Bayesian ranking and slection of fishing boat efficiencies

The steadily accumulating literature on technical efficiency in fisheries attests to the importance of efficiency as an indicator of fleet condition and as an object of management concern. In this paper, we extend previous work by presenting a Bayesian hierarchical approach that yields both efficiency estimates and, as a byproduct of the estimation algorithm, probabilistic rankings of the relative technical efficiencies of fishing boats. The estimation algorithm is based on recent advances in Markov Chain Monte Carlo (MCMC) methods— Gibbs sampling, in particular—which have not been widely used in fisheries economics. We apply the method to a sample of 10,865 boat trips in the US Pacific hake (or whiting) fishery during 1987–2003. We uncover systematic differences between efficiency rankings based on sample mean efficiency estimates and those that exploit the full posterior distributions of boat efficiencies to estimate the probability that a given boat has the highest true mean efficiency.

Potential Predictability and AMIP Implications of Midlatitude Climate Variability in Two General Circulation Models

Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93. The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST. Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.

The European Flood Alert System and the communication, perception, and use of ensemble predictions for operational flood risk management

Although ensemble prediction systems (EPS) are increasingly promoted as the scientific state-of-the-art for operational flood forecasting, the communication, perception, and use of the resulting alerts have received much less attention. Using a variety of qualitative research methods, including direct user feedback at training workshops, participant observation during site visits to 25 forecasting centres across Europe, and in-depth interviews with 69 forecasters, civil protection officials, and policy makers involved in operational flood risk management in 17 European countries, this article discusses the perception, communication, and use of European Flood Alert System (EFAS) alerts in operational flood management. In particular, this article describes how the design of EFAS alerts has evolved in response to user feedback and desires for a hydrographic-like way of visualizing EFAS outputs. It also documents a variety of forecaster perceptions about the value and skill of EFAS forecasts and the best way of using them to inform operational decision making. EFAS flood alerts were generally welcomed by flood forecasters as a sort of ‘pre-alert’ to spur greater internal vigilance. In most cases, however, they did not lead, by themselves, to further preparatory action or to earlier warnings to the public or emergency services. Their hesitancy to act in response to medium-term, probabilistic alerts highlights some wider institutional obstacles to the hopes in the research community that EPS will be readily embraced by operational forecasters and lead to immediate improvements in flood incident management. The EFAS experience offers lessons for other hydrological services seeking to implement EPS operationally for flood forecasting and warning. Copyright © 2012 John Wiley & Sons, Ltd.

The long-term safety and efficacy of bilateral transplantation of human fetal striatal tissue in patients with mild to moderate Huntington's disease.

Huntington's disease (HD) is a fatal autosomal dominant neurodegenerative disease involving progressive motor, cognitive and behavioural decline, leading to death approximately 20 years after motor onset. The disease is characterised pathologically by an early and progressive striatal neuronal cell loss and atrophy, which has provided the rationale for first clinical trials of neural repair using fetal striatal cell transplantation. Between 2000 and 2003, the 'NEST-UK' consortium carried out bilateral striatal transplants of human fetal striatal tissue in five HD patients. This paper describes the long-term follow up over a 3-10-year postoperative period of the patients, grafted and non-grafted, recruited to this cohort using the 'Core assessment program for intracerebral transplantations-HD' assessment protocol. No significant differences were found over time between the patients, grafted and non-grafted, on any subscore of the Unified Huntington's Disease Rating Scale, nor on the Mini Mental State Examination. There was a trend towards a slowing of progression on some timed motor tasks in four of the five patients with transplants, but overall, the trial showed no significant benefit of striatal allografts in comparison with a reference cohort of patients without grafts. Importantly, no significant adverse or placebo effects were seen. Notably, the raclopride positron emission tomography (PET) signal in individuals with transplants, indicated that there was no obvious surviving striatal graft tissue. This study concludes that fetal striatal allografting in HD is safe. While no sustained functional benefit was seen, we conclude that this may relate to the small amount of tissue that was grafted in this safety study compared with other reports of more successful transplants in patients with HD.

