scholarly journals A hazard model of subfreezing temperatures in the United Kingdom using vine copulas

2017 ◽  
Author(s):  
Symeon Koumoutsaris
Keyword(s):  
United Kingdom ◽  
Cold Weather ◽  
Local Climate ◽  
Considerable Uncertainty ◽  
The United Kingdom ◽  
Novel Approach ◽  
Weather Events ◽  
Model Complex ◽  
Extreme Cold ◽  
Vine Copula

Abstract. Extreme cold weather events, such as the winters of 1962/63, the third coldest winter ever recorded, or more recently the winter of 2010/11, have significant consequences for the society and economy. This paper assesses the probability of such extreme cold weather across the United Kingdom. For that, a statistical model is developed in order to model the extremes of the Air Freezing Index (AFI), which is a common measure of magnitude and duration of freezing temperatures. A novel approach in the modelling of the spatial dependence of the hazard has been followed which takes advantage of the vine copula methodology. The method allows to model complex dependencies especially between the tails of the AFI distributions which is important to assess reliably the extreme behaviour of such events. The model suggests that the extreme winter 1962/63 has a return period of approximately once every 89 years, but the relative short record length together with the unclear effects of anthropogenic forcing on the local climate add considerable uncertainty to this estimate. This model is used as part of a probabilistic catastrophe model for insured losses caused by the bursting of pipes.

scholarly journals A hazard model of sub-freezing temperatures in the United Kingdom using vine copulas

2019 ◽  
Vol 19 (3) ◽  
pp. 489-506
Author(s):  
Symeon Koumoutsaris
Keyword(s):  
Climate Change ◽  
United Kingdom ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Cold Weather ◽  
The North ◽  
The United Kingdom ◽  
Extreme Cold ◽  
The North Atlantic ◽  
The North Atlantic Oscillation

Abstract. Extreme cold weather events, such as the winter of 1962/63, the third coldest winter ever recorded in the Central England Temperature record, or more recently the winter of 2010/11, have significant consequences for the society and economy. This paper assesses the probability of such extreme cold weather across the United Kingdom (UK), as part of a probabilistic catastrophe model for insured losses caused by the bursting of pipes. A statistical model is developed in order to model the extremes of the Air Freezing Index (AFI), which is a common measure of the magnitude and duration of freezing temperatures. A novel approach in the modelling of the spatial dependence of the hazard has been followed which takes advantage of the vine copula methodology. The method allows complex dependencies to be modelled, especially between the tails of the AFI distributions, which is important to assess the extreme behaviour of such events. The influence of the North Atlantic Oscillation and of anthropogenic climate change on the frequency of UK cold winters has also been taken into account. According to the model, the occurrence of extreme cold events, such as the 1962/63 winter, has decreased approximately 2 times during the course of the 20th century as a result of anthropogenic climate change. Furthermore, the model predicts that such an event is expected to become more uncommon, about 2 times less frequent, by the year 2030. Extreme cold spells in the UK have been found to be heavily modulated by the North Atlantic Oscillation (NAO) as well. A cold event is estimated to be ≈3–4 times more likely to occur during its negative phase than its positive phase. However, considerable uncertainty exists in these results, owing mainly to the short record length and the large interannual variability of the AFI.

scholarly journals Clustering of equine grass sickness cases in the United Kingdom: a study considering the effect of position-dependent reporting on the space–time K-function

2004 ◽  
Vol 133 (2) ◽  
pp. 343-348 ◽  
Author(s):  
N. P. FRENCH ◽  
H. E. McCARTHY ◽  
P. J. DIGGLE ◽  
C. J. PROUDMAN
Keyword(s):  
United Kingdom ◽  
Management Practices ◽  
Space Time ◽  
Pasture Management ◽  
Local Climate ◽  
Temporal Properties ◽  
Time Interaction ◽  
The United Kingdom ◽  
Grass Sickness ◽  
Type C

