Intervariable Correlations Research Articles

Genç Yetişkinlerde Stres ve Öznel İyi Oluş Arasındaki İlişkide Benlik Belirginliğinin Aracı Rolü

Bu araştırmanın amacı, (a) üniversite öğrencilerinde algılanan stres, öznel iyi oluş ve benlik belirginliği arasındaki ilişkileri ve (b) benlik belirginliğinin algılanan stres ve öznel iyi oluş (olumlu duygu, olumsuz duygu ve yaşam doyumu) arasında aracı rolü olup olmadığını incelemektir. Araştırmanın çalışma grubu, bir devlet üniversitesinin farklı fakültelerinde öğrenim gören 281 kadın (%58.7) ve 198 erkek (%41.3) olmak üzere toplam 479 üniversite öğrencisinden oluşmaktadır. Araştırmada kolay ulaşılabilir örnekleme yöntemi kullanılmıştır. Öncelikle bütün değişkenlere ilişkin betimsel istatistikler hesaplanmış, değişkenler arası ilişkiler Pearson Çarpım Momentler Korelasyon analizi kullanılarak belirlenmiştir. Aracılık analizleri Preacher ve Hayes’in önerileri doğrultusunda gerçekleştirilmiştir. Dolaylı etkilerin anlamlılığını incelemek için hem Sobel Z, hem de Preacher ve Hayes’in bootstrapping prosedürü kullanılmıştır. Bu araştırmada 10000 bootstrap yapılarak bootstrap katsayısı ve güven aralıkları belirlenmiştir. Elde edilen bulgulara göre; algılanan stres ile öznel iyi oluş, olumlu duygu, olumsuz duygu ve yaşam doyumu arasında benlik belirginliğinin kısmi aracı rolü olduğu saptanmıştır. Benlik belirginliği, algılanan stresin öznel iyi oluş, olumlu duygu, olumsuz duygu ve yaşam doyumu üzerindeki etkisinin düşmesinde anlamlı bir etkiye sahiptir.

P274 Association of self-reported symptoms and behaviour in axSpA: initial analyses from the Project Nightingale study

Abstract Background Axial spondyloarthritis (axSpA) is a chronic inflammatory disease primarily affecting the spine and sacroiliac joints, characterised by fluctuating periods of flare and remission. Management is based on patient-reported symptoms and outcome measures collected at follow-up appointments which may be subject to recall bias. Smartphone technologies for monitoring disease symptoms provide an opportunity to gain a more complete understanding of disease burden and symptom patterns and may facilitate optimisation and personalisation of axSpA management. Methods Patients with axSpA attending the Royal National Hospital for Rheumatic Diseases in Bath were invited to participate in the study. Through the uMotif symptom tracking app, patients were sent daily reminders to log pain, fatigue, sleep, recommended exercise, mood and stress using 5-point Likert scales (higher scores indicating better outcomes eg. No pain=5), in addition to optional variables such as screen time, caffeine intake and menstrual cycle. For each patient, at each time-point, Spearman rank correlation coefficients were used to evaluate inter-variable correlations. Lagged variables were used to assess correlations with variables from the previous day (variable-1). Results Between 05/04/18 and 08/03/19, 174 patients consented for research and logged a mean of 99.73 (SD = 99.97) days of data (range=1 - 323 days). Significant correlations were identified between uMotif variables (Table 1), including pain and fatigue; mood and stress; hot flushes and fatigue/pain; confidence in self-management and mood/pain; smoking and pain/fatigue; exercise and sleep; exercise and mood - supporting existing evidence regarding exercise implementation in axSpA. Conclusion These findings demonstrate relationships between a variety of patient-reported symptoms, including variables that to our knowledge, have not yet been explored in axSpA. In future research, it will be important to determine whether there is a chronological pattern within an individual or combination of variables that could predict a flare. Greater understanding of axSpA disease patterns and identification of the optimal timing of intervention to ameliorate these symptoms may ultimately reduce flare frequency, duration and intensity, and greatly improve quality of life for patients with axSpA. Disclosures R. Barnett None. S. Ng None. S.L. Jones None. M. Young None. R. Sengupta None.

