In recent years, ensemble weather forecasting has become a routine at all major weather prediction centers. These forecasts are obtained from multiple runs of numerical weather prediction models with different initial conditions or model parametrizations. However, ensemble forecasts can often be underdispersive and also biased, so some kind of postprocessing is needed to account for these deficiencies

Corona virus disease‐19 (COVID‐19) has substantially reduced human activities and the associated anthropogenic emissions. This study quantifies the effects of COVID‐19 control measures on six major air pollutants over 68 cities in North China by a Difference in Relative‐Difference method that allows estimation of the COVID‐19 effects while taking account of the general annual air quality trends, temporal

Benchmark analysis is a general risk estimation strategy for identifying the benchmark dose (BMD) past which the risk of exhibiting an adverse environmental response exceeds a fixed, target value of benchmark response. Estimation of BMD and of its lower confidence limit (BMDL) is well understood for the case of an adverse response to a single stimulus. In many environmental settings, however, one or

Modeling the extremal dependence structure of spatial data is considerably easier if that structure is stationary. However, for data observed over large or complicated domains, nonstationarity will often prevail. Current methods for modeling nonstationarity in extremal dependence rely on models that are either computationally difficult to fit or require prior knowledge of covariates. Sampson and Guttorp

Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of half‐spectral covariance functions to the case of nonstationarity in space and time and demonstrate that this method provides a natural and tractable way to incorporate complex behaviors into a covariance model. Further, we

Joint modeling of spatially oriented dependent variables is commonplace in the environmental sciences, where scientists seek to estimate the relationships among a set of environmental outcomes accounting for dependence among these outcomes and the spatial dependence for each outcome. Such modeling is now sought for massive data sets with variables measured at a very large number of locations. Bayesian

The classical linear model of coregionalization and its simpler counterpart known as the proportional covariance model, or intrinsic correlation model, have become standard tools in many areas of application for the analysis of multivariate spatial data. Despite the merits of this model, it guarantees optimal predictions only in the case of Gaussian data and can lead to erroneous conclusions in all

The root density of plants with depth follows exponential or the Lindley distribution in the presence of outliers generated from a uniform distribution. In this article, we estimate the parameters of the Lindley distribution in the presence of outliers generated from a uniform distribution based on the moment, maximum likelihood, least squares, weighted least squares, percentile, Cramer–von‐Mises,

Recently, due to accelerations in urban and industrial development, the health impact of air pollution has become a topic of key concern. Of the various forms of air pollution, fine atmospheric particulate matter (PM2.5; particles less than 2.5 micrometers in diameter) appears to pose the greatest risk to human health. While even moderate levels of PM2.5 can be detrimental to health, spikes in PM2

The extremal spatial dependence of significant wave height in the North East Atlantic is explored using Joint Altimetry Satellite Oceanography Network satellite altimeter observations for the period 2002–2018, and a spatial conditional extremes model motivated by the work of Heffernan and Tawn. The analysis involves (a) registering individual satellite passes onto a template transect, (b) marginal

Analyses of environmental phenomena often are concerned with understanding unlikely events such as floods, heatwaves, droughts, or high concentrations of pollutants. Yet the majority of the causal inference literature has focused on modeling means, rather than (possibly high) quantiles. We define a general estimator of the population quantile treatment (or exposure) effects (QTE)—the weighted QTE (WQTE)—of

This article compares the modeling of nonstationary extreme events using parametric models with local parametric and semiparametric approaches also motivated by extreme value theory. Specifically, three estimators are compared based on (a) (local) semiparametric moment estimation, (b) (local) maximum likelihood estimation, and (c) spline‐based maximum likelihood estimation. Inference is performed in

Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as land and sea surface temperature, in a changing climate. Typically, annual harmonics are used to characterize the variation in the seasonal temperature cycle. However, an often overlooked feature of the climate seasonal cycle is the semi‐annual harmonic, which can account for a significant

Two‐phase polynomial regression models (Robison, 1964; Fuller, 1969; Gallant and Fuller, 1973; Zhan et al., 1996) are widely used in ecology, public health, and other applied fields to model nonlinear relationships. These models are characterized by the presence of threshold parameters, across which the mean functions are allowed to change. That the threshold is a parameter of the model to be estimated

