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Improving Prognostic Moist Turbulence Parameterization with Machine Learning and Software Design
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Improving Prognostic Moist Turbulence Parameterization with Machine Learning and Software Design. |
| 개인저자 | McGibbon, Jeremy. |
| 단체저자명 | University of Washington. Atmospheric Sciences. |
| 발행사항 | [S.l.]: University of Washington., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 118 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088378403 |
| 학위논문주기 | Thesis (Ph.D.)--University of Washington, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Bretherton, Christopher S. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | The primary result of this work is that concepts from software design and machine learning may be used to improve moist turbulence parameterization in weather and climate models. We have seen relatively slow improvement of moist turbulence parameterization in past decades, and explore a radically different approach to parameterization involving machine learning. The core of the approach is to rely on a trusted source of training data, such as high-resolution models or reanalysis, to be used to train a machine learning algorithm to perform the closures normally defined by conventional parameterization.The Python packages sympl (System for Modelling Planets) and climt (Climate Modeling and Diagnostics Toolkit) are introduced. These packages are an attempt to rethink climate modelling frameworks from the ground up. The result defines expressive data structures that enforce software design best practices. It allows scientists to easily and reliably combine model components to represent the climate system at a desired level of complexity and enables users to fully understand what the model is doing.Random forest and polynomial regression are used as an alternate closure assumption in a higher-order turbulence closure scheme trained for use over the summertime Northeast Pacific stratocumulus to trade cumulus transition region. While the machine learning closures better match high-resolution model data over withheld validation samples compared to a state-of-the-art higher-order turbulence closure scheme, the resulting model is unstable when used prognostically.Within a first-order closure framework, an artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from ERA5 HIRES reanalysis data over the summertime Northeast Pacific stratocumulus to trade cumulus transition region. The network is trained prognostically using 7-day forecasts rather than using diagnosed instantaneous tendencies alone. The resulting model, Machine Assisted Reanalysis Boundary Layer Emulation (MARBLE), skillfully reproduces the boundary layer structure and cloud properties of the reanalysis data in 7-day single-column prognostic simulations over withheld testing periods. Radiative heating profiles are well-simulated, and the mean climatology and variability of the stratocumulus to cumulus transition are accurately reproduced. MARBLE more closely tracks the reanalysis than does a comparable configuration of the underlying forecast model. Similar results are obtained over the Southern Great Plains. |
| 일반주제명 | Atmospheric sciences. Artificial intelligence. Computer engineering. |
| 언어 | 영어 |
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