scholarly journals A Bayesian modelling framework for tornado occurrences in North America

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincent Y.S. Cheng ◽  
George B. Arhonditsis ◽  
David M.L. Sills ◽  
William A. Gough ◽  
Heather Auld
Keyword(s):  
North America ◽  
Bayesian Modelling ◽  
Modelling Framework

scholarly journals Erratum: A Bayesian modelling framework for tornado occurrences in North America

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincent Y. S. Cheng ◽  
George B. Arhonditsis ◽  
David M. L. Sills ◽  
William A. Gough ◽  
Heather Auld
Keyword(s):  
North America ◽  
Bayesian Modelling ◽  
Modelling Framework

Spatio-temporal decomposition of geophysical signals in North America

2020 ◽  
Author(s):  
Aoibheann Brady ◽  
Jonathan Rougier ◽  
Bramha Dutt Vishwakarma ◽  
Yann Ziegler ◽  
Richard Westaway ◽  
...  
Keyword(s):  
North America ◽  
Sea Level Rise ◽  
Sea Level ◽  
Global Scale ◽  
Bayesian Hierarchical ◽  
Isostatic Adjustment ◽  
Modelling Framework ◽  
Vertical Land Motion ◽  
Early Results ◽  
Spatio Temporal

<p>Sea level rise is one of the most significant consequences of projected future changes in climate. One factor which influences sea level rise is vertical land motion (VLM) due to glacial isostatic adjustment (GIA), which changes the elevation of the ocean floor. Typically, GIA forward models are used for this purpose, but these are known to vary with the assumptions made about ice loading history and Earth structure. In this study, we implement a Bayesian hierarchical modelling framework to explore a data-driven VLM solution for North America, with the aim of separating out the overall signal into its GIA and hydrology (mass change) components. A Bayesian spatio-temporal model is implemented in INLA using satellite (GRACE) and in-situ (GPS) data as observations. Under the assumption that GIA varies in space but is constant in time, and that hydrology is both spatially- and temporally-variable, it is possible to separate the contributions of each component with an associated uncertainty level. Early results will be presented. Extensions to the BHM framework to investigate sea level rise at the global scale, such as the inclusion of additional processes and incorporation of increased volumes of data, will be discussed.</p>

scholarly journals Thermochronologic constraints on the origin of the Great Unconformity

2021 ◽  
Author(s):  
Kalin McDannell ◽  
C. Keller ◽  
William Guenthner ◽  
Peter Zeitler ◽  
David Shuster
Keyword(s):  
Snowball Earth ◽  
Plate Tectonic ◽  
Bayesian Modelling ◽  
Modelling Framework ◽  
High Magnitude ◽  
Full Picture ◽  
Plausible Mechanism ◽  
Thermal Histories ◽  
Rodinia Assembly ◽  
Continental Erosion

The origin of the phenomenon known as the Great Unconformity has been a fundamental yet unresolved problem in the geosciences for over a century. Recent hypotheses advocate either global continental exhumation of more than 3–4 km during Cryogenian (717–635 Ma) snowball Earth glaciations, or alternatively, diachronous episodic exhumation throughout the Neoproterozoic (1000–540 Ma) due to plate tectonic reorganization from supercontinent Rodinia assembly and breakup. To test these hypotheses, the temporal pattern of Neoproterozoic thermal histories were evaluated for four North American locations using previously published medium-to-low temperature thermochronology and geologic information. We present inverse time-temperature simulations within a Bayesian modelling framework that record a consistent signal of relatively rapid, high magnitude cooling of ~120–200°C interpreted as erosional exhumation of upper crustal basement during the Cryogenian. These models imply widespread, synchronous cooling consistent with at least ~3–5 km of unroofing during snowball Earth glaciations, but also demonstrate that plate tectonic drivers, with the potential to cause both exhumation and burial, may have significantly influenced the thermal history in regions that were undergoing deformation concomitant with glaciation. In the cratonic interior, however, glaciation remains the only plausible mechanism that satisfies the required timing, magnitude, and broad spatial pattern of continental erosion revealed by our thermochronological inversions. To obtain a full picture of the extent and synchroneity of such erosional exhumation, studies on stable cratonic crust below the Great Unconformity must be repeated on all continents.