Contrasting probabilistic scoring rules

There are several scoring rules that one can choose from in order to score probabilistic forecasting models or estimate model parameters. Whilst it is generally agreed that proper scoring rules are preferable, there is no clear criterion for preferring one proper scoring rule above another. This manuscript compares and contrasts some commonly used proper scoring rules and provides guidance on scoring rule selection. In particular, it is shown that the logarithmic scoring rule prefers erring with more uncertainty, the spherical scoring rule prefers erring with lower uncertainty, whereas the other scoring rules are indifferent to either option.

Wind gust estimation for Mid-European winter storms: towards a probabilistic view

Three wind gust estimation (WGE) methods implemented in the numerical weather prediction (NWP) model COSMO-CLM are evaluated with respect to their forecast quality using skill scores. Two methods estimate gusts locally from mean wind speed and the turbulence state of the atmosphere, while the third one considers the mixing-down of high momentum within the planetary boundary layer (WGE Brasseur). One hundred and fifty-eight windstorms from the last four decades are simulated and results are compared with gust observations at 37 stations in Germany. Skill scores reveal that the local WGE methods show an overall better behaviour, whilst WGE Brasseur performs less well except for mountain regions. The here introduced WGE turbulent kinetic energy (TKE) permits a probabilistic interpretation using statistical characteristics of gusts at observational sites for an assessment of uncertainty. The WGE TKE formulation has the advantage of a ‘native’ interpretation of wind gusts as result of local appearance of TKE. The inclusion of a probabilistic WGE TKE approach in NWP models has, thus, several advantages over other methods, as it has the potential for an estimation of uncertainties of gusts at observational sites.

Probabilistic physically-based cloud screening of satellite infra-red imagery for operational sea surface temperature retrieval

We propose and demonstrate a fully probabilistic (Bayesian) approach to the detection of cloudy pixels in thermal infrared (TIR) imagery observed from satellite over oceans. Using this approach, we show how to exploit the prior information and the fast forward modelling capability that are typically available in the operational context to obtain improved cloud detection. The probability of clear sky for each pixel is estimated by applying Bayes' theorem, and we describe how to apply Bayes' theorem to this problem in general terms. Joint probability density functions (PDFs) of the observations in the TIR channels are needed; the PDFs for clear conditions are calculable from forward modelling and those for cloudy conditions have been obtained empirically. Using analysis fields from numerical weather prediction as prior information, we apply the approach to imagery representative of imagers on polar-orbiting platforms. In comparison with the established cloud-screening scheme, the new technique decreases both the rate of failure to detect cloud contamination and the false-alarm rate by one quarter. The rate of occurrence of cloud-screening-related errors of >1 K in area-averaged SSTs is reduced by 83%. Copyright © 2005 Royal Meteorological Society.

Forecasters priorities for improving probabilistic flood forecasts.

Hydrological ensemble prediction systems (HEPS) have in recent years been increasingly used for the operational forecasting of floods by European hydrometeorological agencies. The most obvious advantage of HEPS is that more of the uncertainty in the modelling system can be assessed. In addition, ensemble prediction systems generally have better skill than deterministic systems both in the terms of the mean forecast performance and the potential forecasting of extreme events. Research efforts have so far mostly been devoted to the improvement of the physical and technical aspects of the model systems, such as increased resolution in time and space and better description of physical processes. Developments like these are certainly needed; however, in this paper we argue that there are other areas of HEPS that need urgent attention. This was also the result from a group exercise and a survey conducted to operational forecasters within the European Flood Awareness System (EFAS) to identify the top priorities of improvement regarding their own system. They turned out to span a range of areas, the most popular being to include verification of an assessment of past forecast performance, a multi-model approach for hydrological modelling, to increase the forecast skill on the medium range (>3 days) and more focus on education and training on the interpretation of forecasts. In light of limited resources, we suggest a simple model to classify the identified priorities in terms of their cost and complexity to decide in which order to tackle them. This model is then used to create an action plan of short-, medium- and long-term research priorities with the ultimate goal of an optimal improvement of EFAS in particular and to spur the development of operational HEPS in general.