A Stochastic Unified Convection Scheme (UNICON). Part I: Formulation and Single-Column Simulation for Shallow Convection

AbstractBy extending the previously developed unified convection scheme (UNICON), we develop a stochastic UNICON with convective updraft plumes at the surface randomly sampled from the correlated multivariate Gaussian distribution for updraft vertical velocity w^ and thermodynamic scalars ϕ^, of which standard deviations and intervariable correlations are derived from the surface-layer similarity theory. The updraft plume radius R^ at the surface follows a power-law distribution with a specified scale break radius. To enhance computational efficiency, we also develop a hybrid stochastic UNICON consisting of n bin plumes and a single stochastic plume, each of which mainly controls the ensemble mean and variance of grid-mean convective tendency, respectively. We evaluated the stochastic UNICON using the large-eddy simulation (LES) of the Barbados Oceanographic and Meteorological Experiment (BOMEX) shallow convection case in a single-column mode. Consistent with the assumptions in the stochastic UNICON, the LES w^ and ϕ^ at the surface follow approximately the half- and full-Gaussian distributions, respectively. LES showed that a substantial portion of the variability in ϕ^ at the cloud base stems from the surface, which also supports the concept of stochastic UNICON that simulates various types of moist convection based on the dry stochastic convection launched from the surface. Overall, stochastic UNICON adequately reproduces the LES grid-mean thermodynamic states as well as the mean and variance of ϕ^, including their dependency on the domain size and R^. A sensitivity test showed that the perturbations of ϕ^ as well as R^ at the surface are important for the correct simulation of the grid-mean thermodynamic states.

A Two-Stage Multisite and Multivariate Weather Generator

The spatial structure of climatic variables synthesized by a weather generator has considerable impact on the modeling of hydrological variability; however, in most cases, it needs computationally intensive work to reproduce multisite and/or multivariate correlations. This work proposed a two-stage weather generator (TSWG) to preserve intersite and intervariable correlations of daily precipitation, maximum and minimum temperatures. The first stage generates daily precipitation and temperature for each site and for each variable with, but not limited to, the Richardson-type approach. The second stage rebuilds the multisite multivariate correlation using a distribution-free shuffle procedure. The TSWG was applied to a network of 15 stations in the Jing River catchment (Northwest China). It reproduced the statistical parameters and multisite and multivariate correlations well. Furthermore, indirect validation by hydrological modeling showed TSWG outputs could be used satisfactorily for simulating streamflow variability. As a distribution-free method, the correlation reconstruction method can be applied to variables with different probability distributions. The TSWG can efficiently reconstruct the correlation with one optimization for all stations and all variables, which is superior to most current methods operated once for one station pair and one variable. The TSWG provides an option for improved multisite and multivariate weather generation.

Bitcoin Poison? Anecdotal Evidence from Bitcoin Miners Revenue

This paper explores the predictive qualities of Bitcoin Miners Revenue on Bitcoin Returns. Using data on Bitcoin in the cryptocurrency market from July 1, 2010 to February 20, 2018, we reflect intervariable correlations not previously examined. We analyze those relationships with a conditional regression analysis adjusting for calendar effects. We separate the sample, and use the last 17 trading days (month) to test a strategy based on the probability of Bitcoin Returns moving higher. After a slight modification to the logistic regression analysis, we find a profitable trading strategy exists based solely on Bitcoin Miners Revenue and the probability of Bitcoin Returns moving higher.

Soil moisture retention and mass movement of volcanic soils from the “Sabinas” sector in Caldas, Colombia

The mass movement of soils, are soil, rocks displacements or both, caused by soil water excesses in terrains due to gravity effects and other factors. The aim of this research was to quantify the moisture retention capacity of volcanic soils as a indicator in the mass removal phenomena. This research was carried out on soils of the Malteria - Las Margaritas road transept to Magdalena river, right bank of the Chinchina river in the Department of Caldas, Colombia. Through soil sample design, 3 ranges were stratified as follows: high, medium and low. In fact, representative was taken in the study area, sampling the combination of the following variables: plant cover and altitudinal range. In total, 52 points were sampled and georreferenced. High moisture retention was confirmed with saturation levels at different stresses as fundamental triggers of the phenomenon as a consequence of the soil aggregates instability to soil moisture, high porosity and high hydraulic conductivity and its relation with the mineralogy of these soils, high rainfall regimes of the region, position, terrain slopes, which determine the relative by mass movement of soils. Intervariable correlations were found to facilitate the explanation of the mass removal of soil phenomena in the study area, including some of a significant order referring to the association between humidity threat and soil organic matter variables for the analyzed covers.

