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CO2 has significant implications for hourly ambient temperature: Evidence from Hawaii 二氧化碳对每小时环境温度有重大影响：来自夏威夷的证据
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-05-03 DOI: 10.1002/env.2803
Kevin F. Forbes
{"title":"CO2 has significant implications for hourly ambient temperature: Evidence from Hawaii","authors":"Kevin F. Forbes","doi":"10.1002/env.2803","DOIUrl":"https://doi.org/10.1002/env.2803","url":null,"abstract":"<p>A small group of climate scientists and influencers have vigorously disputed the scientific consensus on climate change. They have contributed to a belief system that has impeded policy actions to reduce emissions. They accept that more CO<sub>2</sub> in the atmosphere has consequences for the climate but strongly deny that the magnitude of the effect is significant. Using hourly CO<sub>2</sub> data from the Mauna Loa Observatory in Hawaii, this article examines whether the hourly temperature data at the nearby Hilo International Airport support this belief. ARCH/ARMAX methods are employed because the hourly temperature data, even in Hawaii, are both highly autoregressive and volatile. The temperature data are analyzed using an archive of day-ahead hourly weather forecast data to control for expected meteorological outcomes. The model is estimated using 42,928 hourly observations from August 7, 2009, through December 31, 2014. CO<sub>2</sub> concentrations are found to have statistically significant implications for hourly temperature. The model is evaluated using hourly data from January 1, 2015, through December 31, 2017. The findings add to the consilience of evidence supporting the scientific consensus on climate change.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50119499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Detection of anomalous radioxenon concentrations: A distribution-free approach 异常放射性氙浓度的检测：一种无分布的方法
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-04-24 DOI: 10.1002/env.2804
Michele Scagliarini, Rosanna Gualdi, Giuseppe Ottaviano, Antonietta Rizzo
{"title":"Detection of anomalous radioxenon concentrations: A distribution-free approach","authors":"Michele Scagliarini,&nbsp;Rosanna Gualdi,&nbsp;Giuseppe Ottaviano,&nbsp;Antonietta Rizzo","doi":"10.1002/env.2804","DOIUrl":"https://doi.org/10.1002/env.2804","url":null,"abstract":"<p>The detection of anomalous atmospheric radioxenon concentrations plays a key role in detecting both underground nuclear explosions and radioactive emissions from nuclear power plants and medical isotope production facilities. For this purpose, the CTBTO's International Data Centre uses a procedure based on descriptive thresholds. In order to supplement this procedure with a statistical inference-based method, we compared several non-parametric change-point control charts for detecting shifts above the natural radioxenon background. The results indicate that the proposed methods can provide valuable tools for the institutions responsible for the verification and classification of anomalous radioxenon concentrations.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50142265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mitigating spatial confounding by explicitly correlating Gaussian random fields 通过显式关联高斯随机场减轻空间混淆
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-03-27 DOI: 10.1002/env.2801
Isa Marques, Thomas Kneib, Nadja Klein
{"title":"Mitigating spatial confounding by explicitly correlating Gaussian random fields","authors":"Isa Marques,&nbsp;Thomas Kneib,&nbsp;Nadja Klein","doi":"10.1002/env.2801","DOIUrl":"https://doi.org/10.1002/env.2801","url":null,"abstract":"<p>In the fourth column under the row “MGRF” in Table 1 of Marques et al. (<span>2022</span>) the mean value was incorrect in the original published article. The mean value should read “−0.143” and not “0.143.” The correct table appears below:</p><p>The online version of the article has been corrected.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 4","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50141057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Subordinated Gaussian processes for solar irradiance 太阳辐照度的隶属高斯过程
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-03-23 DOI: 10.1002/env.2800
Caitlin M. Berry, William Kleiber, Bri-Mathias Hodge
{"title":"Subordinated Gaussian processes for solar irradiance","authors":"Caitlin M. Berry,&nbsp;William Kleiber,&nbsp;Bri-Mathias Hodge","doi":"10.1002/env.2800","DOIUrl":"https://doi.org/10.1002/env.2800","url":null,"abstract":"<p>Traditionally the power grid has been a one-way street with power flowing from large transmission-connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by-the-second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non-stationary and non-Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non-Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50153722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multistage hierarchical capture–recapture models 多阶段分层捕获-重新捕获模型
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-03-20 DOI: 10.1002/env.2799
