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To capture the dependence in the upper tail of a time series, we develop non‐negative regularly varying time series models that are constructed similarly to classical non‐extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly varying time series via a measure of pairwise extremal dependencies, the tail pairwise dependence function (TPDF). We state consistency requirements among the finite‐dimensional collections of the elements of a regularly varying time series and show that the TPDF's value does not depend on the dimension of the random vector being considered. So that our models take non‐negative values, we use transformed‐linear operations. We show existence and stationarity of these models, and develop their properties such as the model TPDFs. We fit models to hourly windspeed and daily fire weather index data, and we find that the fitted transformed‐linear models produce better estimates of upper tail quantities than a traditional ARMA model, classical linear regularly varying models, a max‐ARMA model, and a Markov model. [ABSTRACT FROM AUTHOR]

Wildfire risk is greatest during high winds after sustained periods of dry and hot conditions. This paper is a statistical extreme-event risk attribution study that aims to answer whether extreme wildfire seasons are more likely now than under past climate. This requires modeling temporal dependence at extreme levels. We propose the use of transformed-linear time series models, which are constructed similarly to traditional autoregressive–moving-average (ARMA) models while having a dependence structure that is tied to a widely used framework for extremes (regular variation). We fit the models to the extreme values of the seasonally adjusted fire weather index (FWI) time series to capture the dependence in the upper tail for past and present climate. We simulate 10 000 fire seasons from each fitted model and compare the proportion of simulated high-risk fire seasons to quantify the increase in risk. Our method suggests that the risk of experiencing an extreme wildfire season in Grand Lake, Colorado, under current climate has increased dramatically relative to the risk under the climate of the mid-twentieth century. Our method also finds some evidence of increased risk of extreme wildfire seasons in Quincy, California, but large uncertainties do not allow us to reject a null hypothesis of no change. [ABSTRACT FROM AUTHOR]

We propose a method for analyzing extremal behavior through the lens of a most efficient basis of vectors. The method is analogous to principal component analysis, but is based on methods from extreme value analysis. Specifically, rather than decomposing a covariance or correlation matrix, we obtain our basis vectors by performing an eigendecomposition of a matrix that describes pairwise extremal dependence. We apply the method to precipitation observations over the contiguous United States. We find that the time series of large coefficients associated with the leading eigenvector shows very strong evidence of a positive trend, and there is evidence that large coefficients of other eigenvectors have relationships with El Niño–Southern Oscillation. [ABSTRACT FROM AUTHOR]

Understanding dependence structure among extreme values plays an important role in risk assessment in environmental studies. In this work, we propose the χ network and the annual extremal network for exploring the extremal dependence structure of environmental processes. A χ network is constructed by connecting pairs whose estimated upper tail dependence coefficient, χ ^ , exceeds a prescribed threshold. We develop an initial χ network estimator, and we use a spatial block bootstrap to assess both the bias and variance of our estimator. We then develop a method to correct the bias of the initial estimator by incorporating the spatial structure in χ . In addition to the χ network, which assesses spatial extremal dependence over an extended period of time, we further introduce an annual extremal network to explore the year-to-year temporal variation of extremal connections. We illustrate the χ and the annual extremal networks by analyzing the hurricane season maximum precipitation at the US Gulf Coast and surrounding area. Analysis suggests there exists long distance extremal dependence for precipitation extremes in the study region and the strength of the extremal dependence may depend on some regional scale meteorological conditions, for example, sea surface temperature. [ABSTRACT FROM AUTHOR]

We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O 3 ) in three US cities for summers during 1996–2005. The purpose of this study is to evaluate the ability of a high resolution atmospheric chemistry model to reproduce observed relationships between meteorology and high or extreme O 3 . We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O 3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O 3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O 3 , and tail dependence optimization, which is used to investigate even higher O 3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O 3 . [ABSTRACT FROM AUTHOR]

Precipitation extremes are expected to increase in a warming climate, which may have serious societal impacts. This study uses two initial condition ensembles conducted with the Community Earth System Model (CESM) to investigate potential changes in extreme precipitation under two climate change scenarios over the contiguous United States. We fit non-stationary generalized extreme value (GEV) models to annual maximum daily precipitation simulated from a 30-member ensemble under the RCP8.5 scenario and a 15-member ensemble under the RCP4.5 scenario. We then compare impacts using the 1 % annual exceedance probability (AEP) level, which is the amount of daily rainfall with only a 1 % chance of being exceeded in a given year. Under RCP8.5 between 2005 and 2080, the 1 % AEP level is projected to increase by 17 % on average across the U.S., and up to 36 % for some grid cells. Compared to RCP8.5 in the year 2080, RCP4.5 is projected to reduce the 1 % AEP level by 7 % on average, with reductions as large as 18 % for some grid cells. We also investigate a pattern scaling approach in which we produce predictive GEV distributions of annual precipitation maxima under RCP4.5 given only global mean temperatures for this scenario. We compare results from this less computationally intensive method to those obtained from our GEV model fitted directly to the CESM RCP4.5 output and find that pattern scaling produces reasonable projections. [ABSTRACT FROM AUTHOR]

Information about the location and magnitudes of natural gas (NG) leaks from urban distribution pipelines is important for minimizing greenhouse gas emissions and optimizing investment in pipeline management. To enable rapid collection of such data, we developed a relatively simple method using high-precision methane analyzers in Google Street View cars. Our data indicate that this automated leak survey system can document patterns in leak location and magnitude within and among cities, even without wind data. We found that urban areas with prevalent corrosion-prone distribution lines (Boston, MA, Staten Island, NY, and Syracuse, NY), leaked approximately 25-fold more methane than cities with more modern pipeline materials (Burlington, VT, and Indianapolis, IN). Although this mobile monitoring method produces conservative estimates of leak rates and leak counts, it can still help prioritize both leak repairs and replacement of leak-prone sections of distribution lines, thus minimizing methane emissions over short and long terms. [ABSTRACT FROM AUTHOR]

Summary Between 9th and 16th September 2013, northeast Colorado received some of its most extreme rainfall on record. The event affected 6 major rivers and their tributaries and 14 counties, breaking observed records for accumulations from sub-daily through to annual total. NOAA’s rainfall atlases indicated that this event had an anticipated return period of 1000 years. We use the rainfall that led to the 2013 Colorado floods as a case study in order to explore how a large event can affect the generalized extreme value (GEV) parameter estimates often used by designers and planners. We employ daily rainfall observations, with at least 30 years of data, from stations across Colorado’s Front Range of the Rocky Mountains to develop a spatial statistical model for annual maximum daily rainfall. We produce estimates of relatively rare events such as the 1% Annual Exceedance Probability (AEP) level and of extremely rare events such as the return period associated with Boulder’s 2013 observation. To explore sensitivity, we compare estimates including and excluding data from 2013, and both using only individual station data and our model which borrows strength across multiple stations. We compute the uncertainty associated with all of our estimates, and find large uncertainties associated with extremely rare events. Our statistical model is a spatial hierarchical model and we employ a two-stage approach for inference which can be implemented by practitioners. Additionally, the spatial model allows us to interpolate spatially and estimate the GEV parameters at unobserved locations. A further development of the model makes use of an alternatively defined space in terms of elevation and a climate variable, rather than geographical space defined by longitude and latitude, which seems to better account for orographic effects. In addition to producing AEP level and return period estimates to the annual maximum data, we investigate sensitivity to the choice of block length. We find point estimates indicate the tail to be much heavier when a longer block length is used, but the uncertainty associated with this parameter is such that one cannot say the difference is significant. To describe the spatial extent of severe storms, we also investigate the amount of data dependence between station locations. We find evidence in the record for storms with large spatial extent, although an extremal dependence parameter estimate indicates that this dependence is relatively weak. [ABSTRACT FROM AUTHOR]

A fundamental deficiency of classical multivariate extreme value theory is the inability to distinguish between asymptotic independence and exact independence. In this work, we examine multivariate threshold modelling in the framework of regular variation on cones. Tail dependence is described by a limiting measure, which in some cases is degenerate on joint tail regions despite strong subasymptotic dependence in such regions. Hidden regular variation, a higher-order tail decay on these regions, offers a refinement of the classical theory. We develop a representation of random vectors possessing hidden regular variation as the sum of independent regular varying components. The representation is shown to be asymptotically valid via a multivariate tail equivalence result. We develop a likelihood-based estimation procedure from this representation via a Monte Carlo expectation-maximization algorithm which has been modified for tail estimation. The method is demonstrated on simulated data and applied to air pollution measurements. [ABSTRACT FROM AUTHOR]

Several disease forecast models have been developed to guide treatment of the sooty blotch and flyspeck (SBFS) disease complex of apple. Generally, these empirical models are based on the accumulation of hours of leaf wetness (leaf wetness duration [LWD]) from a biofix at or near the phenological growth stage petal fall, when apple flower petals senesce and drop. The models recommend timing of the initial fungicide application targeting SBFS. However, there are significant differences among SBFS forecast models in terms of biofix and the length of LWD thresholds. A comparison of models using a single input data set generated recommendations for the first SBFS fungicide application that differed by up to 5 weeks. In an attempt to improve consistency among models, potential sources for differences were examined. Leaf wetness (LW) is a particularly variable parameter among models, depending on whether on-site or remote weather data were used, the types of sensors and their placement for on-site monitors, and the models used to estimate LW remotely. When SBFS models are applied in the field, recommended treatment thresholds do not always match the method of data acquisition, leading to potential failures. Horticultural factors, such as tree size, canopy density, and cultivar, and orchard site factors such as the distance to potential inoculum sources can impact risk of SBFS and should also be considered in forecast models. The number of fungal species identified as contributors to the SBFS disease complex has expanded tremendously in recent years. A lack of understanding of key epidemiological factors for different fungi in the complex, and which fungi represent the most challenging management problems, are obvious issues in the development of improved SBFS models. If SBFS forecast models are to be adopted, researchers will need to address these issues. [ABSTRACT FROM AUTHOR]

C ooley D.R., M ullins R.F., B radley P.M. and W ilce R.T. 2011. Culturing the upper littoral zone marine alga induces pathogenic interaction with the fungus . Phycologia 50: 541-547. DOI: 10.2216/10-84.1 The green marine alga (Willie 1901) occurs on hard substratum in the upper littoral in the North Atlantic Ocean. In recent field and laboratory studies, grew normally in situ but declined in vitro. Microscopic examination showed the alga inoculum to be associated with a filamentous fungus, where the fungus grew in close association with the alga. samples collected from Ipswich, MA during a two-year period were consistently associated with the fungus. In cultures with the fungus present, algal thalli declined and became necrotic, while the fungus produced hyphae and conidia. The fungus failed to grow in sterile algal medium in the absence of but could be isolated and grown on standard mycological culture media. Light and scanning electron microscopy showed fungal hyphae infiltrated intercellular areas of the algal filaments but did not penetrate algal cell walls. Pathogenicity is apparently a result of the in vitro environment, and it is hypothesized that the fungus produces one or more secondary metabolites, which accumulate in culture. These metabolites produced by the fungus eventually killed algal cells. Fungal ribosomal DNA sequences were compared to sp. in GenBank, indicating that the fungus is (Fresenius) de Vries. Widely distributed, has been shown to be a facultative parasite, endophyte and saprophyte on terrestrial plants and some algae. The fungus has not been described in association with an ulvophyte, a green marine alga. [ABSTRACT FROM AUTHOR]

Abstract: We present a new parametric model for the angular measure of a multivariate extreme value distribution. Unlike many parametric models that are limited to the bivariate case, the flexible model can describe the extremes of random vectors of dimension greater than two. The novel construction method relies on a geometric interpretation of the requirements of a valid angular measure. An advantage of this model is that its parameters directly affect the level of dependence between each pair of components of the random vector, and as such the parameters of the model are more interpretable than those of earlier parametric models for multivariate extremes. The model is applied to air quality data and simulated spatial data. [Copyright &y& Elsevier]

The goal of this work is to characterize the extreme precipitation simulated by a regional climate model (RCM) over its spatial domain. For this purpose, we develop a Bayesian hierarchical model. Since extreme value analyses typically only use data considered to be extreme, the hierarchical approach is particularly useful as it sensibly pools the limited data from neighboring locations. We simultaneously model the data from both a control and future run of the RCM which allows for easy inference about projected change. Additionally, this hierarchical model is the first to spatially model the shape parameter which characterizes the nature of the distribution’s tail. Our hierarchical model shows that for the winter season, the RCM indicates a general increase in 100-year precipitation return levels for most of the study region. For the summer season, the RCM surprisingly indicates a significant decrease in the 100-year precipitation return level. [ABSTRACT FROM AUTHOR]