Multisite and multivariate GCM downscaling using a distribution-free shuffle procedure for correlation reconstruction

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 72:141-151 (2017) - DOI: https://doi.org/10.3354/cr01460 Multisite and multivariate GCM downscaling using a distribution-free shuffle procedure for correlation reconstruction Zhi Li*, Xiaoping Shi, Jingjing Li State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, PR China *Corresponding author: lizhibox@126.com ABSTRACT: Reconstructing the multisite and multivariate correlations of meteorological variables during downscaling of general circulation model (GCM) outputs is extremely important for hydrological variability simulation. To this end, this study proposes a 2-step approach to downscale GCMs considering the correlations between sites and variables. The first step generates single-site climate change scenarios for daily precipitation and maximum and minimum temperature. This step is open to any downscaling technique, since the subsequent correlation reconstruction does not change the statistical parameters. This study employed a quantile mapping method for spatial downscaling and the Richardson-type weather generator for temporal downscaling. The second step re-correlates the generated daily and site-based climate change scenarios by a distribution-free shuffling procedure. We used precipitation and maximum and minimum temperature data from a network of 15 weather stations and 1 GCM to develop and evaluate the method. Results showed that the proposed method satisfactorily preserved the downscaled statistical parameters and intersite and intervariable correlations. By simultaneously shuffling the data for all stations and all 3 variables for each month, the proposed approach is easy to implement, as well as being computation-saving, and provides a rational option for multisite and multivariate GCM downscaling. KEY WORDS: GCM downscaling · Spatial structure · Temporal pattern · Weather generator Full text in pdf format PreviousNextCite this article as: Li Z, Shi X, Li J (2017) Multisite and multivariate GCM downscaling using a distribution-free shuffle procedure for correlation reconstruction. Clim Res 72:141-151. https://doi.org/10.3354/cr01460 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 72, No. 2. Online publication date: May 18, 2017 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2017 Inter-Research.

Evaluation of bias correction methods for wave modeling output

Models that seek to predict environmental variables invariably demonstrate bias when compared to observations. Bias correction (BC) techniques are common in the climate and hydrological modeling communities, but have seen fewer applications to the field of wave modeling. In particular there has been no investigation as to which BC methodology performs best for wave modeling. This paper introduces and compares a subset of BC methods with the goal of clarifying a “best practice” methodology for application of BC in studies of wave-related processes. Specific focus is paid to comparing parametric vs. empirical methods as well as univariate vs. bivariate methods. The techniques are tested on global WAVEWATCH III historic and future period datasets with comparison to buoy observations at multiple locations. Both wave height and period are considered in order to investigate BC effects on inter-variable correlation. Results show that all methods perform uniformly in terms of correcting statistical moments for individual variables with the exception of a copula based method underperforming for wave period. When comparing parametric and empirical methods, no difference is found. Between bivariate and univariate methods, results show that bivariate methods greatly improve inter-variable correlations. Of the bivariate methods tested the copula based method is found to be not as effective at correcting correlation while a “shuffling” method is unable to handle changes in correlation from historic to future periods. In summary, this study demonstrates that BC methods are effective when applied to wave model data and that it is essential to employ methods that consider dependence between variables.

Failures of explaining away and screening off in described versus experienced causal learning scenarios.

