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We conduct a case study of observed and simulated maximum daily 8-h average (MDA8) ozone (O3) 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 O3. We employ regional coupled chemistry-transport model simulations to make three types of comparisons between simulated and observational data, comparing (1) tails of the O3 response variable, (2) distributions of meteorological predictor variables, and (3) sensitivities of high and extreme O3 to meteorological predictors. This last comparison is made using two methods: quantile regression, for the 0.95 quantile of O3, and tail dependence optimization, which is used to investigate even higher O3 extremes. Across all three locations, we find substantial differences between simulations and observational data in both meteorology and meteorological sensitivities of high and extreme O3.

Cover crops (CCs) offer in‐field and environmental benefits when integrated into cropping systems. Low CC adoption in the Southern Piedmont region of South Carolina is partially due to the lack of information on CC performance and benefits within the region. To address this, eight winter CCs and a fallow/pigweed (Amaranthus spp.) treatment were investigated for their influence on soil temperature, volumetric water content (VWC), percent cover, biomass, and the occurrence of soil water repellency (SWR). A randomized complete block design experiment was conducted in the fall and winter of 2021–2022 (EXP A) and repeated in 2022–2023 (EXP B). Experiments were treated separately as methods were revised for EXP B. CCs minimally influenced soil VWC over both experiments with no consistent trend. CC did not influence soil temperatures during EXP A. In EXP B, the fallow/pigweed had the highest soil temperatures on two (out of 10) measuring events (p < 0.05). No SWR was found in either experiment. Establishment and fresh and dry CC biomass were most likely influenced by air temperatures and daylight hours driving germination during days with minimal rainfall. In both experiments, annual rye (Lolium multiflorum) produced cover quickly and yielded high biomass. Crimson clover (Trifolium incarnatum) took longer to establish but also yielded one of the highest biomasses. This study demonstrated that winter CCs had little influence on soil physical properties and that while cereal rye (Secale cereale) is a common CC utilized for erosion control, the greater biomass and surface roots of annual rye make it a superior CC for use in Southern Piedmont agroecosystems. Core Ideas: Results are from a 2‐year field study in Clemson, South Carolina that examined eight common winter cover crops.Soil volumetric water content and temperature were minimally influenced by cover crops in this field study.Annual rye appears to have a greater potential to reduce soil erosion than other cover crops within the region. [ABSTRACT FROM AUTHOR]

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 PM2.5 to atypically high levels are even more dangerous. These spikes are believed to be associated with regionally specific meteorological factors. To quantify these associations, we develop a Bayesian spatiotemporal quantile regression model to estimate the spatially varying effects of meteorological variables purported to be related to PM2.5 levels. By adopting a quantile regression model, we are able to examine the entire distribution of PM2.5 levels; for example, we are able to identify which meteorological drivers are related to abnormally high PM2.5 levels. Our approach uses penalized splines to model the spatially varying meteorological effects and to account for spatiotemporal dependence. The performance of the methodology is evaluated through extensive numerical studies. We apply our modeling techniques to 5 years of daily PM2.5 data collected throughout the eastern United States to reveal the effects of various meteorological drivers. [ABSTRACT FROM AUTHOR]

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 of a bivariate logistic dependence structure while maintaining generalized extreme value (GEV) marginal distributions. Modeling dependence in this manner allows us to better estimate extreme quantiles when block maxima exhibit short‐ranged dependence. We demonstrate via a simulation study that our first‐order Markov GEV model performs well when successive block maxima are dependent, while still being reasonably robust when maxima are independent. We apply our method to two polar annual minimum air temperature data sets that exhibit short‐ranged dependence structures, and find that the proposed model yields modified estimates of high quantiles. [ABSTRACT FROM AUTHOR]

Hurricane Harvey brought extreme levels of rainfall to the Houston, Texas, area over a 7‐day period in August 2017, resulting in catastrophic flooding that caused loss of human life and damage to personal property and public infrastructure. In the wake of this event, there has been interest in understanding the degree to which this event was unusual and estimating the probability of experiencing a similar event in other locations. Additionally, researchers have aimed to better understand the ways in which the sea surface temperature (SST) in the Gulf of Mexico (GoM) is associated with precipitation extremes in this region. This work addresses all of these issues through the development of a multivariate spatial extreme value model. Our analysis indicates that warmer GoM SSTs are associated with higher precipitation extremes in the western Gulf Coast region during hurricane season and that the precipitation totals observed during Hurricane Harvey are less unusual based on the warm GoM SST in 2017. As SSTs in the GoM are expected to steadily increase over the remainder of this century, this analysis suggests that western Gulf Coast locations may experience more severe precipitation extremes during hurricane season. [ABSTRACT FROM AUTHOR]

