11 Apr 2024
 | 11 Apr 2024
Status: this preprint is open for discussion.

Quantifying the Oscillatory Evolution of Simulated Boundary-Layer Cloud Fields Using Gaussian Process Regression

Gunho Oh and Philip H. Austin

Abstract. Average properties of the cloud field, such as cloud size distribution and cloud fraction, have previously been observed to show periodic, oscillatory changes. Identifying this behaviour, however, remains difficult due to the intrinsic variability of the boundary-layer cloud distribution. We use the Gaussian Process (GP) regression to identify this oscillatory behaviour in the statistical distributions of individual cloud properties. Individual cloud samples are retrieved from high-resolution LES model results, and the distribution of cloud sizes is modelled as a power-law distribution. We construct the time-series for the slope of the cloud size distribution b, a slope that is consistent with satellite observations of marine boundary-layer clouds, by observing the changes in the slope of the modelled cloud size distribution. Then, we build a GP model based on prior assumptions about the cloud field following observational studies: a boundary-layer cloud field goes through a phase of relatively strong convection where large clouds dominate, followed by a phase of relatively weak convection where precipitation causes formation of cold pools and suppression of convective growth. The GP model successfully identifies oscillatory motions from the noisy time-series, with a period of 95 ± 3.2 minutes. Furthermore, we examine the time-series of cloud fraction fc and average vertical mass flux M, whose periods were 93 ± 2.5 and 93 ± 3.7 minutes, respectively. The oscillations reveal the role of precipitation in governing convective activities through recharge-discharge cycles.

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Gunho Oh and Philip H. Austin

Status: open (until 12 Jun 2024)

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  • RC1: 'Comment on egusphere-2024-352', Anonymous Referee #1, 06 May 2024 reply
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Gunho Oh and Philip H. Austin

Model code and software

SAM Loren Oh

Gunho Oh and Philip H. Austin


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Short summary
It is difficult to study the behaviour of a cloud field, due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field, and introduce numerical techniques based on machine learning algorithms to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify underlying behaviours from noisy observations.