https://github.com/ray-chew/pycsam

pyCSAM is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis
https://github.com/ray-chew/pycsam

data-analysis gmted icon-model merit-dem orographic spectral-analysis topography weather-forecast

Last synced: about 2 months ago
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pyCSAM is a robust approach for approximating geodesic subgrid-scale orographic spectra with applications to weather forecasting and broader data analysis

• Host: GitHub
• URL: https://github.com/ray-chew/pycsam
• Owner: ray-chew
• License: gpl-3.0
• Created: 2024-03-18T11:16:39.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-05-14T20:48:22.000Z (8 months ago)
• Last Synced: 2024-05-15T17:07:12.832Z (8 months ago)
• Topics: data-analysis, gmted, icon-model, merit-dem, orographic, spectral-analysis, topography, weather-forecast
• Language: Python
• Homepage: https://ray-chew.github.io/pyCSAM/
• Size: 68.8 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.rst
  • License: LICENSE

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README

        


  

  

  



Constrained Spectral Approximation Method
























The Constrained Spectral Approximation Method (CSAM) is a physically sound and robust method for approximating the spectrum of subgrid-scale orography. It operates under the following constraints:

* Utilises a limited number of spectral modes (no more than 100)

* Significantly reduces the complexity of physical terrain by over 500 times

* Maintains the integrity of physical information to a large extent

* Compatible with unstructured geodesic grids

* Inherently scale-aware

This method is primarily used to represent terrain for weather forecasting purposes, but it also shows promise for broader data analysis applications.

---

**[Read the documentation here](https://ray-chew.github.io/pyCSAM/index.html)**

---

## Requirements

See [`requirements.txt`](https://github.com/ray-chew/pyCSAM/blob/main/requirements.txt)

> **NOTE:**  The Sphinx dependencies can be found in [`docs/conf.py`](https://github.com/ray-chew/pyCSAM/blob/main/docs/source/conf.py).

## Usage

### Installation

Fork this repository and clone your remote fork.

### Configuration

The user-defined input parameters are in the [`inputs`](https://github.com/ray-chew/pyCSAM/tree/main/inputs) subpackage. These parameters are imported into the run scripts in [`runs`](https://github.com/ray-chew/pyCSAM/tree/main/runs). 

### Execution

A simple setup can be found in [`runs.idealised_isosceles`](https://github.com/ray-chew/pyCSAM/blob/main/runs/idealised_isosceles.py). To execute this run script:

```console

python3 ./runs/idealised_isosceles.py

```

However, the codebase is structured such that the user can easily assemble a run script to define their own experiments. Refer to the documentation for the [available APIs](https://ray-chew.github.io/pyCSAM/api.html).

## License

GNU GPL v3 (tentative)

## Contributions

Refer to the open issues that require attention.

Any changes, improvements, or bug fixes can be submitted to upstream via a pull request.