I am a Ph.D. student working to understand the statistical structure of Earth's atmosphere. Rather than assuming a thunderstorm and a wind gust are created by distinct physical mechanisms, I advocate for viewing the entire atmosphere as a single, continuous, dynamical object across the dimensions of space, time, size, and lifetime.
I plan to join the Santa Fe Institute as an Omidyar Postdoctoral Fellow in fall of 2026. I write a blog Thought Cloud which covers clouds, turbulence, and other topics. I also create ceramics, with a particular focus on teaware.
Atmospheric flow is commonly thought to be controlled by several distinct physical mechanisms, each operating at a unique spatial scale. We found1 that observations are incompatible with this picture but support a lesser-known "anisotropic" theory of turbulence. The consequences are shown in the animation,2 derived from a vertical cross-section of an anisotropic turbulence simulation. Larger, flatter circulations continually deform into more circular circulations as the observation scale is decreased. Despite being proposed 40 years ago, our study1 was the first independent test of the theory.
1 Preprint: Global sonde datasets do not support a mesoscale transition in the turbulent energy cascade
2 Software: scaleinvariance
Clouds are widely assumed to be created by a large number of separable dynamical mechanisms, each operating at a unique spatial scale. This view is inconsistent with "scale invariance",1 a property we found applies widely to cloud size and shape2,3 but was previously obscured by measurement biases.4 Our findings suggest a more parsimonious understanding of cloud dynamics, with implications for climate modeling.1
1 Blog: The ignored lesson of cloud shape
2 Paper: Climatologically invariant scale invariance seen in distributions of cloud horizontal sizes
3 Paper: Toward less subjective metrics for quantifying the shape and organization of clouds
4 Paper: Finite domains cause bias in measured and modeled distributions of cloud sizes
I maintain two Python packages for simulation and analysis of scale invariant objects and fields. objscale provides fractal dimension and size distribution analysis of objects such as clouds viewed from space, implementing our novel recommended methodologies.1,2,3 scaleinvariance enables optimized simulation of arbitrary scale invariant fields using Lovejoy and Schertzer's "Fractionally Integrated Flux" algorithm (left)4, in addition to routines for Hurst, spectral, and multifractal exponent analyses. My multifractal explorer serves as a visual playground for these processes.
1 Paper: Finite domains cause bias in measured and modeled distributions of cloud sizes
2 Paper: Toward less subjective metrics for quantifying the shape and organization of clouds
3 Blog: On form and pattern in fractal clouds
4 Blog: How to visualize scale invariance
