Integrating Child Care Services: Overcoming Structural Obstacles to Collaboration of Institutional and Community Agency Staffs

Social Work practice settings are so diversified that different perspectives inevitably develop among practitioners. These may undermine collaborative efforts between agencies. Child care services afford an example of a field requiring diversified agency settings and therefore vulnerable to development of contrasting perspectives. Some of the sources of an institutional perspective and of a community perspective are identified, as well as problems originating in lack of a shared perspective. Proposals for overcoming these problems and promoting integration of services comprises the final section of the paper

Hierarchical Bayesian Approach to Boundary Value Problems with Stochastic Boundary Conditions

This is the pre-print version of the article found in the Monthly Weather Review (http://journals.ametsoc.org/toc/mwre/138/10).Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of the relevant fluid equations. Often, limited area models are constructed to interpret intensive datasets collected over a specific region, from a variety of observational platforms. These data are noisy and they typically do not span the domain of interest uniformly in space and time. Traditional numerical procedures cannot easily account for these uncertainties. A hierarchical Bayesian modeling framework is developed for solving boundary value problems in such settings. By allowing the boundary process to be stochastic, and conditioning the interior process on this boundary, one can account for the uncertainties in the boundary process in a reasonable fashion. In the presence of data and all its uncertainties, this idea can be related through Bayes' Theorem to produce distributions of the interior process given the observational data. The method is illustrated with an example of obtaining atmospheric streamfunction fields in the Labrador Sea region, given scatterometer-derived observations of the surface wind field

Spatio-Temporal Hierarchical Bayesian Modeling: Tropical Ocean Surface Winds

This is the author's version of the article found in the Journal of the American Statistical Association. The publisher's version can be found at http://pubs.amstat.org/loi/jasa.Spatio-temporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal variability. The complexity of these processes and large number of observation/prediction locations preclude the use of traditional covariance-based space-time statistical methods. Alternatively, we focus on conditionally-specified (i.e., hierarchical) spatio-temporal models. These methods offer several advantages over traditional approaches. Primarily, physical and dynamical constraints are easily incorporated into the conditional formulation, so that the series of relatively simple, yet physically realistic, conditional models leads to a much more complicated space-time covariance structure than can be specified directly. Furthermore, by making use of the sparse structure inherent in the hierarchical approach, as well as multiresolution (wavelet) bases, the models are computable with very large datasets. This modeling approach was necessitated by a scientifically meaningful problem in the geosciences. Satellite-derived wind estimates have high spatial resolution but are limited in global coverage. In contrast, wind fields provided by the major weather centers provide complete coverage but have low spatial resolution. The goal is to combine these data in a manner that incorporates the space-time dynamics inherent in the surface wind field. This is an essential task to enable meteorological research as no complete high resolution surface wind datasets exist over the world oceans. High resolution datasets of this kind are crucial for improving our understanding of: global air-sea interactions affecting climate, tropical disturbances, and for driving large-scale ocean circulation models.Support for this research was provided for CKW, DN, and LMB by the NCAR Geophysical Statistics Project, sponsored by the National Science Foundation (NSF) under Grant DMS93-12686. Support for RFM and CKW is provided by the NCAR NSCAT Science Working Team cooperative agreement with NASA JPL. NCAR is supported in part by the NSF

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 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

A Bayesian tutorial for data assimilation

Abstract Data assimilation is the process by which observational data are fused with scientific information. The Bayesian paradigm provides a coherent probabilistic approach for combining information, and thus is an appropriate framework for data assimilation. Viewing data assimilation as a problem in Bayesian statistics is not new. However, the field of Bayesian statistics is rapidly evolving and new approaches for model construction and sampling have been utilized recently in a wide variety of disciplines to combine information. This article includes a brief introduction to Bayesian methods. Paying particular attention to data assimilation, we review linkages to optimal interpolation, kriging, Kalman filtering, smoothing, and variational analysis. Discussion is provided concerning Monte Carlo methods for implementing Bayesian analysis, including importance sampling, particle filtering, ensemble Kalman filtering, and Markov chain Monte Carlo sampling. Finally, hierarchical Bayesian modeling is reviewed. We indicate how this approach can be used to incorporate significant physically based prior information into statistical models, thereby accounting for uncertainty. The approach is illustrated in a simplified advection-diffusion model

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

This 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 socalled 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 froman 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

Ocean Ensemble Forecasting, Part II: Mediterranean Forecast System Response

This paper analyzes the ocean forecast response to surface vector wind (SVW) distributions generated by a Bayesian Hierarchical Model (BHM) developed in Part I (Milliff et al., 2009). A new method for Ocean Ensemble Forecasting (OEF), 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 pycnocline of the eddy field. The new method is compared to an ensemble response forced by ECMWF Ensemble Prediction System (EEPS) surface winds, and to an ensemble forecast started from perturbed initial conditions derived from an ad hoc Thermocline Intensified Random Perturbation (TIRP) method. The EEPS-OEF shows spread at the 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. The BHM-SVW-OEF method offers a practical and objective means for producing short-term forecast spread by modeling surface atmospheric forcing uncertainties that have maximum impact at the ocean mesoscales