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81.
Sam Clifford Samantha Low‐Choy Mandana Mazaheri Farhad Salimi Lidia Morawska Kerrie Mengersen 《Environmetrics》2019,30(7)
In environmental monitoring, the ability to obtain high‐quality data across space and time is often limited by the cost of purchasing, deploying and maintaining a large collection of equipment, and the employment of personnel to perform these tasks. An ideal design for a monitoring campaign would be dense enough in time to capture short‐range variation at each site, long enough in time to examine trends at each site and across all sites, and dense enough in space to allow modelling of the relationship between the means at each of the sites. This paper outlines a methodology for semiparametric spatiotemporal modelling of data that is dense in time but sparse in space, obtained from a split panel design, the most feasible approach to covering space and time with limited equipment. The data are hourly averaged particle number concentration (PNC) and were collected as part of the International Laboratory for Air Quality and Health's Ultrafine Particles from Traffic Emissions and Children's Health (UPTECH) project. The panel design comprises two weeks of continuous measurements taken at each of a number of government primary schools in the Brisbane Metropolitan Area, with each school visited sequentially. The school data are augmented by data from long‐term monitoring stations at three locations in Brisbane, Australia. The temporal part of the model explains daily and weekly cycles in PNC at the schools. The temporal variation is modelled hierarchically with a penalised random walk term common to all sites and a similar term accounting for the remaining temporal trend at each site. The modelling of temporal trends requires an acknowledgement that the observations are correlated rather than independent. At each school and long‐term monitoring site, peaks in PNC can be attributed to the morning and afternoon rush hour traffic and new particle formation events. The spatial component of the model describes the school‐to‐school variation in mean PNC at each school and within each school ground. The spatial term in the model is derived from a stochastic partial differential equation and approximates a Gaussian process with a Gaussian Markov Random field. Fitting the model helps describe spatial and temporal variability at a subset of the UPTECH schools and the long‐term monitoring sites, which can be used to estimate the exposure of school children to ultrafine particles. Parameter estimates and their uncertainty are computed in a computationally efficient approximate Bayesian inference environment, R‐INLA. 相似文献
82.
Ensembles of forecasts are typically employed to account for the forecast uncertainties inherent in predictions of future weather states. However, biases and dispersion errors often present in forecast ensembles require statistical post‐processing. Univariate post‐processing models such as Bayesian model averaging (BMA) have been successfully applied for various weather quantities. Nonetheless, BMA and many other standard post‐processing procedures are designed for a single weather variable, thus ignoring possible dependencies among weather quantities. In line with recently upcoming research to develop multivariate post‐processing procedures, for example, BMA for bivariate wind vectors, or flexible procedures applicable for multiple weather quantities of different types, a bivariate BMA model for joint calibration of wind speed and temperature forecasts is proposed on the basis of the bivariate truncated normal distribution. It extends the univariate truncated normal BMA model designed for post‐processing ensemble forecast of wind speed by adding a normally distributed temperature component with a covariance structure representing the dependency among the two weather quantities. The method is applied to wind speed and temperature forecasts of the eight‐member University of Washington mesoscale ensemble and of the 11‐member Aire Limitée Adaptation dynamique Développement International‐Hungary Ensemble Prediction System (ALADIN‐HUNEPS) ensemble of the Hungarian Meteorological Service, and its predictive performance is compared to that of the independent BMA calibration of these weather quantities and the general Gaussian copula method. The results indicate improved calibration of probability and accuracy of point forecasts in comparison to the raw ensemble and the independent BMA approach, and the overall performance of this bivariate model is able to keep up with that of the Gaussian copula method. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
83.
基于相关性分析的 PCBA 热力 学模型修正 总被引：1，自引：0，他引：1
目的精确且高效地对印制电路板组件热力学模型进行修正。方法采用基于拉丁超立方抽样试验设计和Speraman等级相关系数计算公式的相关性分析方法,找出电子产品热仿真试验中对元器件表面温度值影响较大的输入参数,然后进一步分析得出输入与输出之间的函数关系。在此基础上给出印制电路板组件(PCBA)热力学模型修正的一般方法流程。最后利用该方法对某航空电子产品中一块PCBA的热力学模型进行修正。结果修正结果较精确且只调用2次有限元软件。结论该热模型修正方法具有较高的精确性和高效性,可推广用于工程实践。 相似文献
84.
We introduce an adapted form of the Markov random field (MRF) for Bayesian spatial smoothing with small‐area data. This new scheme allows the amount of smoothing to vary in different parts of a map by employing area‐specific smoothing parameters, related to the variance of the MRF. We take an empirical Bayes approach, using variance information from a standard MRF analysis to provide prior information for the smoothing parameters of the adapted MRF. The scheme is shown to produce proper posterior distributions for a broad class of models. We test our method on both simulated and real data sets, and for the simulated data sets, the new scheme is found to improve modelling of both slowly‐varying levels of smoothness and discontinuities in the response surface. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
85.