Communicating probabilistic information from climate model ensembles—lessons from numerical weather prediction

Climate model ensembles are widely heralded for their potential to quantify uncertainties and generate probabilistic climate projections. However, such technical improvements to modeling science will do little to deliver on their ultimate promise of improving climate policymaking and adaptation unless the insights they generate can be effectively communicated to decision makers. While some of these communicative challenges are unique to climate ensembles, others are common to hydrometeorological modeling more generally, and to the tensions arising between the imperatives for saliency, robustness, and richness in risk communication. The paper reviews emerging approaches to visualizing and communicating climate ensembles and compares them to the more established and thoroughly evaluated communication methods used in the numerical weather prediction domains of day-to-day weather forecasting (in particular probabilities of precipitation), hurricane and flood warning, and seasonal forecasting. This comparative analysis informs recommendations on best practice for climate modelers, as well as prompting some further thoughts on key research challenges to improve the future communication of climate change uncertainties.

Time to decide? Simplicity and congruity in comparative judgment

What is the relationship between magnitude judgments relying on directly available characteristics versus probabilistic cues? Question frame was manipulated in a comparative judgment task previously assumed to involve inference across a probabilistic mental model (e.g., “which city is largest” – the “larger” question – versus “which city is smallest” – the “smaller” question). Participants identified either the largest or smallest city (Experiments 1a, 2) or the richest or poorest person (Experiment 1b) in a three-alternative forced choice (3-AFC) task (Experiment 1) or 2-AFC task (Experiment 2). Response times revealed an interaction between question frame and the number of options recognized. When asked the smaller question, response times were shorter when none of the options were recognized. The opposite pattern was found when asked the larger question: response time was shorter when all options were recognized. These task-stimuli congruity results in judgment under uncertainty are consistent with, and predicted by, theories of magnitude comparison which make use of deductive inferences from declarative knowledge.

Improving flood forecasts for better flood preparedness in the UK (and beyond)

This winter (2013/14) coastal storms and an unprecedented amount of rainfall led to significant and widespread flooding across the southern UK. Despite much criticism and blame surrounding the flood events, the Flood Forecasting Centre, a recent development in national-level flood forecasting capabilities for the government and emergency response communities, has received considerable praise. Here we consider how scientific developments and organisational change have led to improvements in the forecasting and flood preparedness seen in this winter's flooding. Although such improvements are admirable, there are many technical and communication challenges that remain for probabilistic flood forecasts to achieve their full potential.

Global forecasting of thermal health hazards: the skill of probabilistic predictions of the Universal Thermal Climate Index (UTCI)

Although over a hundred thermal indices can be used for assessing thermal health hazards, many ignore the human heat budget, physiology and clothing. The Universal Thermal Climate Index (UTCI) addresses these shortcomings by using an advanced thermo-physiological model. This paper assesses the potential of using the UTCI for forecasting thermal health hazards. Traditionally, such hazard forecasting has had two further limitations: it has been narrowly focused on a particular region or nation and has relied on the use of single ‘deterministic’ forecasts. Here, the UTCI is computed on a global scale,which is essential for international health-hazard warnings and disaster preparedness, and it is provided as a probabilistic forecast. It is shown that probabilistic UTCI forecasts are superior in skill to deterministic forecasts and that despite global variations, the UTCI forecast is skilful for lead times up to 10 days. The paper also demonstrates the utility of probabilistic UTCI forecasts on the example of the 2010 heat wave in Russia.

Can dynamically downscaled windstorm footprints be improved by observations through a probabilistic approach?