This paper proposes an alternative method to deal with spatiotemporal data with censored and missing responses using the SAEM algorithm. This algorithm is a stochastic approximation of the widely used EM algorithm and is an important tool for models in which the E‐step does not have an analytic form. Besides the algorithm developed to estimate the model parameters from a likelihood‐based perspective

We develop new flexible univariate models for light‐tailed and heavy‐tailed data, which extend a hierarchical representation of the generalized Pareto (GP) limit for threshold exceedances. These models can accommodate departure from asymptotic threshold stability in finite samples while keeping the asymptotic GP distribution as a special (or boundary) case and can capture the tails and the bulk jointly

We provide a detailed discussion on the analysis presented by Tagle and co‐authors, who suggested an approach to improve earlier models for handling non‐Gaussianity in spatial wind field speed data by simplifying the model formulation to better accommodate large data sets. Our discussion focuses on the energy and socio‐economic context of wind potential assessment in Saudi Arabia – an oil‐rich country

This is the rejoinder of the discussion article: env‐19‐0145, DOI: 10.1002/env.2628

The block maxima approach is an important method in univariate extreme value analysis. While assuming that block maxima are independent results in straightforward analysis, the resulting inferences maybe invalid when a series of block maxima exhibits dependence. We propose a model, based on a first‐order Markov assumption, that incorporates dependence between successive block maxima through the use

Environmental hazard events such as extra‐tropical cyclones or windstorms that develop in the North Atlantic can cause severe societal damage. Environmental hazard is quantified by the hazard footprint, a spatial area describing potential damage. However, environmental hazards are never directly observed, so estimation of the footprint for any given event is primarily reliant on station observations

Modeling data with nonstationary covariance structure is important to represent heterogeneity in geophysical and other environmental spatial processes. In this work, we investigate a two‐stage approach to modeling nonstationary covariances that is efficient for large data sets. First, maximum likelihood estimation is used in local, moving windows to infer spatially varying covariance parameters. These

For many problems of inference about a marginal distribution function, while the entire distribution is important, extreme quantiles are of particular interest because rare outcomes may have large consequences. In some applications, only the extreme upper quantiles require extra attention, but in, for example, climatological applications, extremes in both tails of the distribution can be impactful

We present a multivariate occupancy model to simultaneously model the presence/absence of multiple species, and demonstrate its use with a goal of estimating parameters related to occupancy. The proposed model accounts for both spatial and temporal dependence within each species, as well as dependence across all species. These dependencies are addressed through random effects, defined so there is no

The analysis of circular data is the main subject in many disciplines, such as meteorology and oceanography. In this article, we introduce a new multimodal skew‐circular model as an extension of the circular beta distribution. We propose a truncated power smoothing spline for modeling the skewness parameter and identifying significant factors of the asymmetry. A Markov chain Monte Carlo scheme is provided

Motivated by the widespread use of large gridded data sets in the atmospheric sciences, we propose a new model for extremes of areal data that is inspired by the simultaneous autoregressive (SAR) model in classical spatial statistics. Our extreme SAR model extends recent work on transformed‐linear operations applied to regularly varying random vectors, and is unique among extremes models in being directly

A finite population version of block kriging (FPBK) estimates a total or a mean when there is perfect detection of population units. However, many environmental datasets challenge the assumption of perfect detection. We consider two extensions of FPBK that incorporate imperfect detection. Spatial population estimator with detection: ratio then add (SPEDRA) adjusts observed counts by the estimated detection

Over the past two decades there have been many advancements in modeling capture–recapture (CR) data to account for emerging data collection technology and techniques. Spatial capture–recapture (SCR) models have been introduced to estimate population size and numerous other demographic parameters from spatially explicit CR data. Recently SCR models have also begun incorporating realistic animal movement

Effective air quality management and forecasting in Beijing is urgently needed as the region suffers from the worst air pollution in any standards. However, the statistical mechanism of the PM2.5 formation with respect to various factors is underexplored in this region and China in general. Through an elaborate application with refinement of a spatio‐temporal model with varying coefficients to the

