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- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 107 - 107 (1page)
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The mixing between cumulus clouds and nearby environment is one of the largest sources of uncertainty in climate modeling. The air mass flux crosses into a cloud is called the entrainment and out of a cloud is called the detrainment. A stochastic mixing model with a machine learning technique is proposed for mass flux convection schemes. The model consists of the stochastic differential equations (SDEs) for the fractional entrainment rate, fractional detrainment rate, fractional dilution rate, and vertical acceleration. Unknowns in SDEs are parameterized using a deep neural network with the inputs of cloud and environment properties. The deep neural network is found to predict entrainment and detrainment rates ... 전체 초록 보기
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