4 papers
Understanding Ice Crystal Habit Diversity with Self-Supervised Learning
Joseph Ko, Hariprasath Govindarajan, Fredrik Lindsten +4
Ice-containing clouds strongly impact climate, but they are hard to model due to ice crystal habit (i.e., shape) diversity. We use self-supervised learning (SSL) to learn latent re…
Interactive Atmospheric Composition Emulation for Next-Generation Earth System Models
Seyed Mohammad Hassan Erfani, Kara Lamb, Susanne Bauer +3
Interactive composition simulations in Earth System Models (ESMs) are computationally expensive as they transport numerous gaseous and aerosol tracers at each timestep. This limits…
Towards a Climate OSSE Framework for Satellite Mission Design
Ann M. Fridlind, Gregory S. Elsaesser, Marcus van Lier-Walqui +24
The rich history of observing system simulation experiments (OSSEs) does not yet include a well-established framework for using climate models. The need for a climate OSSE is trigg…
A Machine Learning Framework for Predicting Microphysical Properties of Ice Crystals from Cloud Particle Imagery
Joseph Ko, Jerry Harrington, Kara Sulia +3
The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn stron…