Statistical spatiotemporal environmental data analysis is rarely straightforward, with one having to face challenges relating to big data, non‐Gaussianity, nonstationarity, multiple scales of behavior, deterministic (numerical) model output, and more. One often has to rely heavily on good statistical parallel computing skills and sound knowledge of the application domain. The work of Tagle et al. (2020)

Analysis of the causality between environmental time series is particularly debated nowadays. Checking if the global warming is caused by human activities or the solar irradiance, or if air pollution is produced by industrial plants or consumers' behavior are typical examples. Statistical methods for testing these hypotheses mainly focus on bivariate autoregressive (ARX) models and their fitting performance;

It is well known that the diversity profile provides a complete picture about the evenness of the relative abundance distribution of an ecological population. This complexity measure is a continuous function evaluated on a suitable grid of values x ≥ 0 that determine the measure's sensitivity to the most dominant species. In this paper, a functional design‐based estimation of diversity profiles is

Air pollution is a major public health concern, and large numbers of epidemiological studies have been conducted to quantify its impacts. One study design used to quantify these impacts is a spatial areal unit design, which estimates a population‐level association using data on air pollution concentrations and disease incidence that have been spatially aggregated to a set of nonoverlapping areal units

In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain and are then calibrated against measurements from monitoring stations. However, these different data sources are misaligned in space and time. If misalignment is not considered, it can bias the predictions. We aim at demonstrating how the combination of multiple data sources

In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application‐inferring time‐resolved aerosol concentration from a low‐cost differential pressure sensor. The objective is to estimate a monotone function and make inference

We propose median polish for functional multivariate analysis of variance (FMANOVA) with the implementation of depth for multivariate functional data. As an alternative to classical mean estimation, functional median polish estimates the functional grand effect and factor effects based on functional medians in one‐way and two‐way additive FMANOVA models. Median polish estimates in FMANOVA are visually

In this article, we concentrate on an alternative modeling strategy for positive data that exhibit spatial or spatiotemporal dependence. Specifically, we propose to consider stochastic processes obtained through a monotone transformation of scaled version of χ2 random processes. The latter is well known in the specialized literature and originates by summing independent copies of a squared Gaussian

Protection and safety authorities recommend the use of model averaging to determine the benchmark dose approach as a scientifically more advanced method compared with the no‐observed‐adverse‐effect‐level approach for obtaining a reference point and deriving health‐based guidance values. Model averaging however highly depends on the set of candidate dose–response models and such a set should be rich

Functional data analysis is a statistical framework where data are assumed to follow some functional form. This method of analysis is commonly applied to time series data, where time, measured continuously or in discrete intervals, serves as the location for a function's value. Gaussian processes are a generalization of the multivariate normal distribution to function space and, in this article, they

We propose a Kalman filter algorithm to provide a formal statistical analysis of space‐time data with an autoregressive structure in time. The Kalman filter technique allows to capture the temporal dependence as well as the spatial correlation structure through state‐space equations, and it is aimed to perform statistical inference in terms of parameter estimation and prediction at unobserved locations

As per the records of the World Health Organization, the first formally reported incidence of Zika virus occurred in Brazil in May 2015. The disease then rapidly spread to other countries in Americas and East Asia, affecting more than 1,000,000 people. Zika virus is primarily transmitted through bites of infected mosquitoes of the species Aedes (Aedes aegypti and Aedes albopictus). The abundance of

Saudi Arabia has recently established its renewable energy targets as part of its “Vision 2030” proposal, which represents a roadmap for reducing the country's dependence on oil over the next decade. This study provides a foundational assessment of the wind resource in Saudi Arabia that serves as a guide for the development of the outlined wind energy component. The assessment is based on a new high‐resolution

Incorporating covariates in the second‐ structure of spatial processes is an effective way of building flexible nonstationary covariance models. Fitting these covariances requires covariates to already exist at locations where there is response data. However, studies in environmental statistics often involve covariate and response data that are misaligned in space. A common strategy to remedy this

Characterization of heatwave duration is becoming increasingly important in environmental research as they pose a significant threat to many human lives worldwide. Although several quantification of the extremities of a heatwave have been proposed in literature, they are mostly improvised and there does not exist a universally accepted definition of heatwave. In this article, we devise a probabilistic

A new sampling strategy for design‐based environmental monitoring is proposed. It has the potential to produce superior estimators for totals of environmental variables and their changes over time. In the strategy, we combine two concepts known as spatially balanced sampling and coordination of samples. Spatially balanced sampling can provide superior estimators of totals, while coordination of samples

Environmental extremes often show systematic variation with covariates. Three different nonparametric descriptions (penalized B‐splines, Bayesian adaptive regression splines, and Voronoi partition) for the dependence of extreme value model parameters on covariates are considered. These descriptions take the generic form of a linear combination of basis functions on the covariate domain, but differ

No abstract is available for this article.

