An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using “Cloudy,” a New n‐Moment Bulk Microphysics Scheme
Published in Journal of Advances in Modeling Earth Systems, 2022
Recommended citation: Bieli, M., Dunbar, O. R. A., de Jong, E. K., Jaruga, A., Schneider, T., & Bischoff, T. (2022). An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using “Cloudy,” a new n-moment bulk microphysics scheme. Journal of Advances in Modeling Earth Systems, 14, e2022MS002994. https://doi.org/10.1029/2022MS002994 http://edejong-caltech.github.io/files/2022-cloudy.pdf
This paper demonstrates how to apply Ensemble Kalman in a learning pipeline to discover the parameters of a coalescence kernel for warm rain microphysics.
Recommended citation: Bieli, M., Dunbar, O. R. A., de Jong, E. K., Jaruga, A., Schneider, T., & Bischoff, T. (2022). An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using “Cloudy,” a new n-moment bulk microphysics scheme. Journal of Advances in Modeling Earth Systems, 14, e2022MS002994. https://doi.org/10.1029/2022MS002994