Spatio-temporal statistical models with applications to atmospheric processes

This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model

Species abundance dynamics under neutral assumptions: a Bayesian approach to the controversy

1. Hubbell's 'Unified Neutral Theory of Biodiversity and Biogeography' (UNTB) has generated much controversy about both the realism of its assumptions and how well it describes the species abundance dynamics in real communities. 2. We fit a discrete-time version of Hubbell's neutral model to long-term macro-moth (Lepidoptera) community data from the Rothamsted Insect Survey (RIS) light-traps network in the United Kingdom. 3. We relax the assumption of constant community size and use a hierarchical Bayesian approach to show that the model does not fit the data well as it would need parameter values that are impossible. 4. This is because the ecological communities fluctuate more than expected under neutrality. 5. The model, as presented here, can be extended to include environmental stochasticity, density-dependence, or changes in population sizes that are correlated between different species

Spatio-temporal statistical models with applications to atmospheric processes

This doctoral dissertation is presented as three self-contained papers. An introductory chapter considers traditional spatio-temporal statistical methods used in the atmospheric sciences from a statistical perspective. Although this section is primarily a review, many of the statistical issues considered have not been considered in the context of these methods and several open questions are posed. The first paper attempts to determine a means of characterizing the semiannual oscillation (SAO) spatial variation in the northern hemisphere extratropical height field. It was discovered that the midlatitude SAO in 500hPa geopotential height could be explained almost entirely as a result of spatial and temporal asymmetries in the annual variation of stationary eddies. It was concluded that the mechanism for the SAO in the northern hemisphere is a result of land-sea contrasts. The second paper examines the seasonal variability of mixed Rossby-gravity waves (MRGW) in lower stratospheric over the equatorial Pacific. Advanced cyclostationary time series techniques were used for analysis. It was found that there are significant twice-yearly peaks in MRGW activity. Analyses also suggested a convergence of horizontal momentum flux associated with these waves. In the third paper, a new spatio-temporal statistical model is proposed that attempts to consider the influence of both temporal and spatial variability. This method is mainly concerned with prediction in space and time, and provides a spatially descriptive and temporally dynamic model

A Hierarchical Spatial Model for Constructing Wind Fields from Scatterometer Data in the Labrador Sea

Wind fields are important for many geophysical reasons, but high resolution wind data over ocean regions are scarce and difficult to collect. A satellite-borne scatterometer produces high resolution wind data. We constructed a hierarchical spatial model for estimating wind fields over the Labrador sea region based on scatterometer data. The model incorporates spatial structure via a model of the u and v components of wind conditional on an unobserved pressure field. The conditional dependence is parameterized in this model through the physically based assumption of geostrophy. The pressure field is parameterized as a Gaussian random field with a stationary correlation function. The model produces realistic wind fields, but more importantly it appears to be able to reproduce the true pressure field, suggesting that the parameterization of geostrophy is useful. This further suggests that the model should be able to produce reasonable predictions outside of the data domain. 1 Introductio..

Ocean ensemble forecasting. Part I:Ensemble Mediterranean winds from a Bayesian hierarchical model

A Bayesian hierarchical model (BHM) is developed to estimate surface vector wind (SVW) fields and associated uncertainties over the Mediterranean Sea. The BHM–SVW incorporates data-stage inputs from analyses and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) and SVW retrievals from the QuikSCAT data record. The process-model stage of the BHM–SVW is based on a Rayleigh friction equation model for surface winds. Dynamical interpretations of posterior distributions of the BHM–SVW parameters are discussed. Ten realizations from the posterior distribution of the BHM–SVW are used to force the data-assimilation step of an experimental ensemble ocean forecast system for the Mediterranean Sea in order to create a set of ensemble initial conditions. The sequential data-assimilation method of the Mediterranean forecast system (MFS) is adapted to the ensemble implementation. Analyses of sample ensemble initial conditions for a single data-assimilation period in MFS are presented to demonstrate the multivariate impact of the BHM–SVW ensemble generation methodology. Ensemble initial-condition spread is quantified by computing standard deviations of ocean state variable fields over the ten ensemble members. The methodological findings in this article are of two kinds. From the perspective of statistical modelling, the process-model development is more closely related to physical balances than in previous work with models for the SVW. From the ocean forecast perspective, the generation of ocean ensemble initial conditions via BHM is shown to be practical for operational implementation in an ensemble ocean forecast system. Phenomenologically, ensemble spread generated via BHM–SVW occurs on ocean mesoscale time- and space-scales, in close association with strong synoptic-scale wind-forcing events. A companion article describes the impacts of the BHM–SVW ensemble method on the ocean forecast in comparisons with more traditional ensemble methods

Catalysing virtual collaboration : The experience of the remote TIES working groups

During the COVID-19 pandemic, the idea of collaboration and scientific exchange between members of the scientific community was enhanced by technology. Virtual meetings and work platforms have become common resources to continue generating research, partially replacing instances of joint in-person work before, during or after a conference. The idea of teleworking played a fundamental role in remote collaboration groups within The International Statistical Society (TIES), a community of interdisciplinary scientists such as statisticians, mathematicians, meteorologists, and biologists, among others working on quantitative methods to enhance solutions to environmental problems. In 2021 the Society launched three working groups with the aim of improving networking across the Society's members and develop creative collaboration, while advancing statistical and computational methods motivated by real-world driven applications in environmental research. Here, we provide insights from this virtual collaborative initiative