In his paper in Climate Monitor, TML Wigley uses basic probability arguments to illustrate how a slowly changing climate could potentially affect the frequency of extreme events. In the time since the paper appeared, there has been increased interest in assessing how weather extremes may be altered by climate change. Much of the work has been conducted using extreme value analysis, which is the branch of statistics developed specifically to characterize extreme events. This commentary discusses the advantages of an EVA approach and reviews some EVA techniques that have been used to describe climate change’s potential impact on extreme phenomena. Additionally, this commentary illustrates basic EVA techniques in an analysis of temperatures for central England. In parallel to Wigley’s analysis, a time-varying EVA analysis is compared to a stationary one, and furthermore, the trend from the EVA analysis is compared to the trend in means. [ABSTRACT FROM AUTHOR]

Quantification of precipitation extremes is important for flood planning purposes, and a common measure of extreme events is the r-year return level. We present a method for producing maps of precipitation return levels and uncertainty measures and apply it to a region in Colorado. Separate hierarchical models are constructed for the intensity and the frequency of extreme precipitation events. For intensity, we model daily precipitation above a high threshold at 56 weather stations with the generalized Pareto distribution. For frequency, we model the number of exceedances at the stations as binomial random variables. Both models assume that the regional extreme precipitation is driven by a latent spatial process characterized by geographical and climatological covariates. Effects not fully described by the covariates are captured by spatial structure in the hierarchies. Spatial methods were improved by working in a space with climatological coordinates. Inference is provided by a Markov chain Monte Carlo algorithm and spatial interpolation method, which provide a natural method for estimating uncertainty. [ABSTRACT FROM AUTHOR]

Flyspeck disease caused by the Ascomycete fungus Schizothyrium pomi is a major component of a summer disease complex that has become an increasing problem on apple in the northeastern United States. This study describes the development of thyriothecia of S. pomi on one of its major reservoir hosts, wild blackberry, and suggests that temperature drives thyriothecium maturation. An empirical model relating thyriothecium maturity to degree-days from apple leaf bud-break in Massachusetts had a high coefficient of determination, R² = 0.90. Ninety percent of thyriothecia matured between 540 and 1,625 degree-days from the budbreak biofix. S. pomi produced one generation of asci per year on blackberry. This discrete maturation period occurred well before signs of disease developed on apple fruit and before production of conidia. Considering that fungicide applications for scab management in commercial apple orchards also will protect fruit from flyspeck during early fruit development, it is unlikely that ascospores produced on reservoir hosts directly infect fruit. Instead, they probably initiate infections in reservoir hosts adjacent to commercial orchards, which produce conidia that are the inoculum which infects apple fruit in commercial production. [ABSTRACT FROM AUTHOR]

The experimental validation of a model-based, thermal therapy control system which automatically and simultaneously achieves the specified efficacy and safety objectives of the treatment is reported. MR-thermometry measurements are used in real-time to control the power of a stationary, focused ultrasound transducer in order to achieve the desired treatment outcome in minimum time without violating the imposed safety constraints. Treatment efficacy is quantified in terms of the thermal dose delivered to the target. Normal tissue safety is ensured by automatically maintaining normal tissue temperature below the imposed limit in the user-specified locations. To reflect hardware limitations, constraints on the maximum applied power are also imposed. At the pretreatment stage, MR imaging and thermometry are used to localize the treatment target and identify thermal and actuation models. The results of phantom and canine experiments demonstrate that spatially-distributed, real-time MR temperature measurements enhance one's ability to robustly achieve the desired treatment outcome in minimum time without violating safety constraints. Post-treatment evaluation of the outcome using T2-weighted images of canine muscle showed good spatial correlation between the sonicated area and thermally damaged tissue. [ABSTRACT FROM AUTHOR]

Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of "tail dependence" arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data. [ABSTRACT FROM AUTHOR]