Causal Bayes nets capture many aspects of causal thinking that set them apart from purely associative reasoning. However, some central properties of this normative theory routinely violated. In tasks requiring an understanding of explaining away and screening off, subjects often deviate from these principles and manifest the operation of an associative bias that we refer to as the rich-get-richer principle. This research focuses on these two failures comparing tasks in which causal scenarios are merely described (via verbal statements of the causal relations) versus experienced (via samples of data that manifest the intervariable correlations implied by the causal relations). Our key finding is that we obtained stronger deviations from normative predictions in the described conditions that highlight the instructed causal model compared to those that presented data. This counterintuitive finding indicate that a theory of causal reasoning and learning needs to integrate normative principles with biases people hold about causal relations.

The structure of obsessive compulsive symptoms and beliefs: A correspondence and biplot analysis

Research has suggested that obsessive-compulsive (OC) beliefs are differentially predictive of OC symptom dimensions. One additional way in which beliefs and symptoms may be related is by severity; that is, the role of beliefs may vary as a function of symptom severity. In order to evaluate this possibility, correspondence analysis with biplot was applied to evaluate the association between OC beliefs and OC symptom severity across three subsamples, individuals with obsessive-compulsive disorder (OCD; N=398), individuals with anxiety disorders (N=104), and undergraduate students (N=285). To do so, we generated five row categories of symptom severity and six columns based on the Obsessive Compulsive Beliefs Questionnaire (OBQ) for three subsamples. Unlike factor analyses of inter-variable correlations (or covariances), the CA-biplot paradigm calibrates simultaneously row and column information and estimates dimensional coordinates (analogous to factor loadings) separately for rows and columns. We used the first two dimensions from each subsample because they accounted for most variance (on average 89%) so as to construct a hypothetical plane with them. Then, we visually inspected associations among five severity categories (rows) and six OBQ subscales (columns) in the plane and also calculated their correlations. The visual configurations and numerical correlations were consistent across three subsamples, indicating that inflated responsibility was significantly associated with OC symptoms, but only for those with the least severe symptoms. Importance and control of thoughts were associated with OC symptoms across all levels of symptom severity. The implications of these findings for the cognitive model of OCD are considered.

Dependence-Preserving Approach to Synthesizing Household Characteristics

One effective approach to study day-to-day traveler behavior is through the activity-based traffic demand model, in which all travelers are treated as individual agents and interact under a computation-intensive framework. Nevertheless, because of high survey costs, low response rate, and privacy concerns, detailed household and personal characteristics are usually unavailable. Various population synthesizers were therefore proposed to reconstruct a methodologically rigorous estimate of household characteristics from different surveys. For instance, the iterative proportional fitting (IPF) algorithm is used to synthesize the full population from the public use microdata sample (PUMS) and Census Summary File 3 (SF3) in the popular activity-based traffic demand model, TRANSIMS. However, some fundamental limitations of IPF (e.g., zero cells in the contingency table as a result of small sample size) have drawn sufficient attention and resulted in the development of enhanced IPF algorithms and other strategies. This paper proposes a copula-based method to synthesize household characteristics that preserves marginal distributions and dependence structure between variables. The proposed method is tested for the state of Iowa, and the results are compared with the IPF approach of TRANSIMS. The synthesized households resulted in the same local SF3 statistics at each block group. But having similar inter-variable correlations as described in the PUMS suggest the applicability of the copula-based approach. Because marginal distributions and dependence structure can be faithfully preserved, the proposed method could be a suitable alternative to synthesize realistic agent characteristics for further activity-based traffic demand modeling.

P03-362 - Experienced stress, self-efficacy, self-esteem and strategies of coping with stress and their association with clinical symptoms in schizophrenia

IntroductionStress affects the development/course of schizophrenia. Inefficient coping may influence functioning.ObjectiveAssessment of experienced stress, self-efficacy, self-esteem and coping strategies in schizophrenia.MethodsStudy group: 33 schizophrenic in-patients and 27 healthy subjects. Scales: clinical symptoms - PANSS, HDRS; stress-related - experienced stress (SES), self-efficacy (GSES), self-esteem (RSES), coping strategies (COPE).ResultsPANSS, HDRS scores (mean ± SD): 49.2 ± 16.2, 10.1 ± 7.6. Table 1 shows SES, GSES, RSES scores, Table 2 - coping strategies (only significant differences). Inter-variable correlations were found: SES-PANSS (r = 0.56, p < 0.001), SES-HDRS (r = 0.69, p = 0.01), RSES-HDRS (r = -0.39, p = 0.02), COPE14-PANSS (r = 0.38, p = 0.03), COPE14-PANSS-P (r = 0.45, p = 0.008).[Stress, self-efficacy, self-esteem (mean score ± SD)][Coping strategies (mean score ± SD)]ConclusionsHigher experienced stress, lower self-efficacy and self-esteem were found in schizophrenic patients. Stress and coping strategies may affect/result from clinical symptoms. “Passive/avoiding” coping strategies were more frequent in schizophrenic patients.

