This repository contains a Jupyter notebook that demonstrates techniques for visualizing climate data (ScenarioMIP SSP3-7.0 scenario ERA5). The notebook creates field vector animation and temperature distributions over Europe.
This project focuses on:
- Accessing and processing climate data from the DestinE Earth Data Hub
- Creating static visualizations of wind vector fields and temperature distributions
- Generating animations of flowing wind patterns overlaid on temperature maps
- Applying advanced techniques for streamline visualization with dynamic transparency
- Data Access: Connects to the DestinE Earth Data Hub to retrieve IFS-NEMO high-resolution climate data
- Geographic Filtering: Extracts data for the European domain (35°N-71°N, 10°W-40°E)
- Visualization Techniques:
- Wind vector field visualization using streamlines
- Temperature distribution display with proper color mapping
- Animated flow visualization with dynamic transparency
- Animation Generation: Creates MP4 animations of wind patterns and temperature data
- Clone this repository:
git clone git@github.com:sebastien-tetaud/lombric-quiver.git- Create python env
conda create --name anim python==3.13
conda activate anim- Install xarray
python -m pip install "xarray[complete]"- Install required packages:
pip install -r requirements.txtThe notebook uses data from the DestinE Earth Data Hub, specifically:
- Dataset: ScenarioMIP-SSP3-7.0-IFS-NEMO-0001-high-sfc-v0.zarr
- Variables:
- t2m: 2-meter temperature (K)
- u10: 10-meter zonal wind component (m/s)
- v10: 10-meter meridional wind component (m/s)
The final output is an MP4 animation showing temperature as a color-coded map with animated wind streamlines flowing across Europe.
The notebook includes several parameters that can be adjusted:
- Geographic region (latitude_range, longitude_range)
- Time period (start_date, end_date)
- Animation properties (frames, fps)
- Visualization settings (colormap, density of streamlines)
Apache License 2.0