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Classroom Contents
Lessons and Outlook for ML Parameterization of Sub Grid Atmospheric Physics From the Vantage of Emulating Cloud Superparameterization - Mike Pritchard
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- 1 Introduction
- 2 Motivation
- 3 Turbulence
- 4 Global modeling
- 5 The challenge
- 6 Multiskill modeling
- 7 Global storm resolving models
- 8 A silly first attempt
- 9 Aerosol cloud indirect effects
- 10 Regionalization
- 11 GPU Computing
- 12 Creative Complexity
- 13 Short Simulations
- 14 Course Graining
- 15 Super Crude Architecture
- 16 Lessons emerging
- 17 Feature engineering
- 18 Separate processes
- 19 Microphysical rates
- 20 Example
- 21 Constraints
- 22 Tradeoffs
- 23 Generalization
- 24 Strategy
- 25 Preprint
- 26 Results
- 27 Physical Credibility
- 28 Hyperparameter Tuning
- 29 Missing Information
- 30 Neural Network Tuning
- 31 Summary
- 32 Cognitive dissonance
- 33 Excitement
- 34 Thank you
- 35 Maria
- 36 Reporting failures
- 37 Retraining neural networks
- 38 Sampling
- 39 Failures
