Chronosphere adapts climate data detail across space and time
Chronosphere: Space-Time Tessellation of Local Climate Experts
Computer Vision and Pattern RecognitionMachine Learning
Summary
Representing climate data is challenging because weather and climate change in complex ways across different places and times. The authors created Chronosphere, a machine learning method that divides space and time into adaptable regions, each capturing the right amount of climate detail. This approach combines local and global methods to better focus on important areas and times, improving how well climate information is understood and predicted. Their method outperforms existing ones, especially when applied to new locations or times.
What this means in practice
- •For climate modelers: Improve climate data encoding to enhance predictions for different regions and seasons using adaptable spatial and temporal detail.
- •For environmental monitoring teams: Generate more accurate local climate representations by adjusting model complexity based on time and location specifics.
Authors
Daniel Cher, Eric Xing, Kexing Li, Brian Wei, Isaac Corley, Nathan Jacobs
Abstract
We introduce Chronosphere, a spatio-temporal neural field that learns representations of climate. A central challenge in geographic representation learning is modeling environmental processes whose spatial and temporal complexity varies widely. Yet existing location encoders typically fix a single level of detail everywhere. Global bases such as spherical harmonics spread capacity uniformly across space and time. Localized bases resolve only predefined regions. Learned tessellations adapt, but are inefficient at representing higher frequencies. Chronosphere unifies these approaches, pairing an adaptive tessellation of learnable sites on the spacetime torus $S^2\times S^1$ with a shared bank of local basis functions. Both where capacity is placed and how much detail each region carries adapt to the data, across space and time. Trained to reconstruct climatology, Chronosphere matches or leads state-of-the-art location encoders across spatial and temporal tasks, with the largest gains under spatial and temporal transfer.