Media Summary: Kaitlyn Loftus (she/her): Kait is a LEAP postdoc at Columbia working on liquid Earth System Models (ESM) encode our knowledge about the physical world, enabling both short-term weather and long-term ... ABSTRACT: The formation of drizzle and rain via drop collision-coalescence (“warm rain” initiation) is a key component of Earth ...

Parameterizing Cloud Microphysics With Ml - Detailed Analysis & Overview

Kaitlyn Loftus (she/her): Kait is a LEAP postdoc at Columbia working on liquid Earth System Models (ESM) encode our knowledge about the physical world, enabling both short-term weather and long-term ... ABSTRACT: The formation of drizzle and rain via drop collision-coalescence (“warm rain” initiation) is a key component of Earth ... Machine learning is well suited for problems which have a complex structure that we don't understand well, but about which we ... This presentation instructs WRF users on cumulus On Tuesday October 19 at 3PM (GMT+2), we will discuss recent developments in

Recorded 03 February 2026. Damian Rouson of Lawrence Berkeley Laboratory presents "Learning Discussion of bin (explicit cloud particle size distribution) and bulk (moments of distribution) approaches to

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Parameterizing Cloud Microphysics with Machine Learning enabled Bayesian Parameter Inference
Parameterizing Cloud Microphysics with ML-Enabled Bayesian Parameter Inference by Kaitlyn Loftus
Parameterizing Cloud Microphysics with ML Enabled Bayesian Parameter Inference with Kaitlyn Loftus
Physics-informed machine learning of cloud microphysical processes
ML for Ice Microphysics  Challenges, Progress, and Opportunities
Toward an Improved Representation of Warm Cloud Microphysics in Earth System Models
'Machine' Learning Cloud Physics. Where Do We Stand? with Andrew Gettelman
181113 - Machine learning and cloud process parameterization for weather and climate models
WRF Physics: Cumulus Parameterization
ML@Hereon Episode 34: A bin-full of Clouds
Damian Rouson - Cloud microphysics via progressive refinement mimicking maximal info entropy
MAR572 L25 Cloud Microphysics
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Parameterizing Cloud Microphysics with Machine Learning enabled Bayesian Parameter Inference

Parameterizing Cloud Microphysics with Machine Learning enabled Bayesian Parameter Inference

ABSTRACT: Warm

Parameterizing Cloud Microphysics with ML-Enabled Bayesian Parameter Inference by Kaitlyn Loftus

Parameterizing Cloud Microphysics with ML-Enabled Bayesian Parameter Inference by Kaitlyn Loftus

Climate models can't resolve

Parameterizing Cloud Microphysics with ML Enabled Bayesian Parameter Inference with Kaitlyn Loftus

Parameterizing Cloud Microphysics with ML Enabled Bayesian Parameter Inference with Kaitlyn Loftus

https://doi.org/10.1029/2019MS001689 Kaitlyn Loftus (she/her): Kait is a LEAP postdoc at Columbia working on liquid

Physics-informed machine learning of cloud microphysical processes

Physics-informed machine learning of cloud microphysical processes

Earth System Models (ESM) encode our knowledge about the physical world, enabling both short-term weather and long-term ...

ML for Ice Microphysics  Challenges, Progress, and Opportunities

ML for Ice Microphysics Challenges, Progress, and Opportunities

ABSTRACT:

Toward an Improved Representation of Warm Cloud Microphysics in Earth System Models

Toward an Improved Representation of Warm Cloud Microphysics in Earth System Models

ABSTRACT: The formation of drizzle and rain via drop collision-coalescence (“warm rain” initiation) is a key component of Earth ...

'Machine' Learning Cloud Physics. Where Do We Stand? with Andrew Gettelman

'Machine' Learning Cloud Physics. Where Do We Stand? with Andrew Gettelman

Abstract:

181113 - Machine learning and cloud process parameterization for weather and climate models

181113 - Machine learning and cloud process parameterization for weather and climate models

Machine learning is well suited for problems which have a complex structure that we don't understand well, but about which we ...

WRF Physics: Cumulus Parameterization

WRF Physics: Cumulus Parameterization

This presentation instructs WRF users on cumulus

ML@Hereon Episode 34: A bin-full of Clouds

ML@Hereon Episode 34: A bin-full of Clouds

On Tuesday October 19 at 3PM (GMT+2), we will discuss recent developments in

Damian Rouson - Cloud microphysics via progressive refinement mimicking maximal info entropy

Damian Rouson - Cloud microphysics via progressive refinement mimicking maximal info entropy

Recorded 03 February 2026. Damian Rouson of Lawrence Berkeley Laboratory presents "Learning

MAR572 L25 Cloud Microphysics

MAR572 L25 Cloud Microphysics

Discussion of bin (explicit cloud particle size distribution) and bulk (moments of distribution) approaches to

Lecture13 Cloud Microphysics Overview

Lecture13 Cloud Microphysics Overview

ATMOS 5000 Lecture 13: