edu.ucar.opensky::articles:27331 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T18:45:33.505295 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d77948ww https://doi.org/10.5281/zenodo.7487288 https Related Dataset #1 Reduced Order Modeling for Linearized Representations of Microphysical Process Rates https://doi.org/10.5281/zenodo.10621784 https Related Software #1 kdlamb/neural-dsd: Code repository for Reduced-order modeling for linearized representations1 of microphysical process rates Reduced-order modeling for linearized representations of microphysical process rates 2024-07-01T00:00:00Z publication https://n2t.org/ark:/85065/d77948ww https://doi.org/10.1029/2023MS003918 Lamb, K. D. author van Lier-Walqui, M. author Santos, S. author Morrison, Hugh author UCAR/NCAR - Library publisher Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data-driven reduced-order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision-coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision-coalescence in models. We demonstrate how deep learning based reduced-order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision-coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment-based representations. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation