Bayesian modelling of extreme wind speed at Cape Town,South Africa

In the framework of generalized extreme value (GEV) distribution, the frequentist and Bayesian methods have been used to analyse the extremes of annual maxima wind speed recorded by automatic weather stations in Cape Town, Western Cape, South Africa. In the frequentist approach, the GEV distribution parameters were estimated using maximum likelihood, whereas in the Bayesian method the Markov Chain Monte Carlo technique with the Metropolis–Hastings algorithm was used. The results show that the GEV model with trend in the location parameter appears to be a better model for annual maxima data. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from other weather stations. The results from the Bayesian analysis show that posterior inference might be affected by the choice of priors and hence by the distance between a weather station used to formulate the priors and the point of interest.     

Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields

We discuss an approach for the statistical modelling of extreme precipitation events in South-West Australia over space and time, using a latent spatiotemporal process where precipitation maxima follow a generalised extreme value distribution. Temporal features are captured by modelling trends on the location and scale parameters. Spatial features are captured using anisotropic Gaussian random fields. Site specific explanatory variables are also incorporated. We fit several models using Bayesian inferential methods to precipitation extremes recorded at 36 weather stations around the Western Australian state capital city of Perth over the period 1907–2009. Model choice is performed using the DIC criterion. The best fitting model shows significant non-stationarity over time, with extreme precipitation events becoming less frequent. Extreme precipitation events are stronger at coastal locations, with the intensity decreasing as we head to the higher and drier areas to the North-East.     

Sea level extremes in the U.S.-Affiliated Pacific Islands—a coastal hazard scenario to aid in decision analyses

The objective of this study is to provide a perspective on the extremes of sea-level variability and predictability for the U.S.-Affiliated Pacific Islands (USAPI) on seasonal time-scales. Based on the Generalized Extreme Value (GEV) model, the L-moments method has been used to estimate the model parameters. The bootstrap method has been used to define the exceedance probability level of upper and lower bounds of the return periods at the 90% confidence interval. On the basis of these return calculations and expected extremes of high sea level, the seasonal maxima of sea level and the varying likelihood of extreme events have been estimated. For analyzing the predictability of the extremes of sea-level, a canonical correlation analysis (CCA) statistical model has been developed. Findings reveal that there is seasonal climatology of extreme events in the vicinity of USAPI that are variable on temporal and spatial scales. Some of the islands (Yap and Saipan) display considerably higher seasonal extremes than the others for 20 to 100 year return periods because of typhoon-related storm surges. These surges are likely to cause huge tidal large sea-level inundations and increased erosion to low-lying atolls/islands and result in considerable damage to roads, harbors, unstable sandy beaches, and other major infrastructures. Finally, the need for evaluating the extreme events and associated typhoons from a regional perspective has been stressed for coastal hazard management decision analyses in the USAPI.     

Modeling the annual thermal regime of lake Ohrid,Yugoslavia, using daily weather data

The time-dependent characteristics of the five weather variables which control the annual thermal response of Lake Ohrid are analyzed in detail. These are daily values for solar radiation, air temperature, humidity, wind speed and cloud cover. A simple numerical model of the lake's thermal response, forced by thermally driven density mixing, is developed and tested using observed thermal profiles for verification. The numerical model successfully reproduces the major features of the lake's thermal regime over a 6 y period from 1972 to 1977, the average root mean square value for the simulated profiles being 1.2°C with extremes of 2.2 and 0.3°C and a standard deviation of 0.4°C.     

A stochastic weather generation model for Alaska

A specific problem encountered in ecosystem-level simulation of Arctic ecosystems is the depth and extent of the driving variable record. Often, climate records are of short duration, gathered at locations different from the area to be simulated, or do not contain all the variables required by a given model. This paper addresses this problem for ecosystem simulation in Alaska with the development of a weather generator. The generator, called WGENAL, is based on the WGEN climate generator developed and validated in the 48 conterminous states. Because of the extreme variability of weather in Alaska that is not accommodated by the statistical metrics in the earlier model, a new climate generator was developed. WGENAL generates daily values of precipitation, maximum temperature, minimum temperature, solar radiation, and wind run. Precipitation is generated using a Markov chain-gamma model. A two-parameter gamma distribution is used to generate wind run. Temperatures and solar radiation are generated using procedures developed in the earlier study. Validation of the generator shows it provides adequate diurnal and seasonal weather records for Fairbanks. Other comparisons of synthetic weather with observed weather for sites north of the Brooks Range in Alaska are also within the error of the original data.     

Economic impacts of climate change on agriculture: The importance of additional climatic variables other than temperature and precipitation

Climate change shifts the distributions of a set of climatic variables, including temperature, precipitation, humidity, wind speed, sunshine duration, and evaporation. This paper explores the importance of those additional climatic variables other than temperature and precipitation. Using the county-level agricultural data from 1980 to 2010 in China, we find that those additional climatic variables, especially humidity and wind speed, are critical for crop growth. Therefore, omitting those variables is likely to bias the predicted impacts of climate change on crop yields. In particular, omitting humidity tends to overpredict the cost of climate change on crop yields, while ignoring wind speed is likely to underpredict the effect. Our preferred specification indicates that climate change is likely to decrease the yields of rice, wheat, and corn in China by 36.25%, 18.26%, and 45.10%, respectively, by the end of this century.     

近60年新疆库尔勒市的气候变化特点分析

根据库尔勒市气象局和研究区安装的自动气象站的气象资料,采用滑动平均、距平分析、线性回归等方法,分析库尔勒市近60年的气候变化的基本特征,结果表明：（1）库尔勒市平均温度有4暖3冷的冷暖变化阶段,该地区平均气温呈上升趋势,特别是20世纪90年代以后气温明显增加;近10年的气温变化较大,比前50年的平均值上升了1℃;平均气温存在着明显的线性升温趋势。（2）库尔勒市年降水量变化趋势不是很明显,有2多4少的干湿变化阶段。近10年的降水量与前50年的降水量相比较偏少了2．4mm;年降水量不存在明显的线性变化趋势。（3）库尔勒市平均极端最高最低气温呈上升趋势,60年来该地区最高气温上升率与南疆最高气温上升率一致。近10年的极端最高最低气温与前50年的分别上升0．36℃和1．2℃,最低气温的上升对年平均气温的升高贡献最大。（4）通过比较库尔勒市气象站提供的气象数据和自动气象站提供的数据可以看出,该区域一定程度上受到城市热岛效应和人为干扰,因此需要在城市周边的某些地区安装气象仪器,以提高气象数据的精确性。     

Quantifying parameter uncertainty in a coral reef model using Metropolis-Coupled Markov Chain Monte Carlo

Coral reefs are threatened ecosystems, so it is important to have predictive models of their dynamics. Most current models of coral reefs fall into two categories. The first is simple heuristic models which provide an abstract understanding of the possible behaviour of reefs in general, but do not describe real reefs. The second is complex simulations whose parameters are obtained from a range of sources such as literature estimates. We cannot estimate the parameters of these models from a single data set, and we have little idea of the uncertainty in their predictions.We have developed a compromise between these two extremes, which is complex enough to describe real reef data, but simple enough that we can estimate parameters for a specific reef from a time series. In previous work, we fitted this model to a long-term data set from Heron Island, Australia, using maximum likelihood methods. To evaluate predictions from this model, we need estimates of the uncertainty in our parameters. Here, we obtain such estimates using Bayesian Metropolis-Coupled Markov Chain Monte Carlo. We do this for versions of the model in which corals are aggregated into a single state variable (the three-state model), and in which corals are separated into four state variables (the six-state model), in order to determine the appropriate level of aggregation. We also estimate the posterior distribution of predicted trajectories in each case.In both cases, the fitted trajectories were close to the observed data, but we had doubts about the biological plausibility of some parameter estimates. We suggest that informative prior distributions incorporating expert knowledge may resolve this problem. In the six-state model, the posterior distribution of state frequencies after 40 years contained two divergent community types, one dominated by free space and soft corals, and one dominated by acroporid, pocilloporid, and massive corals. The three-state model predicts only a single community type. We conclude that the three-state model hides too much biological heterogeneity, but we need more data if we are to obtain reliable predictions from the six-state model. It is likely that there will be similarly large, but currently unevaluated, uncertainty in the predictions of other coral reef models, many of which are much more complex and harder to fit to real data.     

Bayesian spatial prediction

This paper presents a complete Bayesian methodology for analyzing spatial data, one which employs proper priors and features diagnostic methods in the Bayesian spatial setting. The spatial covariance structure is modeled using a rich class of covariance functions for Gaussian random fields. A general class of priors for trend, scale, and structural covariance parameters is considered. In particular, we obtain analytic results that allow easy computation of the predictive distribution for an arbitrary prior on the parameters of the covariance function using importance sampling. The computations, as well as model diagnostics and sensitivity analysis, are illustrated with a set of precipitation data.     

河西走廊极端干旱区PM10质量浓度分布特征及其与气象要素关系研究

利用敦煌和酒泉2007—2011年的PM10质量浓度资料和风速、气温、相对湿度、气压、天气现象等相关气象要素资料,分析了河西走廊西部极端干旱区不同下垫面环境PM。0质量浓度的时空分布特征,结果表明,下垫面是沙地环境的敦煌PMl0质量浓度年平均值为128．9lμg·m-1,明显高于绿洲环境酒泉的76．1mg·m-1两站均是春季大于其他季节,尤以4月最为显著,敦煌和酒泉分别达到272．1lμg·m0和151lμg·m-2;PMl0质量浓度的不同分布特征与气象因素有密切的关系,尤其受沙尘天气的影响较大,其最大值可以反映沙尘天气的强度,非沙尘日PMl0质量浓度在不同下垫面条件下虽有一定相差,但空气质量状况均在“良”以上。两站PM10质量浓度日变化差异较大,敦煌四季的日变化特征均不特别显著,变化比较平稳,基本都呈单峰单谷型分布,最大值出现在17：00时左右,最小值出现在6：00左右;酒泉春、秋季日变化基本一致,呈单峰型,最大值出现在正午时段;夏季日变化规律性不明显,变化幅度比较平缓;冬季呈双峰双谷型,最大值和次大值分别出现在16：00和2：00左右,最小值和次小值分别出现在10：00和0：00左右。进一步分析发现,在沙尘日和非沙尘日PM10质量浓度明显不同,其对应的压、温、湿、风及能见度也有一定规律,沙尘日的日均风速和日最大风速大于非沙尘日,相对湿度、气压和能见度小于非沙尘日。两站的气温、气压、相对湿度、风速等气象要素与PM10质量浓度均有一定相关性,但PM10质量浓度的分布最终是受各要素综合影响的结果,敦煌和酒泉,PM值与PM10质量浓度日均值的相关性都很显著,相关系数分别为0．8961和0．9152,远高于其他各单气象要素与PM10质量浓度的相关性。两站沙尘日的昂M均值分别是非沙尘日2-3倍,因此气象影响指数能有效的区别沙尘日和非沙尘日。IPM的分布也能较好的反映PMl0质量浓度的分布,因此可用抽d来量化评价PM10质量浓度。     

云贵高原1961-2006年大气能见度和消光因素变化趋势及原因

根据云贵高原203个气象台站1961—2006年大气能见度、降水、相对湿度、风速和天气现象等观测资料,采用倾向率方法对能见度和大气消光系数的变化趋势进行了分析。还应用Mann-Kendall方法对19km能见度、霾日数和消光系数的多年变化进行了气候突变检验。结果表明,有84.2%台站出现了能见度减少趋势。减少最多为-11km·10a-1,最少为-1km·10a-1。减少的平均气候倾向率在1961—1979年为0.96km·10a-1,1980—2006年为1.6km·10a-1,高原平均能见度从60年代的约34km下降到目前的约27km。另一方面,有15.8%台站能见度有增加趋势,且多集中在人类活动较为稀少的高海拔山区。有71%的台站19km能见度频率出现减少的趋势,平均倾向率为-2%·10a-1,主要出现在高原东部和中部人口和工业稠密区。该地区同时也出现霾日增加的现象。Mann-Kendall检测结果表明,19km能见度频率减少和霾日数增加现象出现突变的时间相同。年平均消光系数发生突变的时间稍推后。认为能见度下降、消光因素增加的原因与人为排放污染物浓度增加有密切关系。     

Geoadditive regression modeling of stream biological condition

Indices of biotic integrity have become an established tool to quantify the condition of small non-tidal streams and their watersheds. To investigate the effects of watershed characteristics on stream biological condition, we present a new technique for regressing IBIs on watershed-specific explanatory variables. Since IBIs are typically evaluated on an ordinal scale, our method is based on the proportional odds model for ordinal outcomes. To avoid overfitting, we do not use classical maximum likelihood estimation but a component-wise functional gradient boosting approach. Because component-wise gradient boosting has an intrinsic mechanism for variable selection and model choice, determinants of biotic integrity can be identified. In addition, the method offers a relatively simple way to account for spatial correlation in ecological data. An analysis of the Maryland Biological Streams Survey shows that nonlinear effects of predictor variables on stream condition can be quantified while, in addition, accurate predictions of biological condition at unsurveyed locations are obtained.     

Hierarchical modeling of count data with application to nuclear fall-out

We propose a method for a Bayesian hierarchical analysis of count data that are observed at irregular locations in a bounded domain of R². We model the data as having been observed on a fine regular lattice, where we do not have observations at all the sites. The counts are assumed to be independent Poisson random variables whose means are given by a log Gaussian process. In this article, the Gaussian process is assumed to be either a Markov random field (MRF) or a geostatistical model, and we compare the two models on an environmental data set. To make the comparison, we calibrate priors for the parameters in the geostatistical model to priors for the parameters in the MRF. The calibration is obtained empirically. The main goal is to predict the hidden Poisson-mean process at all sites on the lattice, given the spatially irregular count data; to do this we use an efficient MCMC. The spatial Bayesian methods are illustrated on radioactivity counts analyzed by Diggle et al. (1998).     

An application of Bayesian variable selection to spatial concurrent linear models

Spatial concurrent linear models, in which the model coefficients are spatial processes varying at a local level, are flexible and useful tools for analyzing spatial data. One approach places stationary Gaussian process priors on the spatial processes, but in applications the data may display strong nonstationary patterns. In this article, we propose a Bayesian variable selection approach based on wavelet tools to address this problem. The proposed approach does not involve any stationarity assumptions on the priors, and instead we impose a mixture prior directly on each wavelet coefficient. We introduce an option to control the priors such that high resolution coefficients are more likely to be zero. Computationally efficient MCMC procedures are provided to address posterior sampling, and uncertainty in the estimation is assessed through posterior means and standard deviations. Examples based on simulated data demonstrate the estimation accuracy and advantages of the proposed method. We also illustrate the performance of the proposed method for real data obtained through remote sensing.     

Variability and predictability of sea-level extremes in the Hawaiian and U.S.-Trust Islands—a knowledge base for coastal hazards management

