Environmetrics最新文献

{"title":"Estimating atmospheric motion winds from satellite image data using space-time drift models","authors":"Indranil Sahoo,&nbsp;Joseph Guinness,&nbsp;Brian J. Reich","doi":"10.1002/env.2818","DOIUrl":"10.1002/env.2818","url":null,"abstract":"<p>Geostationary weather satellites collect high-resolution data comprising a series of images. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data and estimate atmospheric winds by tracking features in the images. However, the wind estimates from the DMW Algorithm are often missing and do not come with uncertainty measures. Also, the DMW Algorithm estimates can only be half-integers, since the algorithm requires the original and shifted data to be at the same locations, in order to calculate the displacement vector between them. This motivates us to statistically model wind motions as a spatial process drifting in time. Using a covariance function that depends on spatial and temporal lags and a drift parameter to capture the wind speed and wind direction, we estimate the parameters by local maximum likelihood. Our method allows us to compute standard errors of the local estimates, enabling spatial smoothing of the estimates using a Gaussian kernel weighted by the inverses of the estimated variances. We conduct extensive simulation studies to determine the situations where our method performs well. The proposed method is applied to the GOES-15 brightness temperature data over Colorado and reduces prediction error of brightness temperature compared to the DMW Algorithm.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 8","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2818","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87866159","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}

{"title":"Long memory conditional random fields on regular lattices","authors":"Angela Ferretti,&nbsp;L. Ippoliti,&nbsp;P. Valentini,&nbsp;R. J. Bhansali","doi":"10.1002/env.2817","DOIUrl":"https://doi.org/10.1002/env.2817","url":null,"abstract":"<p>This paper draws its motivation from applications in geophysics, agricultural, and environmental sciences where empirical evidence of slow decay of correlations have been found for data observed on a regular lattice. Spatial ARFIMA models represent a widely used class of spatial models for analyzing such data. Here, we consider their generalization to conditional autoregressive fractional integrated moving average (CARFIMA) models, a larger class of long memory models which allows a wider range of correlation behavior. For this class we provide detailed descriptions of important representative models, make the necessary comparison with some other existing models, and discuss some important inferential and computational issues on estimation, simulation and long memory process approximation. Results from model fit comparison and predictive performance of CARFIMA models are also discussed through a statistical analysis of satellite land surface temperature data.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 5","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50123883","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}

{"title":"Spatio-temporal downscaling emulator for regional climate models","authors":"Luis A. Barboza,&nbsp;Shu Wei Chou Chen,&nbsp;Marcela Alfaro Córdoba,&nbsp;Eric J. Alfaro,&nbsp;Hugo G. Hidalgo","doi":"10.1002/env.2815","DOIUrl":"https://doi.org/10.1002/env.2815","url":null,"abstract":"<p>Regional climate models (RCM) describe the mesoscale global atmospheric and oceanic dynamics and serve as dynamical downscaling models. In other words, RCMs use atmospheric and oceanic climate output from general circulation models (GCM) to develop a higher resolution climate output. They are computationally demanding and, depending on the application, require several orders of magnitude of compute time more than statistical climate downscaling. In this article, we describe how to use a spatio-temporal statistical model with varying coefficients (VC), as a downscaling emulator for a RCM using VC. In order to estimate the proposed model, two options are compared: INLA, and varycoef. We set up a simulation to compare the performance of both methods for building a statistical downscaling emulator for RCM, and then show that the emulator works properly for NARCCAP data. The results show that the model is able to estimate non-stationary marginal effects, which means that the downscaling output can vary over space. Furthermore, the model has flexibility to estimate the mean of any variable in space and time, and has good prediction results. INLA was the fastest method for all the cases, and the approximation with best accuracy to estimate the different parameters from the model and the posterior distribution of the response variable.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50129877","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}

{"title":"Generalized gamma ARMA process for synthetic aperture radar amplitude and intensity data","authors":"Willams B. F. da Silva,&nbsp;Pedro M. Almeida-Junior,&nbsp;Abraão D. C. Nascimento","doi":"10.1002/env.2816","DOIUrl":"https://doi.org/10.1002/env.2816","url":null,"abstract":"<p>We propose a new autoregressive moving average (ARMA) process with generalized gamma (G<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Γ</mi>\u0000 </mrow>\u0000 <annotation>$$ Gamma $$</annotation>\u0000 </semantics></math>) marginal law, called G<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Γ</mi>\u0000 </mrow>\u0000 <annotation>$$ Gamma $$</annotation>\u0000 </semantics></math>-ARMA. We derive some of its mathematical properties: moment-based closed-form expressions, score function, and Fisher information matrix. We provide a procedure for obtaining maximum likelihood estimates for the G<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Γ</mi>\u0000 </mrow>\u0000 <annotation>$$ Gamma $$</annotation>\u0000 </semantics></math>-ARMA parameters. Its performance is quantified and discussed using Monte Carlo experiments, considering (among others) various link functions. Finally, our proposal is applied to solve remote sensing problems using synthetic aperture radar (SAR) imagery. In particular, the G<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Γ</mi>\u0000 </mrow>\u0000 <annotation>$$ Gamma $$</annotation>\u0000 </semantics></math>-ARMA process is applied to real data from images taken in the Munich and San Francisco regions. The results show that G<math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>Γ</mi>\u0000 </mrow>\u0000 <annotation>$$ Gamma $$</annotation>\u0000 </semantics></math>-ARMA describes the neighborhoods of SAR features better than the gamma-ARMA process (a reference for asymmetric positive data). For pixel ray modeling, our proposal outperforms <math>\u0000 <mrow>\u0000 <msubsup>\u0000 <mrow>\u0000 <mi>𝒢</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>I</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>0</mn>\u0000 </mrow>\u0000 </msubsup>\u0000 </mrow></math> and gamma-ARMA.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50128036","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}

