170 research outputs found
Assimilation of oceanographic observations with estimates of vertical background-error covariances by a bayesian hierarchical model
No full textnew method to estimate the vertical part of the background-error covariance matrix for an ocean variational data assimilation system is presented and tested in the Mediterranean operational daily analysis system. The operational, seasonally varying error covariances are compared with high-frequency estimates from a Bayesian Hierarchical Model (BHM) which estimates distributions for the vertical error covariances from two data-stage inputs: model anomalies and differences between model background and observations, i.e. so-called misfits. It is found that the posterior mean BHM-error covariance estimates that vary on 5-day time-scales reduce the misfits root mean square of the analysis vertical profiles of temperature and salinity by 10–20% versus analyses arising from covariances that vary on seasonal time-scales or those from the BHM given only model anomalies as data stage inputs
Hierarchical stochastic modelling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example
No full textWe present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, climate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our downscaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.This article is published as Mark L. Wildhaber, Christopher K. Wikle, Edward H. Moran, Christopher J. Anderson, Kristie J. Franz and Rima Dey Geological Society, London, Special Publications, 408, 119-145, 12 October 2015, doi: 10.1144/SP408.11.</p
A stochastic bioenergetics model-based approach to translating large river flow and temperature into fish population responses: the pallid sturgeon example
No full textIn managing fish populations, especially at-risk species, realistic mathematical models are needed to help predict population response to potential management actions in the context of environmental conditions and changing climate while effectively incorporating the stochastic nature of real world conditions. We provide a key component of such a model for the endangered pallid sturgeon (Scaphirhynchus albus) in the form of an individual-based bioenergetics model influenced not only by temperature but also by flow. This component is based on modification of a known individual-based bioenergetics model through incorporation of: the observed ontogenetic shift in pallid sturgeon diet from marcroinvertebrates to fish; the energetic costs of swimming under flowing-water conditions; and stochasticity. We provide an assessment of how differences in environmental conditions could potentially alter pallid sturgeon growth estimates, using observed temperature and velocity from channelized portions of the Lower Missouri River mainstem. We do this using separate relationships between the proportion of maximum consumption and fork length and swimming cost standard error estimates for fish captured above and below the Kansas River in the Lower Missouri River. Critical to our matching observed growth in the field with predicted growth based on observed environmental conditions was a two-step shift in diet from macroinvertebrates to fish.This article is published as Mark L. Wildhaber, Rima Dey, Christopher K. Wikle, Edward H. Moran, Christopher J. Anderson and Kristie J. Franz. Geological Society, London, Special Publications, 408, 101-118, 28 May 2015. doi: 10.1144/SP408.10.</p
Ocean ensemble forecasting. Part I:Ensemble Mediterranean winds from a Bayesian hierarchical model
No full textA 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
Ocean ensemble forecasting. Part II: Mediterranean Forecast System response
No full textThis article analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian hierarchical model (BHM) developed in Part I of this series. A new method for ocean ensemble forecasting (OEF), the so-called BHM-SVW-OEF, is described. BHM-SVW realizations are used to produce and force perturbations in the ocean state during 14 day analysis and 10 day
forecast cycles of the Mediterranean Forecast System (MFS). The BHM-SVW-OEF ocean response spread is amplified at the mesoscales and in the pycnocline of the eddy field. The new method is compared with an ensemble response forced by European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EEPS) surface winds, and with an ensemble forecast started from perturbed initial conditions derived from an ad hoc thermocline intensified random perturbation (TIRP) method. The EEPS-OEF shows spread on basin scales while the TIRP-OEF response is mesoscale-intensified as in the BHM-SVW-OEF response. TIRP-OEF perturbations fill more of the MFS domain, while the BHM-SVW-OEF
perturbations are more location-specific, concentrating ensemble spread at the sites where the ocean-model response to uncertainty in the surface wind forcing is largest
Space, uncertainty, and the environment:honoring the distinguished career of noel Cressie
No full textA special session and dinner at the July 2023 conference Spatial Statistics 2023: Climate and the Environment was dedicated to honoring the career of Distinguished Professor Noel Cressie (University of Wollongong, Australia), who was in attendance. This article provides a very brief overview of Prof. Cressie's career and concludes with the text of a speech delivered by the first author at the conference dinner in honor of Prof. Cressie
Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model
No full textA 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 tophysicalbalances than inpreviousworkwithmodels for the SVW.Fromthe
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.Published858–878JCR Journalembargoed_2014050
Appendix A. A description of the Gibbs sampler, which uses Markov chain Monte Carlo (MCMC) methods.
No full textA description of the Gibbs sampler, which uses Markov chain Monte Carlo (MCMC) methods
José-María Montero, Gema Fernández-Avilés, and Jorge Mateu: Spatial and Spatio-Temporal Geostatistical Modeling and Kriging
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