I am a Ph.D. student working to understand the nature of clouds and atmospheric flow. I think of the atmosphere as a stochastic complex system and believe that higher-level emergent laws are the future of our scientific understanding of weather and climate.
I hold a B.S. in Physics and an M.S. in Atmospheric Science. I also write a blog Thought Cloud where I cover clouds, turbulence, and other topics.
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 distinct 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
