dataset of 400 paired images without explicit train/val/test splits, making it difficult to assess generalization or overfitting.
- Qualitative visual inspection is used alongside MSE, but no standardized meteorological accuracy metrics (e.g., anomaly correlation, bias, or RMSE per variable) are reported.

## Evidence (verbatim from paper)

> To train our models, we used Mean Squared Error (MSE) loss to minimize the difference between outputs and ground truth and as an evaluation metric.

## Citation

```bibtex
@misc{zhang2024superresolution,
  title={Super Resolution On Global Weather Forecasts},
  author={Zhang et al. (2024)},
  year={2024},
  note={arXiv:2409.11502}
}
```

- arXiv: 2409.11502