A Bayesian modelling framework for the estimation of catch-at-age of commercially harvested fish species

2012 ◽  
Vol 69 (12) ◽  
pp. 2064-2076 ◽  
Author(s):  
David Hirst ◽  
Geir Storvik ◽  
Hanne Rognebakke ◽  
Magne Aldrin ◽  
Sondre Aanes ◽  
...  
Keyword(s):  
Age Determination ◽  
Stock Identification ◽  
Posterior Distributions ◽  
Bayesian Modelling ◽  
Bayesian Hierarchical ◽  
Modelling Framework ◽  
Reading Errors ◽  
Fishery Data ◽  
Age Reading ◽  
Modelling Approach

A Bayesian hierarchical model was developed to estimate catch-at-age from commercial fishery data. Most common forms of data can be utilized: age and length, length-stratified ages, and length only. There is no need to construct an age–length key. Both landings and discards can be estimated, as can the effects of age reading errors. Estimates can be made for difficult to distinguish stocks, where stock identification is only possible in some fish, for example, by using otoliths and age determination. Uncertainty in stock identification can be included in this modelling approach which allows errors in the estimates to be fully captured in their posterior distributions. An important component of this model is the inclusion of random effects to account for positive correlation in both fish size and age within the sampling units.

scholarly journals An assessment of geographical distribution of different plant functional types over North America simulated using the CLASS–CTEM modelling framework

2017 ◽  
Vol 14 (20) ◽  
pp. 4733-4753 ◽  
Author(s):  
Rudra K. Shrestha ◽  
Vivek K. Arora ◽  
Joe R. Melton ◽  
Laxmi Sushama
Keyword(s):  
North America ◽  
Geographical Distribution ◽  
Plant Functional Types ◽  
The Arctic ◽  
Vegetation Coverage ◽  
Fractional Coverage ◽  
Terrestrial Ecosystem Model ◽  
Modelling Framework ◽  
Functional Types ◽  
Vegetation Fractional Coverage

Abstract. The performance of the competition module of the CLASS–CTEM (Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model) modelling framework is assessed at 1° spatial resolution over North America by comparing the simulated geographical distribution of its plant functional types (PFTs) with two observation-based estimates. The model successfully reproduces the broad geographical distribution of trees, grasses and bare ground although limitations remain. In particular, compared to the two observation-based estimates, the simulated fractional vegetation coverage is lower in the arid southwest North American region and higher in the Arctic region. The lower-than-observed simulated vegetation coverage in the southwest region is attributed to lack of representation of shrubs in the model and plausible errors in the observation-based data sets. The observation-based data indicate vegetation fractional coverage of more than 60 % in this arid region, despite only 200–300 mm of precipitation that the region receives annually, and observation-based leaf area index (LAI) values in the region are lower than one. The higher-than-observed vegetation fractional coverage in the Arctic is likely due to the lack of representation of moss and lichen PFTs and also likely because of inadequate representation of permafrost in the model as a result of which the C3 grass PFT performs overly well in the region. The model generally reproduces the broad spatial distribution and the total area covered by the two primary tree PFTs (needleleaf evergreen trees, NDL-EVG; and broadleaf cold deciduous trees, BDL-DCD-CLD) reasonably well. The simulated fractional coverage of tree PFTs increases after the 1960s in response to the CO2 fertilization effect and climate warming. Differences between observed and simulated PFT coverages highlight model limitations and suggest that the inclusion of shrubs, and moss and lichen PFTs, and an adequate representation of permafrost will help improve model performance.

scholarly journals A Bayesian modelling framework to estimateCampylobacterprevalence and culture methods sensitivity: application to a chicken meat survey in Belgium

2008 ◽  
Vol 105 (6) ◽  
pp. 2002-2008 ◽  
Author(s):  
I. Habib ◽  
I. Sampers ◽  
M. Uyttendaele ◽  
L. De Zutter ◽  
D. Berkvens
Keyword(s):  
Chicken Meat ◽  
Bayesian Modelling ◽  
Culture Methods ◽  
Modelling Framework

scholarly journals An assessment of geographical distribution of different plant functional types over North America simulated using the CLASS-CTEM modelling framework

2017 ◽  
Author(s):  
Rudra K. Shrestha ◽  
Vivek K. Arora ◽  
Joe R. Melton ◽  
Laxmi Sushama
Keyword(s):  
North America ◽  
Geographical Distribution ◽  
Plant Functional Types ◽  
The Arctic ◽  
Vegetation Coverage ◽  
Fractional Coverage ◽  
Modelling Framework ◽  
South West ◽  
Functional Types ◽  
Vegetation Fractional Coverage