Equine grass sickness (EGS) is a largely fatal, pasture-associated dysautonomia. Although the aetiology of this disease is unknown, there is increasing evidence that Clostridium botulinum type C plays an important role in this condition. The disease is widespread in the United Kingdom, with the highest incidence believed to occur in Scotland. EGS also shows strong seasonal variation (most cases are reported between April and July). Data from histologically confirmed cases of EGS from England and Wales in 1999 and 2000 were collected from UK veterinary diagnostic centres. The data did not represent a complete census of cases, and the proportion of all cases reported to the centres would have varied in space and, independently, in time. We consider the variable reporting of this condition and the appropriateness of the space–time K-function when exploring the spatial-temporal properties of a ‘thinned’ point process. We conclude that such position-dependent under-reporting of EGS does not invalidate the Monte Carlo test for space–time interaction, and find strong evidence for space–time clustering of EGS cases (P<0·001). This may be attributed to contagious or other spatially and temporally localized processes such as local climate and/or pasture management practices.

scholarly journals Residential Natural Gas Demand Response Potential during Extreme Cold Events in Electricity-Gas Coupled Energy Systems

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5192
Author(s):  
Andrew Speake ◽  
Paul Donohoo-Vallett ◽  
Eric Wilson ◽  
Emily Chen ◽  
Craig Christensen
Keyword(s):  
United States ◽  
Natural Gas ◽  
Demand Response ◽  
The United States ◽  
Cold Weather ◽  
Residential Heating ◽  
Weather Events ◽  
Extreme Cold ◽  
Natural Gas Demand ◽  
The Impact

In regions where natural gas is used for both power generation and heating buildings, extreme cold weather events can place the electrical system under enormous stress and challenge the ability to meet residential heating and electric demands. Residential demand response has long been used in the power sector to curtail summer electric load, but these types of programs in general have not seen adoption in the natural gas sector during winter months. Natural gas demand response (NG-DR) has garnered interest given recent extreme cold weather events in the United States; however, the magnitude of savings and potential impacts—to occupants and energy markets—are not well understood. We present a case-study analysis of the technical potential for residential natural gas demand response in the northeast United States that utilizes diverse whole-building energy simulations and high-performance computing. Our results show that NG-DR applied to residential heating systems during extreme cold-weather conditions could reduce natural gas demand by 1–29% based on conservative and aggressive strategies, respectively. This indicates a potential to improve the resilience of gas and electric systems during stressful events, which we examine by estimating the impact on energy costs and electricity generation from natural gas. We also explore relationships between hourly indoor temperatures, demand response, and building envelope efficiency.

scholarly journals Measuring the Significance of Policy Outputs with Positive Unlabeled Learning

2020 ◽  
pp. 1-8
Author(s):  
RADOSLAW ZUBEK ◽  
ABHISHEK DASGUPTA ◽  
DAVID DOYLE
Keyword(s):  
United Kingdom ◽  
Construct Validity ◽  
Government Regulations ◽  
New Approach ◽  
The United Kingdom ◽  
Novel Approach ◽  
Pu Learning ◽  
Finite Set ◽  
Small Set

Identifying important policy outputs has long been of interest to political scientists. In this work, we propose a novel approach to the classification of policies. Instead of obtaining and aggregating expert evaluations of significance for a finite set of policy outputs, we use experts to identify a small set of significant outputs and then employ positive unlabeled (PU) learning to search for other similar examples in a large unlabeled set. We further propose to automate the first step by harvesting “seed” sets of significant outputs from web data. We offer an application of the new approach by classifying over 9,000 government regulations in the United Kingdom. The obtained estimates are successfully validated against human experts, by forecasting web citations, and with a construct validity test.

scholarly journals Future long-term care expenditure in Germany, Spain, Italy and the United Kingdom

2006 ◽  
Vol 26 (2) ◽  
pp. 285-302 ◽  
Author(s):  
ADELINA COMAS-HERRERA ◽  
RAPHAEL WITTENBERG ◽  
JOAN COSTA-FONT ◽  
CRISTIANO GORI ◽  
ALESSANDRA DI MAIO ◽  
...  
Keyword(s):  
United Kingdom ◽  
Older People ◽  
Informal Care ◽  
Long Term Care ◽  
Base Case ◽  
Term Care ◽  
Considerable Uncertainty ◽  
The United Kingdom ◽  
The Future