In this study, we begin a comprehensive characterization of temperature extremes in Ireland for the period 1981–2010. We produce return levels of anomalies of daily maximum temperature extremes for an area over Ireland, for the 30‐year period 1981–2010. We employ extreme value theory (EVT) to model the data using the generalized Pareto distribution (GPD) as part of a three‐level Bayesian hierarchical

Understanding the complex relationship between the duration and size of forest fires is important in order to better predict these key characteristics of fires for fire management purposes in a changing climate. Describing this relationship is also important for our fundamental understanding of fire science. Here, we develop and utilize novel techniques for characterizing the distribution of multiple

Animal movement studies have become ubiquitous in animal ecology for the estimation of space use and the analysis of movement behavior. In these studies, animal movement data are primarily collected at regular time intervals. We propose an irregular sampling design that could lead to greater efficiency and information gain in animal movement studies. Our novel sampling design, called lattice and random

The multivariate smooth transition autoregressive model with order k (M‐STAR)(k) is a nonlinear multivariate time series model able to capture regime changes in the conditional mean. The main aim of this paper is to develop a Bayesian estimation scheme for the M‐STAR(k) model that includes the coefficient parameter matrix, transition function parameters, covariance parameter matrix, and the model order

Color is one of the most important features in any textile material. Due to its competitive price, most of the colorants currently used for textile dyeing are synthetic, originated from nonrenewable sources, and highly pollutant. There is an increasing interest for natural processes to dye fabrics. When new textile dyeing technologies are developed, evaluating the quality of these techniques involves

Atmospheric inverse modeling is a method for reconstructing historical fluxes of green‐house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric concentrations in relation to the number of unknown flux components makes the inverse problem ill‐conditioned, and assumptions on the fluxes are needed

This paper deals with analyzing structural breaks in the covariance operator of sequentially observed functional data. For this purpose, procedures are developed to segment an observed stretch of curves into periods for which second‐order stationarity may be reasonably assumed. The proposed methods are based on measuring the fluctuations of sample eigenvalues, either individually or jointly, and traces

This paper proposes a novel nonparametric approach to model and reveal differences in the geochemical properties of the soil, when these are described by space–time measurements collected in a spatial region naturally divided into two parts. The investigation is motivated by a real study on a space–time geochemical data set, consisting of measurements of potassium chloride pH, water pH, and percentage

We introduce a novel geometry‐oriented methodology, based on the emerging tools of topological data analysis, into the change‐point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data‐generating process. While the applications of topological data analysis to change‐point detection are potentially very broad, in this paper

Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM2.5), in which data are usually not measured at all study locations. PM2.5 is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lowerdimensional representative scores of such multipollutant

One of the major concerns engendered by a warming climate are changing sea levels and their lasting effects on coastal populations, infrastructures, and natural habitats. Sea levels are now monitored by satellites, but long‐term records are only available at discrete locations along the coasts. Sea levels and sea‐level processes must be better understood at the local level to best inform real‐world

A hierarchical Bayesian model is proposed to quantify the magnitude of extreme surges on the Atlantic coast of Canada with limited data. Generalized extreme value distributions are fitted to surges derived from water levels measured at 21 buoys along the coast. The parameters of these distributions are linked together through a Gaussian field whose mean and variance are driven by atmospheric sea‐level

In this paper, we use a Bayesian spatial model to spatially interpolate forest inventory data from the Timiskaming and Abitibi River forests in Ontario, Canada. We consider a Bayesian generalized linear geostatistical model and implement a Markov chain Monte Carlo algorithm to sample from its posterior distribution. How spatial predictions for new sites in the forests change as the amount of training

No abstract is available for this article.