Abstract. The performance of the competition module of the CLASS-CTEM (Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model) modelling framework is assessed at 1° spatial resolution over North America by comparing the simulated geographical distribution of plant functional types (PFTs) with two observation-based estimates. The model successfully reproduces the broad geographical distribution of trees, grasses and bare ground although limitations remain. In particular, compared to the two observation-based estimates, the simulated fractional vegetation coverage is lower in the arid south-west North American region and higher in the Arctic region. The lower than observed simulated vegetation coverage in the south-west region is attributed to lack of representation of shrubs in the model and plausible errors in the observation-based data sets. The observation-based data indicates vegetation fractional coverage of more than 60 % in this arid region, despite only 200–300 mm of precipitation that the region receives annually and observation-based leaf area index (LAI) in the region are lower than one. The higher than observed vegetation fractional coverage in the Arctic is due to the lack of representation of moss and lichen PFTs and also likely because of inadequate representation of permafrost in the model as a result of which the C3 grass PFT performs overly well in the region. The model generally reproduces the broad spatial distribution and the total area covered by the two primary tree PFTs (needleleaf evergreen and broadleaf cold deciduous trees) reasonably well. The simulated fractional coverage of tree PFTs increases after 1960s in response to the CO2 fertilization effect and climate warming. Differences between observed and simulated PFT coverages highlight limitations in the model and provide insight into physical and structural processes that need improvement.

scholarly journals Evaluating sources of variability in inflorescence number, flower number and the progression of flowering in Sauvignon blanc using a Bayesian modelling framework

OENO One ◽  
2022 ◽  
Vol 56 (1) ◽  
pp. 1-15
Author(s):  
Amber K. Parker ◽  
Jaco Fourie ◽  
Mike C. T. Trought ◽  
Kapila Phalawatta ◽  
Esther Meenken ◽  
...  
Keyword(s):  
Fruit Set ◽  
Seasonal Temperature ◽  
Bayesian Modelling ◽  
Key Factors ◽  
Flower Number ◽  
Image Capture ◽  
Sauvignon Blanc ◽  
Linear Mixed Effects ◽  
Modelling Framework ◽  
Shoot Number

The time of flowering is key to understanding the development of grapevines. Flowering coincides with inflorescence initiation and fruit set, important determinants of yield. This research aimed to determine between and within-vine variability in 4-cane-pruned Sauvignon blanc inflorescence number per shoot, number of flowers per inflorescence and flowering progression using an objective method of assessing flowering via image capture and statistical analysis using a Bayesian modelling framework. The inflorescence number and number of flowers per inflorescence were measured by taking images over the flowering period. Flowering progression was assessed by counting open and closed flowers for each image over two seasons. An ordinal multinomial generalised linear mixed-effects model (GLMM) was fitted for inflorescence number, a Poisson GLMM for flower counts and a binomial GLMM for flowering progression. All the models were fitted and interpreted within a Bayesian modelling framework. Shoots arising from cane node one had lower numbers of inflorescences compared to those at nodes 3, 5 and 7, which were similar. The number of flowers per inflorescence was greater for basal inflorescences on a shoot than apical ones. Flowering was earlier, by two weeks, and faster in 2017/18 when compared to 2018/19 reflecting seasonal temperature differences. The time and duration of flowering varied at each inflorescence position along the cane. While basal inflorescences flowered later and apical earlier at lower insertion points on the shoot, the variability in flowering at each position on the vine dominated the date and duration of flowering.This is the first study using a Bayesian modelling framework to assess variability inflorescence presence and flower number, as well as flowering progression via objective quantification of open and closed flower counts rather than the more subjective method of visual estimation in the field or via cuttings. Although flower number differed for apical and basal bunches, little difference in timing and progression of flowering by these categories was observed. The node insertion point along a shoot was more important. Overall, the results indicate individual inflorescence variation and season are the key factors driving flowering variability and are most likely to impact fruit set and yield.

scholarly journals Species with more volatile population dynamics are differentially impacted by weather

2015 ◽  
Vol 11 (2) ◽  
pp. 20140792 ◽  
Author(s):  
Joshua G. Harrison ◽  
Arthur M. Shapiro ◽  
Anne E. Espeset ◽  
Christopher C. Nice ◽  
Joshua P. Jahner ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Southern Oscillation ◽  
Weather Conditions ◽  
Climatic Variation ◽  
Butterfly Species ◽  
Bayesian Modelling ◽  
Volatile Species ◽  
Modelling Framework ◽  
Stable Species ◽  
Abiotic Interactions

Climatic variation has been invoked as an explanation of population dynamics for a variety of taxa. Much work investigating the link between climatic forcings and population fluctuation uses single-taxon case studies. Here, we conduct comparative analyses of a multi-decadal dataset describing population dynamics of 50 co-occurring butterfly species at 10 sites in Northern California. Specifically, we explore the potential commonality of response to weather among species that encompass a gradient of population dynamics via a hierarchical Bayesian modelling framework. Results of this analysis demonstrate that certain weather conditions impact volatile, or irruptive, species differently as compared with relatively stable species. Notably, precipitation-related variables, including indices of the El Niño Southern Oscillation, have a more pronounced impact on the most volatile species. We hypothesize that these variables influence vegetation resource availability, and thus indirectly influence population dynamics of volatile taxa. As one of the first studies to show a common influence of weather among taxa with similar population dynamics, the results presented here suggest new lines of research in the field of biotic–abiotic interactions.

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