We develop a Bayesian model–based approach to finite population estimation accounting for spatial dependence. Our innovation here is a framework that achieves inference for finite population quantities in spatial process settings. A key distinction from the small area estimation setting is that we analyze finite populations referenced by their geographic coordinates. Specifically, we consider a two‐stage sampling design in which the primary units are geographic regions, the secondary units are point‐referenced locations, and the measured values are assumed to be a partial realization of a spatial process. Estimation of finite population quantities from geostatistical models does not account for sampling designs, which can impair inferential performance, whereas design‐based estimates ignore the spatial dependence in the finite population. We demonstrate by using simulation experiments that process‐based finite population sampling models improve model fit and inference over models that fail to account for spatial correlation. Furthermore, the process‐based models offer richer inference with spatially interpolated maps over the entire region. We reinforce these improvements and demonstrate scalable inference for groundwater nitrate levels in the population of California Central Valley wells by offering estimates of mean nitrate levels and their spatially interpolated maps. 相似文献
86.
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 inferential framework to characterize heatwave and come up with a definition that can capture the essence of all existing ad hoc definitions. We derive an exact distribution on the frequency of such durations for a stationary Markov process and also an approximate distribution of durations for a stationary non‐Markov time series. For a given site, using a daily time series (of ambient temperature or heat‐index), we define a heatwave as the number of sustained days above a given threshold using the probability distribution of the durations. We illustrate the proposed methodology using daily time series of ambient temperature for a fixed site (of Atlanta) and also using the USCRN consisting of 126 sites across the United States. Furthermore, we also derive an empirical quadratic curve based relationship between expected durations and extreme thresholds. The proofs of the theorems, datasets, algorithms, and computer codes are provided in the supplementary materials. 相似文献
87.
C. J. Wilkie C. A. Miller E. M. Scott R. A. O'Donnell P. D. Hunter E. Spyrakos A. N. Tyler 《Environmetrics》2019,30(3)
Statistical downscaling has been developed for the fusion of data of different spatial support. However, environmental data often have different temporal support, which must also be accounted for. This paper presents a novel method of nonparametric statistical downscaling, which enables the fusion of data of different spatiotemporal support through treating the data at each location as observations of smooth functions over time. This is incorporated within a Bayesian hierarchical model with smoothly spatially varying coefficients, which provides predictions at any location or time, with associated estimates of uncertainty. The method is motivated by an application for the fusion of in situ and satellite remote sensing log(chlorophyll‐a) data from Lake Balaton, in order to improve the understanding of water quality patterns over space and time. 相似文献
88.
The aim of the study is an uncertainty analysis of an air dispersion model. The model used is described in NRPB‐R91 (Clarke, 1979), a model for short and medium range dispersion of radionuclides released into the atmosphere. Uncertainties in the model predictions arise both from the uncertainty of the input variables and the model simplifications, resulting in parameter uncertainty. The uncertainty of the predictions is well described by the credibility intervals of the predictions (prediction limits), which in turn are derived from the distribution of the predictions. The methodology for estimating this distribution consists of running multiple simulations of the model for discrete values of input parameters following some assumed random distributions. The value of the prediction limits lies in their objectivity. However, they depend on the assumed input distributions and their ranges (as do the model results). Hence the choice of distributions is very important for the reliability of the uncertainty analysis. In this work, the choice of input distributions is analysed from the point of view of the reliability of the predictive uncertainty of the model. An analysis of the influence of different assumptions regarding model input parameters is performed. Of the parameters investigated (i.e. roughness length, release height, wind fluctuation coefficient and wind speed), the model showed the greatest sensitivity to wind speed values. A major influence on the results of the stability condition specification is also demonstrated. Copyright © 2000 John Wiley & Sons, Ltd. 相似文献
89.
Precipitation over the Western part of Iberian Peninsula is known to be related to the large‐scale sea level pressure field and thus to advection of humidity into this area. The major problem is to downscale this synoptic atmospheric information to local daily precipitation patterns. One way to handle this problem is by weather‐state models, where, based on the pressure field, each day is classified into a weather state and precipitation is then modeled within each weather state via multivariate distributions. In this paper, we propose a spatiotemporal Bayesian hierarchical model for precipitation. Basic objective and novelty of the paper is to capture and model the essential spatiotemporal relationships that exist between large‐scale sea level pressure field and local daily precipitation. A specific local spatial ordering that mimics the essential large‐scale patterns is used in the likelihood. The model is then applied to a network of rain gauge stations in the river Tagus valley. The inference is then carried out using appropriate MCMC methods. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
90.
Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand (COD) reduction was the response variable of interest. Via the proposed approach, the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen (TN)-rich wastewater, and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving. 相似文献