Partial correlation metric based classifier for food product characterization

Data classification algorithms applied to multidimensional and multiclass food characterization problems mainly assume feature independency to quantify intra-class similarity or inter-class dissimilarities. As an alternative, possible class specific inter-relations among the feature vectors can be exploited for distinguishing samples into specific classes. Based on this idea, a new partial correlation coefficient metric (PCCM) based classification method is proposed. Existence of such inter-variable correlations as signatures of unique classes is established with illustrative problems. Categorized variable dependency structures are hypothesized as the basis for class discrimination. Two food quality analysis datasets with chemometrics importance are utilized as benchmark problems to compare the performance of new method with classification algorithms like LDA (linear discriminant analysis), CART, Treenet and SVM (support vector machines). The PCCM method is observed to perform well for different tests over large sets of classification experiments. Discriminating PCCM classifier also provides a quick visualization tool to diagnose complex classification problems.

Spatial‐scale partitioning of in situ turbulent flow data over a pebble cluster in a gravel‐bed river

Spatial structures within turbulent flow data were investigated through the use of a new multivariate variation partitioning analysis technique involving principal coordinates of neighbor matrices (PCNM), which is a form of distance‐based eigenvector maps (DBEM). The analysis revealed a significant (α = 0.01) spatial dependence, 58%, for the mean and turbulent flow variables. The flow variables were obtained from instantaneous two‐dimensional velocities collected in situ along a streamwise section that crosses over a pebble cluster in a gravel‐bed river. Using the orthogonal property of the PCNM variables, the explained variation was partitioned over four significant (α = 0.01) spatial scales: very large (VL, 17%), large (L, 24%), medium (M, 6%) and fine (F, 2%). Nearly 75% of the variance of the main turbulent flow indicators, such as the root‐mean‐square of the streamwise and vertical velocity components and the mean uv component Reynolds shear stress, was explained by the VL‐ and L‐scale PCNM submodels, which have streamwise and vertical length scales of the order of Δx = 5.3H − 2.6H and Δy = 1.0H − 0.5H (where H is the flow depth), respectively. Through a multivariate mapping procedure, clear spatial patterns within the explained flow variables emerge around the cluster, where the flow separation zone seems to play a significant role at a range of scales. As well, intervariable correlations at each spatial scale, obtained through eigenvector scatterplots, show intricate relationships between the flow variables. The interdependence of the Reynolds shear stress and the u component turbulent energy is much stronger at the VL scale than at the L and M scales. The application of PCNM analysis on the turbulent flow field shows the power of the technique to resolve the relevant spatial scales and patterns, and demonstrates its potential use in a variety of water resources studies.

Revisiting statistical–topographical methods for avalanche predetermination: Bayesian modelling for runout distance predictive distribution

Return period is a classical tool for avalanche hazard mapping but is often poorly defined. To reduce ambiguity, high quantiles of a given quantity should be preferred. Inspired by the statistical-topographical Norwegian approaches and concepts developed by Ancey and Meunier, this paper presents a new method for computing the predictive distribution of snow avalanche runout distances. We evaluate the uncertainties associated with design values using a very simple propagation operator and minimal statistical hypotheses. Only release and runout altitudes are necessary, allowing the model to work with the French historical avalanche database. We propose a stochastic model flexible enough to reasonably capture avalanche data variability and to express inter-variable correlations. The Bayesian framework facilitates parameter inference and allows taking estimation error into account for predictive simulations.

Development of the M-SACRAH, a modified, shortened version of SACRAH (Score for the Assessment and Quantification of Chronic Rheumatoid Affections of the Hands).