The objective of this study is to provide an improved climatology of sea level extremes on seasonal and long-term time scales for Hawaii and the U.S-Trust islands. Observations revealed that the Hawaiian and U.S.-Trust islands, by and large, display a strong annual cycle. For estimating the statistics of return period, the three-parameter generalized extreme value (GEV) distribution is fitted using the method of L-moments. In the context of extremes (20- to 100-year return periods), the deviations in most of the Hawaiian Islands (except at Nawiliwili and Hilo) displayed a moderate sea-level rise (i.e., close to 200 mm), but the deviations in the U.S.-Trust islands displayed a considerably higher rise (i.e., more than 300 mm) in some seasons due to typhoon-related storm surges. This rise may cause damage to roads, harbors, and unstable sandy beaches. Correlations between the El Niño-Southern Oscillation (ENSO) climate cycle and the variability of seasonal sea level have been investigated. Results show that correlation for the station located west of the International Date Line (DL) is strong, but it is moderate or even weaker for stations east of the DL. The skill of SST-based Canonical Correlation Analyses (CCA) forecasts was found to be weak to moderate (0.4–0.6 for Honolulu, Kahului, Hilo, and Wake, and 0.3 or below for Kahului, Mokuoloe, and Johnston). Finally, these findings are synthesized for evaluating the potential implications of sea level variability in these islands.     

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.     

天津城市化对市区气候环境的影响

利用天津市区和郊区两个气象站近50年的地面温度、辐射、日照、风速、相对湿度和降水量资料,系统地分析了天津城市化进程对城市气候的影响,结果表明影响是显著的。受城市化的影响,天津市区地面温度远高于郊区,城市热岛效应明显,市区增温率达0．42℃·（10a）^-1,50年增温幅度达2．5℃,其地面总辐射、直接辐射和日照时数逐年减少的特征也进一步证明了城市化对大气的增温效应;市区近地层风速、相对湿度明显小于郊区,其中,平均风速以0．35m·s^-1·（10a）^-1的速率减小,相对湿度以1．11％·（10a）^-1的速率减小。近50年来天津市区的降水量和降水日数均呈现明显的减少趋势,降水量的倾向率为5．91mm·（10a）^-1,降水13数的倾向率为3．2d·（10a）^-1。     

On the use of correlated beta random variables with animal population modelling

We give reasons why demographic parameters such as survival and reproduction rates are often modelled well in stochastic population simulation using beta distributions. In practice, it is frequently expected that these parameters will be correlated, for example with survival rates for all age classes tending to be high or low in the same year. We therefore discuss a method for producing correlated beta random variables by transforming correlated normal random variables, and show how it can be applied in practice by means of a simple example. We also note how the same approach can be used to produce correlated uniform, triangular, and exponential random variables.     

蓝藻水华强度的显著相关环境因素识别模型

为识别蓝藻水华强度的显著相关环境因素,克服现有研究中因变量选择不合理、时间与空间精度较低等问题,构建了以蓝藻水华强度等级为因变量,以水质、水文和气象3类监测指标为自变量的多元线性回归模型,并将该模型应用于太湖蓝藻水华研究.基于水华面积和集聚强度数据,用7级量表生成水华强度等级值,使因变量具有宏观性,避免了仅使用叶绿素a浓度等类似指标表示水华强度所体现出的微观性不足.该数据集的时间精度达到每天采样2次,空间精度则达到太湖湖湾内的某个水域空间范围.因此因变量具有适度宏观性,而自变量的值则与因变量的值在较高的时间和空间精度基础上严格对应.模型的分析结果显示,太湖大贡山水域蓝藻水华强度与气温和硝酸盐浓度呈显著正相关,与风速、湿度和电导率呈显著负相关.上述结论与该研究领域的主流结论一致,验证了该模型的有效性.     

Ranked set sample design for environmental investigations

Ranked set sampling can provide an efficient basis for estimating parameters of environmental variables, particularly when sampling costs are intrinsically high. Various ranked set estimators are considered for the population mean and contrasted in terms of their efficiencies and useful- ness, with special concern for sample design considerations. Specifically, we consider the effects of the form of the underlying random variable, optimisation of efficiency and how to allocate sampling effort for best effect (e.g. one large sample or several smaller ones of the same total size). The various prospects are explored for two important positively skew random variables (lognormal and extreme value) and explicit results are given for these cases. Whilst it turns out that the best approach is to use the largest possible single sample and the optimal ranked set best linear estimator (ranked set BLUE), we find some interesting qualitatively different conclusions for the two skew distributions