Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data
Enforcing sparse structure within learning has led to significant advances in the field of data-
driven discovery of dynamical systems. However, such methods require access not only to …
driven discovery of dynamical systems. However, such methods require access not only to …
[PDF][PDF] EnsembleKalmanProcesses. jl: Derivative-free ensemble-based model calibration
R Oliver, I Lopez-Gomez, A Garbuno-Iñigo… - Journal of Open …, 2022 - joss.theoj.org
EnsembleKalmanProcesses. jl is a Julia-based toolbox that can be used for a broad class of
black-box gradient-free optimization problems. Specifically, the tools enable the …
black-box gradient-free optimization problems. Specifically, the tools enable the …
High‐dimensional covariance estimation from a small number of samples
We synthesize knowledge from numerical weather prediction, inverse theory, and statistics
to address the problem of estimating a high‐dimensional covariance matrix from a small …
to address the problem of estimating a high‐dimensional covariance matrix from a small …
[PDF][PDF] New developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-stepping
PySDM and the accompanying PySDM-examples packages are open-source modeling
tools for computational studies of atmospheric clouds, aerosols, and precipitation. The …
tools for computational studies of atmospheric clouds, aerosols, and precipitation. The …
[PDF][PDF] CalibrateEmulateSample. jl: Accelerated Parametric Uncertainty Quantification
R Oliver, M Bieli, A Garbuno-Iñigo… - Journal of Open …, 2024 - joss.theoj.org
A Julia language (Bezanson et al., 2017) package providing practical and modular
implementation of “Calibrate, Emulate, Sample”(Cleary et al., 2021), hereafter CES, an …
implementation of “Calibrate, Emulate, Sample”(Cleary et al., 2021), hereafter CES, an …
Spanning the gap from bulk to bin: A novel spectral microphysics method
Microphysics methods for climate models and numerical weather prediction typically track
one, two, or three moments of a droplet size distribution for various categories of liquid, ice …
one, two, or three moments of a droplet size distribution for various categories of liquid, ice …
Training warm‐rain bulk microphysics schemes using super‐droplet simulations
Cloud microphysics is a critical aspect of the Earth's climate system, which involves
processes at the nano‐and micrometer scales of droplets and ice particles. In climate …
processes at the nano‐and micrometer scales of droplets and ice particles. In climate …
Breakups are complicated: An efficient representation of collisional breakup in the superdroplet method
A key constraint of particle-based methods for modeling cloud microphysics is the
conservation of total particle number, which is required for computational tractability. The …
conservation of total particle number, which is required for computational tractability. The …
[HTML][HTML] Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
A key constraint of particle-based methods for modeling cloud microphysics is the
conservation of total particle number, which is required for computational tractability. The …
conservation of total particle number, which is required for computational tractability. The …
Theoretical Investigations of the Water Cycle on Earth and Other Planets
K Loftus - 2023 - search.proquest.com
Abstract From exoplanets to Solar System bodies to modern Earth, the multi-scale and
interconnected processes of the water cycle are fundamental drivers of planetary climate …
interconnected processes of the water cycle are fundamental drivers of planetary climate …