Windstorms are a main feature of the European climate and exert strong socioeconomic impacts. Large effort has been made in developing and enhancing models to simulate the intensification of windstorms, resulting footprints, and associated impacts. Simulated wind or gust speeds usually differ from observations, as regional climate models have biases and cannot capture all local effects. An approach to adjust regional climate model (RCM) simulations of wind and wind gust toward observations is introduced. For this purpose, 100 windstorms are selected and observations of 173 (111) test sites of the German Weather Service are considered for wind (gust) speed. Theoretical Weibull distributions are fitted to observed and simulated wind and gust speeds, and the distribution parameters of the observations are interpolated onto the RCM computational grid. A probability mapping approach is applied to relate the distributions and to correct the modeled footprints. The results are not only achieved for single test sites but for an area-wide regular grid. The approach is validated using root-mean-square errors on event and site basis, documenting that the method is generally able to adjust the RCM output toward observations. For gust speeds, an improvement on 88 of 100 events and at about 64% of the test sites is reached. For wind, 99 of 100 improved events and ~84% improved sites can be obtained. This gives confidence on the potential of the introduced approach for many applications, in particular those considering wind data.

Probabilistic parameterisation of the surface mass balance--elevation feedback in regional climate model simulations of the Greenland ice sheet

We present a new parameterisation that relates surface mass balance (SMB: the sum of surface accumulation and surface ablation) to changes in surface elevation of the Greenland ice sheet (GrIS) for the MAR (Modèle Atmosphérique Régional: Fettweis, 2007) regional climate model. The motivation is to dynamically adjust SMB as the GrIS evolves, allowing us to force ice sheet models with SMB simulated by MAR while incorporating the SMB–elevation feedback, without the substantial technical challenges of coupling ice sheet and climate models. This also allows us to assess the effect of elevation feedback uncertainty on the GrIS contribution to sea level, using multiple global climate and ice sheet models, without the need for additional, expensive MAR simulations. We estimate this relationship separately below and above the equilibrium line altitude (ELA, separating negative and positive SMB) and for regions north and south of 77� N, from a set of MAR simulations in which we alter the ice sheet surface elevation. These give four “SMB lapse rates”, gradients that relate SMB changes to elevation changes. We assess uncertainties within a Bayesian framework, estimating probability distributions for each gradient from which we present best estimates and credibility intervals (CI) that bound 95% of the probability. Below the ELA our gradient estimates are mostly positive, because SMB usually increases with elevation: 0.56 (95% CI: −0.22 to 1.33) kgm−3 a−1 for the north, and 1.91 (1.03 to 2.61) kgm−3 a−1 for the south. Above the ELA, the gradients are much smaller in magnitude: 0.09 (−0.03 to 0.23) kgm−3 a−1 in the north, and 0.07 (−0.07 to 0.59) kgm−3 a−1 in the south, because SMB can either increase or decrease in response to increased elevation. Our statistically founded approach allows us to make probabilistic assessments for the effect of elevation feedback uncertainty on sea level projections (Edwards et al., 2014).

Attribution of extreme weather events in Africa: A preliminary exploration of the science and policy implications

At the most recent session of the Conference of the Parties (COP19) in Warsaw (November 2013) the Warsaw international mechanism for loss and damage associated with climate change impacts was established under the United Nations Framework Convention on Climate Change (UNFCCC). The mechanism aims at promoting the implementation of approaches to address loss and damage associated with the adverse effects of climate change. Specifically, it aims to enhance understanding of risk management approaches to address loss and damage. Understanding risks associated with impacts due to highly predictable (slow onset) events like sea-level rise is relatively straightforward whereas assessing the effects of climate change on extreme weather events and their impacts is much more difficult. However, extreme weather events are a significant cause of loss of life and livelihoods, particularly in vulnerable countries and communities in Africa. The emerging science of probabilistic event attribution is relevant as it provides scientific evidence on the contribution of anthropogenic climate change to changes in risk of extreme events. It thus provides the opportunity to explore scientifically-backed assessments of the human influence on such events. However, different ways of framing attribution questions can lead to very different assessments of change in risk. Here we explain the methods of, and implications of different approaches to attributing extreme weather events with a focus on Africa. Crucially, it demonstrates that defining the most appropriate attribution question to ask is not a science decision but needs to be made in dialogue with those stakeholders who will use the answers.