Future energy systems may rely on natural gas as a low-cost fuel to support variable renewable power. However, leaking natural gas causes climate damage because methane (CH4) has a high global warming potential. In this study, we use extreme-value theory to explore the distribution of natural gas leak sizes. By analyzing ~ 15 000 measurements from 18 prior studies, we show that all available natural gas leakage data sets are statistically heavy-tailed, and that gas leaks are more extremely distributed than other natural and social phenomena. A unifying result is that the largest 5% of leaks typically contribute over 50% of the total leakage volume. While prior studies used log-normal model distributions, we show that log-normal functions poorly represent tail behavior. Our results suggest that published uncertainty ranges of CH4 emissions are too narrow, and that larger sample sizes are required in future studies to achieve targeted confidence intervals. Additionally, we find that cross-study aggregation of data sets to increase sample size is not recommended due to apparent deviation between sampled populations. Understanding the nature of leak distributions can improve emission estimates, better illustrate their uncertainty, allow prioritization of source categories, and improve sampling design. Also, these data can be used for more effective design of leak detection technologies. [ABSTRACT FROM AUTHOR]

Demethylation inhibitors (DMIs) are a class of single-site fungicides with high levels of protective and curative efficacy against Venturia inaequalis, the causal agent of apple scab. To determine the prevalence of resistance to the DMI fungicide myclobutanil, 3,987 single-lesion conidial V. inaequalis isolates from 141 commercial, research, and baseline orchard populations were examined throughout New England, the mid-Atlantic, and the Midwest from 2004 to 2013. Of these orchard populations, 63% had practical resistance, 13% had reduced sensitivity, and 24% were sensitive to myclobutanil. A sensitivity baseline for the recently introduced DMI fungicide difenoconazole was established to make comparisons with myclobutanil sensitivity in orchard populations. The mean effective concentration of difenoconazole at which mycelial growth was inhibited by 50% (EC50) was determined to be 0.002 µg ml-1 for 44 baseline isolates of V. inaequalis. From 2010 to 2013, 1,012 isolates of V. inaequalis from 37 of the 141 orchard populations above were screened for sensitivity to difenoconazole. In all, 1 orchard population had reduced sensitivity to difenoconazole, while the remaining 36 orchard populations were sensitive to the fungicide. In field experiments, difenoconazole demonstrated high levels of apple scab control on mature apple fruit, despite the fact that the population of V. inaequalis had practical resistance to difenoconazole. Although our results indicate widespread resistance to myclobutanil but not difenoconazole, due to the propensity for cross-sensitivity among DMI fungicides, growers with myclobutanil resistance should be cautious when using difenoconazole for disease management. [ABSTRACT FROM AUTHOR]

Quinone-outside-inhibitor (Qol) fungicides are a safe and effective means of managing apple scab caused by Venturia inaequalis. To determine the prevalence of both quantitative (partial) and qualitative (complete) Qol resistance in V. inaequalis in the northeastern United States, we sampled single-lesion conidial isolates (n = 4,481 ) from 120 commercial and research orchards from 2004 to 2011 with a range of exposure to Qol fungicides from none to several applications a year. In all, 67% of these orchard populations of V. inaequalis were sensitive to Qol fungicides, 28% exhibited Qol practical resistance, and 5% were not sensitive Qol fungicides but had not become practically resistant. Isolates with qualitative Qol resistance, conferred by the G143A cytochrome b gene mutation, were found in 13 of the 34 Qol-resistant orchard populations. To evaluate the stability of the G143A mutation, 27 isolates were selected from different orchard populations to represent the scope of regional populations. These isolates were subcultured continuously in the presence or absence of the Qol fungicide trifloxystrobin. All isolates that initially possessed qualitative resistance maintained the resistant genotype (Gl43A) for six transfers over 6 months in both the absence and presence of trifloxystrobin. Given the observed Qol resistance in orchard populations of V. inaequalis and the stability of the G143A mutation in individual isolates, apple scab management paradigms must encompass strategies to limit selection of Qol resistance in the sensitive orchard populations remaining in the region. [ABSTRACT FROM AUTHOR]

Heat waves take a major toll on human populations, with negative impacts on the economy, agriculture, and human health. As a result, there is great interest in studying the changes over time in the probability and magnitude of heat waves. In this paper we propose a hierarchical Bayesian model for serially-dependent extreme temperatures. We assume the marginal temperature distribution follows the generalized Pareto distribution (GPD) above a location-specific threshold, and capture dependence between subsequent days using a transformed max-stable process. Our model allows both the parameters in the marginal GPD and the temporal dependence function to change over time. This allows Bayesian inference on the change in likelihood of a heat wave. We apply this methodology to daily high temperatures in nine cities in the western US for 1979-2010. Our analysis reveals increases in the probability of a heat wave in several US cities. This article has supplementary material online. [ABSTRACT FROM AUTHOR]