This paper reports findings from a European Commission funded study of future long-term care expenditure in Germany, Italy, Spain and the United Kingdom, and presents projections of future long-term care expenditure in the four countries under a number of assumptions about the future. Macro-simulation (or cell-based) models were used to make comparable projections based on a set of common assumptions. A central base-case served as a point of comparison by which to explore the sensitivity of the models to alternative scenarios for the key determinants. The sensitivity of the models to variant assumptions about the future numbers of older people, the prevalence of functional dependency and informal care, patterns of long-term provision, and macroeconomic conditions are examined. It was found that, under the base-case, the proportion of gross domestic product spent on long-term care is projected to more than double between 2000 and 2050 in each country. The projected future demand for long-term care services for older people is sensitive to assumptions about the future number of older people, the prevalence of dependency and the availability of informal care, and projected expenditure is sensitive to assumptions about rises in the real unit-costs of services and the structure of the models. It is important, for planning purposes, to recognise the considerable uncertainty about future levels of long-term care expenditure.

scholarly journals Brexit: The Economics of International Disintegration

2017 ◽  
Vol 31 (4) ◽  
pp. 163-184 ◽  
Author(s):  
Thomas Sampson
Keyword(s):  
European Union ◽  
United Kingdom ◽  
Economic Effects ◽  
Economic Consequences ◽  
The European Union ◽  
Global Integration ◽  
Considerable Uncertainty ◽  
Empirical Estimates ◽  
The United Kingdom ◽  
The Eu

On June 23, 2016, the United Kingdom held a referendum on its membership in the European Union. Although most of Britain’s establishment backed remaining in the EU, 52 percent of voters disagreed and handed a surprise victory to the “leave” campaign. Brexit, as the act of Britain exiting the EU has become known, is likely to occur in early 2019. This article discusses the economic consequences of Brexit and the lessons of Brexit for the future of European and global integration. I start by describing the options for post-Brexit relations between the United Kingdom and the European Union and then review studies of the likely economic effects of Brexit. The main conclusion of this literature is that Brexit will make the United Kingdom poorer than it would otherwise have been because it will lead to new barriers to trade and migration between the UK and the European Union. There is considerable uncertainty over how large the costs of Brexit will be, with plausible estimates ranging between 1 and 10 percent of UK per capita income. The costs will be lower if Britain stays in the European Single Market following Brexit. Empirical estimates that incorporate the effects of trade barriers on foreign direct investment and productivity find costs 2–3 times larger than estimates obtained from quantitative trade models that hold technologies fixed.

Using an Emergency Department Syndromic Surveillance System to investigate the impact of extreme cold weather events

Public Health ◽  
2014 ◽  
Vol 128 (7) ◽  
pp. 628-635 ◽  
Author(s):  
H.E. Hughes ◽  
R. Morbey ◽  
T.C. Hughes ◽  
T.E. Locker ◽  
T. Shannon ◽  
...  
Keyword(s):  
Emergency Department ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Cold Weather ◽  
Syndromic Surveillance System ◽  
Weather Events ◽  
Extreme Cold ◽  
The Impact

scholarly journals CORRIGENDUM

2007 ◽  
Vol 22 (1) ◽  
pp. 216-219
Author(s):  
T. P. Legg ◽  
K. R. Mylne
Keyword(s):  
United Kingdom ◽  
Operating Characteristic ◽  
Severe Weather ◽  
Relative Operating Characteristic ◽  
The United Kingdom ◽  
Weather Events ◽  
Skill Scores ◽  
Early Warnings

Abstract In an earlier paper the authors described the use of ensemble information for the generation of early warnings of defined severe-weather events within the United Kingdom. A comprehensive verification of the system was also included in this study. However, an error was later found within the verification code for relative operating characteristic and reliability, which affects most of the results (though the Brier skill scores were not affected). The purpose of the present corrigendum is to provide amended verification results. Briefly, what was found before was that skill appeared to exhibit a maximum for these severe-weather events at 4 days ahead, but, although the results for day 4 remain good, the authors underestimated the skill at other days and so the 4-day skill maximum is no longer clear; instead, skill is useful at days 1–4, and tails off only slowly at days 5–6.

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