Turbulent collapse and recovery are both observed to occur abruptly in the atmospheric stable boundary layer (SBL). The understanding and predictability of turbulent recovery remains limited, reducing numerical weather prediction accuracy. Previous studies have shown that regime occupation is the result of the net effect of highly variable processes, from turbulent to synoptic scales, making stochastic methods a compelling approach. Idealized stable boundary layer models have shown that under some circumstances, regimes can be related to the stable branches of model equilibria, and an additional unstable equilibrium is predicted. This work seeks to determine the extent to which the SBL regime occupation can be explained using a one-dimensional stochastic differential equation (SDE). The drift and diffusion coefficients of the SDE of an input time series are approximated from the statistics of its averaged time tendencies. These approximated coefficients are fit using Gaussian Process Regression. Probabilistic estimates of the system's equilibrium points are then found and used to create an empirical bifurcation diagram without making any prior assumptions on the dynamical form of the system. This data driven bifurcation diagram is compared to modelled predictions. The analysis is repeated on several meteorological towers around the world to assess the influence of local meteorological settings. This work provides empirical insights into the nature of regime dynamics and the extent to which the SBL displays hysteresis. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/12988 |
| Date | 18 May 2021 |
| Creators | Ramsey, Elizabeth |
| Contributors | Monahan, Adam Hugh |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
Page generated in 0.004 seconds