As part of my ongoing research into ecological data and its role in shaping virtual environments, I’ve been exploring the visualisation of historical rainfall records. This early experiment uses monthly rainfall data from the Bureau of Meteorology (BOM), dating back to 1904, to generate both visuals and sound in Touch Designer.
The process began by exporting the data as a CSV and filtering it to isolate the relevant rainfall values by month and year. From there, I formatted the dataset for use in Touch Designer, allowing me to begin translating numbers into visual and sonic form.
Step 1: Separate raw data from BOM and filter it down to rainfall values by month and year.
Step 2: Process the data for compatibility with Touch Designer.
Step 3: Animate a particle system and noise texture to represent rainfall amounts, while simultaneously using the same dataset to modulate a noise waveform, turning rainfall into a soundscape.
Step 4: Attach temporal markers (year and month) and render the outcome.
The result is a particle-based visualisation where the density and behaviour of particles correspond to rainfall levels, accompanied by an evolving sound environment. Both visual and audio elements are directly shaped by over a century of environmental data, offering a sensory encounter with long-term climate patterns.
This workflow is also a testbed for future applications. My goal is to extend these experiments into Unreal Engine, where real-time or pre-recorded environmental data can drive the behaviour of virtual landscapes. For example, rainfall levels could determine how water flows through a simulated forest, or how plant forms adapt and shift in response to changing ecological conditions.
By grounding digital environments in historical and real-time ecological data, I aim to create virtual spaces that embody more-than-human agencies, where water, plants, and climate patterns actively shape the experience. This experiment is just the first step toward developing immersive, data-driven works that reimagine our relationship with environmental cycles. Stay tuned for more experiments!
Thanks to Max Brading for assisting this development 🙂