{"title":"Bayesian geostatistical modeling for discrete-valued processes","authors":"Xiaotian Zheng,&nbsp;Athanasios Kottas,&nbsp;Bruno Sansó","doi":"10.1002/env.2805","DOIUrl":"https://doi.org/10.1002/env.2805","url":null,"abstract":"<p>We introduce a flexible and scalable class of Bayesian geostatistical models for discrete data, based on nearest-neighbor mixture processes (NNMP), referred to as discrete NNMP. To define the joint probability mass function (pmf) over a set of spatial locations, we build from local mixtures of conditional pmfs using a directed graphical model, with a directed acyclic graph that summarizes the nearest neighbor structure. The approach supports direct, flexible modeling for multivariate dependence through specification of general bivariate discrete distributions that define the conditional pmfs. In particular, we develop a modeling and inferential framework for copula-based NNMPs that can attain flexible dependence structures, motivating the use of bivariate copula families for spatial processes. Moreover, the framework allows for construction of models given a pre-specified family of marginal distributions that can vary in space, facilitating covariate inclusion. Compared to the traditional class of spatial generalized linear mixed models, where spatial dependence is introduced through a transformation of response means, our process-based modeling approach provides both computational and inferential advantages. We illustrate the methodology with synthetic data examples and an analysis of North American Breeding Bird Survey data.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2805","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50118030","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}

{"title":"Assessing the ability of adaptive designs to capture trends in hard coral cover","authors":"AWLP Thilan,&nbsp;P Menéndez,&nbsp;JM McGree","doi":"10.1002/env.2802","DOIUrl":"https://doi.org/10.1002/env.2802","url":null,"abstract":"<p>Coral reefs have become one of the most vulnerable ecosystems worldwide due to rising environmental and anthropogenic pressures. Methods from experimental design can be used to furnish our ability to monitor such ecosystems efficiently. Recently, adaptive design approaches have been proposed for monitoring coral reefs; however, questions have surfaced around the ability of such approaches to capture trends over time. The aim of this study was to develop an approach to assess trends in hard coral cover and evaluate the effectiveness of adaptive designs for estimating such trends in coral reef communities within a region of the Great Barrier Reef. Our approach was couched within a Bayesian design and inference framework such that uncertainty was captured rigorously and so that information from accumulating data can be incorporated straightforwardly to inform future data collection. The designs found under this approach were compared to historical non-adaptive designs which surveyed all locations over time. Through this comparison, we show that adaptive designs can maintain trends over time with little to no loss in information, even when sampling effort is substantially reduced. Accordingly, this research serves to further promote adaptive design methods for efficiently and effectively sampling in ecological monitoring.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 6","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2802","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50125381","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}

{"title":"A hierarchical Bayesian non-asymptotic extreme value model for spatial data","authors":"Federica Stolf,&nbsp;Antonio Canale","doi":"10.1002/env.2806","DOIUrl":"https://doi.org/10.1002/env.2806","url":null,"abstract":"<p>Spatial maps of extreme precipitation are crucial in flood prevention. With the aim of producing maps of precipitation return levels, we propose a novel approach to model a collection of spatially distributed time series where the asymptotic assumption, typical of the traditional extreme value theory, is relaxed. We introduce a Bayesian hierarchical model that accounts for the possible underlying variability in the distribution of event magnitudes and occurrences, which are described through latent temporal and spatial processes. Spatial dependence is characterized by geographical covariates and effects not fully described by the covariates are captured by spatial structure in the hierarchies. The performance of the approach is illustrated through simulation studies and an application to daily rainfall extremes across North Carolina (USA). The results show that we significantly reduce the estimation uncertainty with respect to state of the art techniques.</p>","PeriodicalId":50512,"journal":{"name":"Environmetrics","volume":"34 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/env.2806","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50120792","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}

{"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 CO₂ in the atmosphere has consequences for the climate but strongly deny that the magnitude of the effect is significant. Using hourly CO₂ 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. CO₂ 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}

{"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}

{"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}