Mevin B. Hooten, Michael R. Schwob, Devin S. Johnson, Jacob S. Ivan
{"title":"Multistage hierarchical capture–recapture models","authors":"Mevin B. Hooten,&nbsp;Michael R. Schwob,&nbsp;Devin S. Johnson,&nbsp;Jacob S. Ivan","doi":"10.1002/env.2799","DOIUrl":"https://doi.org/10.1002/env.2799","url":null,"abstract":"<p>Ecologists increasingly rely on Bayesian methods to fit capture–recapture models. Capture–recapture models are used to estimate abundance while accounting for imperfect detectability in individual-level data. A variety of implementations exist for such models, including integrated likelihood, parameter-expanded data augmentation, and combinations of those. Capture–recapture models with latent random effects can be computationally intensive to fit using conventional Bayesian algorithms. We identify alternative specifications of capture–recapture models by considering a conditional representation of the model structure. The resulting alternative model can be specified in a way that leads to more stable computation and allows us to fit the desired model in stages while leveraging parallel computing resources. Our model specification includes a component for the capture history of detected individuals and another component for the sample size which is random before observed. We demonstrate this approach using three examples including simulation and two datasets resulting from capture–recapture studies of different species.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50139245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Approximation of Bayesian Hawkes process with inlabru 用inlabru逼近Bayesian-Hawkes过程
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-03-14 DOI: 10.1002/env.2798
Francesco Serafini, Finn Lindgren, Mark Naylor
{"title":"Approximation of Bayesian Hawkes process with inlabru","authors":"Francesco Serafini,&nbsp;Finn Lindgren,&nbsp;Mark Naylor","doi":"10.1002/env.2798","DOIUrl":"https://doi.org/10.1002/env.2798","url":null,"abstract":"<p>Hawkes process are very popular mathematical tools for modeling phenomena exhibiting a <i>self-exciting</i> or <i>self-correcting</i> behavior. Typical examples are earthquakes occurrence, wild-fires, drought, capture-recapture, crime violence, trade exchange, and social network activity. The widespread use of Hawkes process in different fields calls for fast, reproducible, reliable, easy-to-code techniques to implement such models. We offer a technique to perform approximate Bayesian inference of Hawkes process parameters based on the use of the R-package <span>inlabru</span> . The <span>inlabru</span> R-package, in turn, relies on the INLA methodology to approximate the posterior of the parameters. Our Hawkes process approximation is based on a decomposition of the log-likelihood in three parts, which are linearly approximated separately. The linear approximation is performed with respect to the mode of the parameters' posterior distribution, which is determined with an iterative gradient-based method. The approximation of the posterior parameters is therefore deterministic, ensuring full reproducibility of the results. The proposed technique only requires the user to provide the functions to calculate the different parts of the decomposed likelihood, which are internally linearly approximated by the R-package <span>inlabru</span> . We provide a comparison with the <span>bayesianETAS</span>  R-package which is based on an MCMC method. The two techniques provide similar results but our approach requires two to ten times less computational time to converge, depending on the amount of data.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2798","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50132691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
New estimation methods for extremal bivariate return curves 极值二元回归曲线的新估计方法
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-02-17 DOI: 10.1002/env.2797
C. J. R. Murphy-Barltrop, J. L. Wadsworth, E. F. Eastoe
{"title":"New estimation methods for extremal bivariate return curves","authors":"C. J. R. Murphy-Barltrop,&nbsp;J. L. Wadsworth,&nbsp;E. F. Eastoe","doi":"10.1002/env.2797","DOIUrl":"https://doi.org/10.1002/env.2797","url":null,"abstract":"<p>In the multivariate setting, estimates of extremal risk measures are important in many contexts, such as environmental planning and structural engineering. In this paper, we propose new estimation methods for extremal bivariate return curves, a risk measure that is the natural bivariate extension to a return level. Unlike several existing techniques, our estimates are based on bivariate extreme value models that can capture both key forms of extremal dependence. We devise tools for validating return curve estimates, as well as representing their uncertainty, and compare a selection of curve estimation techniques through simulation studies. We apply the methodology to two metocean data sets, with diagnostics indicating generally good performance.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50144470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Environmental data science: Part 2 环境数据科学：第2部分