The October 2015 precipitation event in the Southeastern United States brought large amounts of rainfall to South Carolina, with particularly heavy amounts in Charleston and Columbia. The subsequent flooding resulted in numerous casualties and hundreds of millions of dollars in property damage. Precipitation levels were so severe that media outlets and government agencies labeled this storm as a 1 in 1000-year event in parts of the state. Two points of discussion emerged as a result of this event. The first was related to understanding the degree to which this event was anomalous; the second was related to understanding whether precipitation extremes in South Carolina have changed over recent time. In this work, 50 years of daily precipitation data at 28 locations are used to fit a spatiotemporal hierarchical model, with the ultimate goal of addressing these two points of discussion. Bayesian inference is used to estimate return levels and to perform a severity-area-frequency analysis, and it is determined that precipitation levels related to this event were atypical throughout much of the state, but were particularly unusual in the Columbia area. This analysis also finds marginal evidence in favor of the claim that precipitation extremes in the Carolinas have become more intense over the last 50 years. [ABSTRACT FROM AUTHOR]

Fine particulate matter ( $$\hbox {PM}_{2.5}$$ ) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level $$\hbox {PM}_{2.5}$$ . Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of $$\hbox {PM}_{2.5}$$ data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both 'typical' and 'high' $$\hbox {PM}_{2.5}$$ events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location. [ABSTRACT FROM AUTHOR]

This article reports that the Casper College Statistics Department in Wyoming is offering several statistics courses, including Fundamentals of Sampling, Experimental Design, and Categorical Data Analysis. Topics discussed include the Casper College Statistics Program and the articulation agreements that Casper College has with the University of Wyoming. The successes of the Casper College Statistics Department and challenges the department may face in the future are also mentioned.

Fine particulate matter (PM2.5) poses a significant risk to human health, with long-term exposure being linked to conditions such as asthma, chronic bronchitis, lung cancer, and atherosclerosis. In order to improve the current pollution control strategies and to better shape public policy, the development of a more comprehensive understanding of this air pollutant is necessary. To this end, this work attempts to quantify the relationship between certain meteorological drivers and the levels of PM2.5. It is expected that the set of important meteorological drivers will vary both spatially and within the conditional distribution of PM2.5 levels. To account for these characteristics, a new local linear penalized quantile regression methodology is developed. The proposed estimator uniquely selects the set of important drivers at every spatial location and for each quantile of the conditional distribution of PM2.5 levels. The performance of the proposed methodology is illustrated through simulation, and it is then used to determine the association between several meteorological drivers and PM2.5 over the Eastern United States. This analysis suggests that the primary drivers throughout much of the Eastern United States tend to differ based on season and geographic location, with similarities existing between 'typical' and 'high' PM2.5 levels. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5 ) poses a significant risk to human health, with long-term exposure being linked to conditions such as asthma, chronic bronchitis, lung cancer, atherosclerosis, etc. In order to improve current pollution control strategies and to better shape public policy, the development of a more comprehensive understanding of this air pollutant is necessary. To this end, this work attempts to quantify the relationship between certain meteorological drivers and the levels of PM 2.5 . It is expected that the set of important meteorological drivers will vary both spatially and within the conditional distribution of PM 2.5 levels. To account for these characteristics, a new local linear penalized quantile regression methodology is developed. The proposed estimator uniquely selects the set of important drivers at every spatial location and for each quantile of the conditional distribution of PM 2.5 levels. The performance of the proposed methodology is illustrated through simulation, and it is then used to determine the association between several meteorological drivers and PM 2.5 over the Eastern United States (US). This analysis suggests that the primary drivers throughout much of the Eastern US tend to differ based on season and geographic location, with similarities existing between "typical" and "high" PM 2.5 levels.