The Score for the Assessment and Quantification of Chronic Rheumatoid Affections of the Hands (SACRAH) is a self-administered questionnaire assessing functional status, stiffness and pain in patients suffering from hand osteoarthritis (HOA) as well as rheumatoid arthritis (RA) of the hand. It consists of 23 questions in three domains, to be answered on 100 mm visual analogue scales (VAS). Our goal was to shorten the original SACRAH by elimination of redundant questions in order to make it easier to use for patients and physicians. Following an arbitrary procedure employing high intervariable correlations, redundant questions were eventually eliminated. To validate the shortened version, 60 patients with HOA, recruited at four rheumatological centres in Austria, completed the original SACRAH as well as the shortened version. Fifty-five patients suffering from RA of the hands treated at the Second Department of Medicine, Humanis Klinikum Stockerau, also completed both questionnaires. A total of 11 questions (nine from the function domain and two from the pain domain) were eliminated, leading to the modified score consisting of 12 questions. Comparing the results of SACRAH and M-SACRAH, as well as the domain scores in individual patients, correlation coefficients were r = 0.978 for HOA patients (P < 0.0001) and r = 0.986 for RA patients (P < 0.0001). M-SACRAH, the shortened and simplified version of the original SACRAH questionnaire, proved to be as reliable and as representative as SACRAH for hand status in individual HOA and RA patients.

Direct and indirect interrelations between anthropometric and physiological variables in athletic and non-athletic adolescent girls: a path-analytic study

Our previous analysis of anthropometric and exercise test data of 62 athletic and 56 non-athletic girls (age range 10.5-15.5 years) showed that the intensity of habitual exercise failed to discriminate between the group means of the studied variables. However, the patterns of intervariable correlations differed between the subgroups categorized by physical activity. The present paper studies the problem of this difference further by using exploratory multivariate regression of aerobic power (VO2max) on 10 anthropometric variables and age. The VO2(max) regression was significant (Y(nonathletic) = 0.0194x(1) + 0.004x(2) - 0.371x(3) + 0.045x(4) - 0.177x(5) + 0.070x(6) - 0.271x(7) - 0.170x(8) + 0.015x(9) - 0.0005x(10) + 0.185x(11), SEE = 0.37, R2 = 0.71, F(11.44) = 9.63; Y(athletic) = 0.029x(1) + 0.063x(2) + 0.277x(3) - 0.030x(4) - 0.069x(5) + 0.151x(6) - 0.148x(7) + 0.001x(8) + 0.018x(9) - 0.019x(10) - 0.065x(11), SEE = 0.32, R2 = 0.71, F(11.50) = 11.30), but none or only one of the independent variables had a significant partial regression coefficient. The individual VO2(max) estimates were studied in both groups by using the other group's regression formula to rule out sample dependence. Both formulae gave good approximations of the observed values in spite of the dissimilar regression coefficients. The path analysis of the respective criterion-predictor correlation coefficients confirmed that the relationship of the predictor variables with VO2(max) involved quantitatively direct and indirect effects in the non-athletic and athletic groups.

Precipitation predictors for downscaling: observed and general circulation model relationships