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-02-16 DOI: 10.1002/env.2788
Wesley S. Burr, Nathaniel K. Newlands, Andrew Zammit-Mangion
{"title":"Environmental data science: Part 2","authors":"Wesley S. Burr,&nbsp;Nathaniel K. Newlands,&nbsp;Andrew Zammit-Mangion","doi":"10.1002/env.2788","DOIUrl":"https://doi.org/10.1002/env.2788","url":null,"abstract":"<div>\u0000 <p>Environmental data science is a multi-disciplinary and mature field of research at the interface of statistics, machine learning, information technology, climate and environmental science. The two-part special issue ‘Environmental Data Science’ comprises a set of research articles and opinion pieces led by statisticians who are at the forefront of the field. This editorial identifies and discusses common research themes that appear in the contributions to Part 2, which focuses on applications. These include spatio-temporal modeling; the problem of aggregation and sparse sampling; the importance of community-building and training for the next generation of specialists in environmental data science; and the need to look forward at the challenges that lie ahead for the discipline. This editorial complements that of Part 1, which largely focuses on statistical methodology; see Zammit-Mangion, Newlands, and Burr (2023).</p>\u0000 </div>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50134738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparing emulation methods for a high-resolution storm surge model 高分辨率风暴潮模型仿真方法的比较
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-02-14 DOI: 10.1002/env.2796
Grant Hutchings, Bruno Sansó, James Gattiker, Devin Francom, Donatella Pasqualini
{"title":"Comparing emulation methods for a high-resolution storm surge model","authors":"Grant Hutchings,&nbsp;Bruno Sansó,&nbsp;James Gattiker,&nbsp;Devin Francom,&nbsp;Donatella Pasqualini","doi":"10.1002/env.2796","DOIUrl":"https://doi.org/10.1002/env.2796","url":null,"abstract":"<p>Realistic simulations of complex systems are fundamental for climate and environmental studies. Large computer systems are often not sufficient to run sophisticated computational models for large numbers of different input settings. Statistical surrogate models, or emulators, are key tools enabling fast exploration of the simulator input space. Gaussian processes have become standard for computer simulator emulation. However, they require careful implementation to scale appropriately, motivating alternative methods more recently introduced. We present a comparison study of surrogates of the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) simulator—the simulator of choice for government agencies—using four emulation approaches: BASS; BART; SEPIA; and RobustGaSP. SEPIA and RobustGaSP use Gaussian processes, BASS implements adaptive splines, and BART is based on ensembles of regression trees. We describe the four models and compare them in terms of computation time and predictive metrics. These surrogates use proven and distinct methodologies, are available through accessible software, and quantify prediction uncertainty. Our data cover millions of response values. We find that SEPIA and RobustGaSP provide exceptional predictive power, but cannot scale to emulate experiments as large as the one considered in this paper as effectively as BASS and BART.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50132634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Smooth copula-based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada 基于光滑copula的加拿大中东部极端降雨广义极值模型和空间插值
IF 1.7 3区 环境科学与生态学
Environmetrics Pub Date : 2023-02-11 DOI: 10.1002/env.2795
Fatima Palacios-Rodriguez, Elena Di Bernardino, Melina Mailhot
{"title":"Smooth copula-based generalized extreme value model and spatial interpolation for extreme rainfall in Central Eastern Canada","authors":"Fatima Palacios-Rodriguez,&nbsp;Elena Di Bernardino,&nbsp;Melina Mailhot","doi":"10.1002/env.2795","DOIUrl":"https://doi.org/10.1002/env.2795","url":null,"abstract":"<p>This paper proposes a smooth copula-based Generalized Extreme Value (GEV) model to map and predict extreme rainfall in Central Eastern Canada. The considered data contains a large portion of missing values, and one observes several nonconcomitant record periods at different stations. The proposed two-step approach combines GEV parameters' smooth functions in space through the use of spatial covariates and a flexible hierarchical copula-based model to take into account dependence between the recording stations. The hierarchical copula structure is detected via a clustering algorithm implemented with an adapted version of the copula-based dissimilarity measure recently introduced in the literature. Finally, we compare the classical GEV parameter interpolation approaches with the proposed smooth copula-based GEV modeling approach.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 3","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50149033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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