https://github.com/meteoswiss/opendata-nwp-demos
Notebook examples using model data for the Open Government Data initiative
https://github.com/meteoswiss/opendata-nwp-demos
jupyter-notebook meteorological-data numericalweatherpredictions ogd opendata processing python visualization weather-forecasting xarray
Last synced: 7 days ago
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Notebook examples using model data for the Open Government Data initiative
- Host: GitHub
- URL: https://github.com/meteoswiss/opendata-nwp-demos
- Owner: MeteoSwiss
- License: bsd-3-clause
- Created: 2025-01-17T15:03:51.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-05-09T08:56:26.000Z (11 days ago)
- Last Synced: 2025-05-09T09:38:08.802Z (11 days ago)
- Topics: jupyter-notebook, meteorological-data, numericalweatherpredictions, ogd, opendata, processing, python, visualization, weather-forecasting, xarray
- Language: Jupyter Notebook
- Homepage:
- Size: 6.78 MB
- Stars: 4
- Watchers: 7
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
OGD Model Data Access & Processing
Jupyter Notebook Examples Using MeteoSwiss NWP Data
This repository provides Jupyter notebook examples for accessing and processing numerical weather prediction (NWP) model data from **MeteoSwiss**, released through Switzerland’s **Open Government Data (OGD)** initiative.
---
## 📓 Notebooks
- [**01_retrieve_process_precip.ipynb**](01_retrieve_process_precip.ipynb)- Retrieve and load precipitation forecasts as an Xarray object, then process, analyze, and visualize the data using Python tools.
- [**02_download_soil_temp.ipynb**](02_download_soil_temp.ipynb) — Download forecast files to disk for offline storage, external tools, or advanced manual processing.
- [**03_calculate_wind_speed.ipynb**](03_calculate_wind_speed.ipynb) — Retrieve wind component forecasts as Xarray objects and derive the horizontal wind speed using the Python library [meteodata-lab](https://meteoswiss.github.io/meteodata-lab/).
## 🚀 Getting Started
### Install Dependencies
Clone the repository and install all required packages. This project requires **Python 3.11** and [Poetry](https://python-poetry.org/docs/) to manage dependencies and environments.
1. #### Install Python dependencies using Poetry
```bash
poetry install
```
2. #### Install the Jupyter kernel
Activate the Poetry environment and register it as a Jupyter kernel so it can be used within notebooks:
```bash
poetry run python -m ipykernel install --user --name=notebooks-nwp-env --display-name "Python (notebooks-nwp-env)"
```
### Open and Run Notebooks
You can run the notebooks using **Visual Studio Code** or **JupyterLab** — whichever you prefer.
#### Option A: Using Visual Studio Code
Make sure you have the following VS Code extensions installed:
- Python (by Microsoft)
- Jupyter (by Microsoft)
Once installed:
1. Open the project folder in VS Code.
2. Open a Jupyter notebook file, for example `01_retrieve_process_precip.ipynb`.
3. When prompted (or from the top-right kernel picker), select the kernel: **Python (notebooks-nwp-env)**.
> 💡 If you don't see the environment, restart VS Code after running the kernel installation step.
---
#### Option B: Using JupyterLab
If you don't have VS Code or prefer using JupyterLab:
1. Install JupyterLab using `pipx`:
```bash
pipx install jupyterlab
```
Don’t have `pipx` yet? Get it here: [https://pipx.pypa.io/stable/installation/](https://pipx.pypa.io/stable/installation/)
2. Launch JupyterLab:
```bash
jupyter lab
```
3. Open your notebook and select the kernel **Python (notebooks-nwp-env)** from the kernel menu.
## 📚 Related Documentation
For more context on the available numerical weather forecast data and how it’s structured, see:
🔗 [MeteoSwiss Forecast Data Documentation](https://opendatadocs.meteoswiss.ch/e-forecast-data/e2-e3-numerical-weather-forecasting-model)
## 💬 Feedbacks
Feel free to open issues to suggest improvements or contribute new examples!