Machine Learning Parameterizations from the Surface to the ... · Machine learning of surface layer...
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Machine Learning Parameterizations from the Surface to the Clouds David John Gagne, Rich Loft, Andrew Gettelman, Jack Chen, Tyler McCandless, Branko Kosovic, Tom Brummet, Sue Ellen Haupt, Bai Yang* National Center for Atmospheric Research, *ERT, Inc., FDR/ARL/NOAA Neural networks can emulate the distributions and sensitivities of the cloud-to-rain conversion processes in a bin microphysics scheme. Machine learning of surface layer energy fluxes improves on Monin-Obukhov similarity theory. Motivation We use machine learning to either emulate or improve on existing atmospheric parameterization. 1. Microphysics: Can we emulate a computationally-intense scheme within a more efficient scheme? 2. Surface Layer: Can we use many observations to train a ML model to improve on existing schemes? Microphysics Methods Problem: Use neural networks to emulate bin microphysics processes in bulk microphysics scheme Data: Run CAM6 globally for two years globally with TAU bin and MG2 bulk microphysics Inputs: QC, QR, NC, NR, density Outputs: Cloud and Rain Water Tendencies Models: Dense neural networks Surface Layer Methods Problem: Predict surface momentum and heating fluxes from surface atmospheric profiles Data: Flux and tower observations from Cabauw, Netherlands, and Scoville, Idaho Models: Random forests and neural networks for u*, θ*, and q* Surface Layer Partial Dependence Microphysics Partial Dependence Next Steps Fortran neural network and random forest parameterization modules have been developed. Now integrating Fortran ML parameterizations with WRF and CESM. Neural Network vs. TAU Bin MG2 Bulk vs. TAU Bin Friction Velocity Temperature Scale Moisture Scale Partial dependence shows the sensitivity of mean prediction to changes in input values. Cabauw Data U* θ* Q* MO 0.90 0.44 0.14 RF from Cabauw 0.93 0.82 0.73 RF from Idaho 0.90 0.77 0.49 ML Cross-Testing R 2 Please Contact Me Email: [email protected] Twitter: @DJGagneDos Github: djgagne
Transcript of Machine Learning Parameterizations from the Surface to the ... · Machine learning of surface layer...
Machine Learning Parameterizations from the Surface to the CloudsDavid John Gagne, Rich Loft, Andrew Gettelman, Jack Chen, Tyler McCandless, Branko Kosovic, Tom Brummet, Sue Ellen Haupt, Bai Yang*
National Center for Atmospheric Research, *ERT, Inc., FDR/ARL/NOAA
Neural networks can emulate the distributions and sensitivities of the cloud-to-rain conversion processes in a bin microphysics scheme.
Machine learning of surface layer energy fluxes improves on Monin-Obukhov similarity theory.
MotivationWe use machine learning to eitheremulate or improve on existing atmospheric parameterization.1. Microphysics: Can we emulate acomputationally-intense scheme within a more efficient scheme?2. Surface Layer: Can we use many observations to train a ML modelto improve on existing schemes?
Microphysics MethodsProblem: Use neural networks to emulate bin microphysics processes in bulk microphysics schemeData: Run CAM6 globally for two years globally with TAU bin and MG2 bulk microphysicsInputs: QC, QR, NC, NR, densityOutputs: Cloud and Rain Water TendenciesModels: Dense neural networks
Surface Layer MethodsProblem: Predict surface momentum and heating fluxes from surface atmospheric profilesData: Flux and tower observations from Cabauw, Netherlands, and Scoville, IdahoModels: Random forests and neural networks for u*, θ*, and q*
Surface Layer Partial Dependence
Microphysics Partial Dependence
Next StepsFortran neural network andrandom forest parameterization modules have been developed.Now integrating Fortran MLparameterizations with WRF andCESM.
Neural Network vs. TAU Bin
MG2 Bulk vs. TAU Bin
Friction Velocity Temperature Scale
Moisture Scale
Partial dependence shows the sensitivity of mean prediction to changes in input values.
Cabauw Data U* θ* Q*MO 0.90 0.44 0.14
RF from Cabauw 0.93 0.82 0.73RF from Idaho 0.90 0.77 0.49
ML Cross-Testing R2
Please Contact MeEmail: [email protected]: @DJGagneDosGithub: djgagne