Because of the coarse resolution of general circulation models (GCM), ‘downscaling’ techniques have emerged as a means of relating meso-scale atmospheric variables to grid- and sub-grid-scale surface variables. This study investigates these relationships. As a precursor, inter-variable correlations were investigated within a suite of 15 potential downscaling predictor variables on a daily time-scale for six regions in the conterminous USA, and observed correlations were compared with those based on the HadCM2 coupled ocean/atmosphere GCM. A comparison was then made of observed and model correlations between daily precipitation occurrence (a time series of zeroes and ones) and wet-day amounts and the 15 predictors. These two analyses provided new insights into model performance and provide results that are central to the choice of predictor variables in downscaling of daily precipitation. Also determined were the spatial character of relationships between observed daily precipitation and both mean sea-level pressure (mslp) and atmospheric moisture and daily precipitation for selected regions. The question of whether the same relationships are replicated by HadCM2 was also examined. This allowed the assessment of the spatial consistency of key predictor–predictand relationships in observed and HadCM2 data. Finally, the temporal stability of these relationships in the GCM was examined. Little difference between results for 1980–1999 and 2080–2099 was observed. For correlations between predictor variables, observed and model results were generally similar, providing strong evidence of the overall physical realism of the model. For correlations with precipitation, the results are less satisfactory. For example, model precipitation is more strongly dependent on surface divergence and specific humidity than observed precipitation, while the latter has a stronger link to 500 hPa divergence than is evident in the model. These results suggest possible deficiencies in the model precipitation process, and may indicate that the model overestimates future changes in precipitation. Correlation field patterns for mslp versus precipitation are remarkably similar for observed data and HadCM2 output. Differences in the correlation fields for specific humidity are more noticeable, especially in summer. In many cases, maximum correlations between precipitation and mslp occurred away from the grid box; whereas correlations with specific humidity were largest when the data were propinquitous. This suggests that the choice of predictor variable and the corresponding predictor domain, in terms of location and spatial extent, are critical factors affecting the realism and stability of downscaled precipitation scenarios. Copyright © 2000 Royal Meteorological Society

Precipitation predictors for downscaling: observed and general circulation model relationships

Because of the coarse resolution of general circulation models (GCM), ‘downscaling’ techniques have emerged as a means of relating meso-scale atmospheric variables to grid- and sub-grid-scale surface variables. This study investigates these relationships. As a precursor, inter-variable correlations were investigated within a suite of 15 potential downscaling predictor variables on a daily time-scale for six regions in the conterminous USA, and observed correlations were compared with those based on the HadCM2 coupled ocean/atmosphere GCM. A comparison was then made of observed and model correlations between daily precipitation occurrence (a time series of zeroes and ones) and wet-day amounts and the 15 predictors. These two analyses provided new insights into model performance and provide results that are central to the choice of predictor variables in downscaling of daily precipitation. Also determined were the spatial character of relationships between observed daily precipitation and both mean sea-level pressure (mslp) and atmospheric moisture and daily precipitation for selected regions. The question of whether the same relationships are replicated by HadCM2 was also examined. This allowed the assessment of the spatial consistency of key predictor–predictand relationships in observed and HadCM2 data. Finally, the temporal stability of these relationships in the GCM was examined. Little difference between results for 1980–1999 and 2080–2099 was observed. For correlations between predictor variables, observed and model results were generally similar, providing strong evidence of the overall physical realism of the model. For correlations with precipitation, the results are less satisfactory. For example, model precipitation is more strongly dependent on surface divergence and specific humidity than observed precipitation, while the latter has a stronger link to 500 hPa divergence than is evident in the model. These results suggest possible deficiencies in the model precipitation process, and may indicate that the model overestimates future changes in precipitation. Correlation field patterns for mslp versus precipitation are remarkably similar for observed data and HadCM2 output. Differences in the correlation fields for specific humidity are more noticeable, especially in summer. In many cases, maximum correlations between precipitation and mslp occurred away from the grid box; whereas correlations with specific humidity were largest when the data were propinquitous. This suggests that the choice of predictor variable and the corresponding predictor domain, in terms of location and spatial extent, are critical factors affecting the realism and stability of downscaled precipitation scenarios. Copyright © 2000 Royal Meteorological Society

Sampling Design for Contaminant Distribution in Lake Sediments

A sampling design methodology for monitoring contaminant distribution in lake sediments is presented in this paper. Two optimization approaches are employed: a minimization of the variance of estimation approach and a sampling cost minimization approach, allowing an economically efficient sampling design and a decision-making tool given the multiobjective nature of the problem. The geostatistical method of cokriging is used as a tool on which the proposed sampling design is based. The adopted technique incorporates spatial as well as intervariable correlations, to improve the prediction and estimation of sampled quantities. The methodology is applied to Clear Lake, California, to design a network for sampling mercury concentrations in lake sediments. The network design takes advantage of the cross correlation between the mercury concentrations and sediment grain size index. A sensitivity analysis is carried out to assess the sensitivity of the solution to the optimization model inputs.