bluuebunny/crossref_metadata_2025_split huggingface dataset:
MM5 Contrail Forecasting in Alaska
Authors: Stuefer Martin , Meng Xiande , Wendler Gerd 
Published: January 2006

Abstract
The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is being used for forecasting the atmospheric layers of aircraft condensation trail (contrail) formation. Contrail forecasts are based on a conventional algorithm describing the adiabatic mixing of aircraft exhaust with environmental air. Algorithm input data are MM5-forecasted temperature and humidity values at defined pressure or sigma levels, and an aircraft-relevant contrail factor that is derived statistically from a contrail observation database.
For comparison purposes a mean overlap (MO), which is a parameter quantifying the overlap between forecasted contrail layers and contrail layers derived from radiosonde measurements, is introduced. Mean overlap values are used to test for the altitude and thickness of forecasted contrail layers. Contrail layers from Arctic MM5 and Air Force Weather Agency (AFWA) MM5 models agree well with contrail layers derived from corresponding radiosonde measurements for certain forecast periods; a steady decrease of the MO shows a decrease of contrail forecast accuracy with the increasing forecast period. Mean overlaps around 82% indicate reasonable results for the 48-h forecasts. Verification of MM5 with actual contrail observations shows a slightly better performance of Arctic MM5. A possible dry bias might occur in humidity measurements at low temperature levels due to temperature-dependence errors of the humidity sensor polymer, which might also affect forecasts of humidity of the upper troposphere or lower stratosphere. Despite this fact, this contrail verification study shows hit rates higher than 82% within forecast periods up to 36 h using Arctic MM5.
Numerical Simulations of Sea-Breeze Circulations over Northwest Hawaii*
Authors: Zhang Yongxin , Chen Yi-Leng , Schroeder Thomas A. , Kodama Kevin 
Published: January 2006

AbstractSea-breeze cases during 23–28 June 1978 over northwest Hawaii are simulated using the National Centers for Environmental Prediction (NCEP) Mesoscale Spectral Model (MSM) coupled with an advanced Land Surface Model (LSM) with 3-km horizontal resolution. Subjective analyses show that except for 27 June, the MSM–LSM-predicted onset time, duration, and vertical extent of the sea breezes agree well with observations. The largest mean absolute errors for surface air temperature occur at the coastal stations under strong trade wind conditions (e.g., 23 and 27 June). The model-simulated rainfall distribution in association with sea-breeze fronts is consistent with observations. Sensitivity tests demonstrate the modulation of sea-breeze behavior by surface properties. High-resolution (1 km) MSM–LSM simulations for 23 and 27 June show improvements over the 3-km MSM–LSM in reproducing the observed sea breezes through a better representation of local terrain and a better simulation of orographically enhanced trades channeling through the Waimea Saddle. Deficiencies noted in the model simulations include 1) sea-breeze speeds are more than 2–3 m s−1 weaker than observations, and 2) horizontal penetration of sea breezes is generally overestimated. These deficiencies in the model simulations are primarily related to two factors: one is the underestimation of the trade wind speeds in the initialization from the NCEP–NCAR reanalysis data that is favoring the farther penetration of the sea breezes, and the other is the uncertainties in the thermal properties of the lava rocks that affect the surface temperature and the sea-breeze speed.
A Sensitivity Study of Hodograph-Based Methods for Estimating Supercell Motion
Authors: Ramsay Hamish A. , Doswell Charles A. 
Published: January 2006

AbstractFour supercell motion forecast algorithms are investigated with respect to their hodograph-analysis parameters. Another method derived from the data presented herein, the so-called offset method, is used to develop a baseline standard for the aforementioned schemes, using the observed storm motions and the mean wind. It is not a forecast scheme, as it is based on knowing the observed storm motions. This work explores the sensitivity of these algorithms to their arbitrary parameters by systematically varying those parameters, using a dataset of 394 right-moving supercells, and associated proximity soundings. The parameters used in these algorithms to define the layer depths for advection and/or propagation of supercells have not been shown to be optimum for this purpose. These arbitrary parameters compose the top and bottom levels of the mean wind layer, and a deviation vector from the mean wind defined through that layer. Two of the most recently developed algorithms have also implemented the vertical wind shear vector over an arbitrary layer depth. It has been found that, among other results, the scheme using both mean wind and vertical wind shear is more sensitive to the depth of the mean wind layer than it is to the depth of the vertical wind shear layer. It has also been shown that, when using the simplest schemes, the most accurate forecasts, on average, are obtained by using deep mean wind layers (i.e., greater than 0–10 km). Indeed, all the forecast schemes show a strong tendency for the u component of the predicted storm motion to be regulated by the depth of the mean wind layer. The υ component of the prediction storm motion, on the other hand, appears to be controlled by the deviation vector from the layer-mean wind. Although the schemes using vertical shear are shown to perform somewhat better on average than schemes based on the mean wind alone, there are times in which they also result in large forecast errors. The results demonstrate the inherent difficulty in using an observed hodograph to predict supercell motion.
Warm Season Lightning Probability Prediction for Canada and the Northern United States
Authors: Burrows William R. , Price Colin , Wilson Laurence J. 
Published: January 2006

AbstractStatistical models valid May–September were developed to predict the probability of lightning in 3-h intervals using observations from the North American Lightning Detection Network and predictors derived from Global Environmental Multiscale (GEM) model output at the Canadian Meteorological Centre. Models were built with pooled data from the years 2000–01 using tree-structured regression. Error reduction by most models was about 0.4–0.7 of initial predictand variance.Many predictors were required to model lightning occurrence for this large area. Highest ranked overall were the Showalter index, mean sea level pressure, and troposphere precipitable water. Three-hour changes of 500-hPa geopotential height, 500–1000-hPa thickness, and MSL pressure were highly ranked in most areas. The 3-h average of most predictors was more important than the mean or maximum (minimum where appropriate). Several predictors outranked CAPE, indicating it must appear with other predictors for successful statistical lightning prediction models.Results presented herein demonstrate that tree-structured regression is a viable method for building statistical models to forecast lightning probability. Real-time forecasts in 3-h intervals to 45–48 h were made in 2003 and 2004. The 2003 verification suggests a hybrid forecast based on a mixture of maximum and mean forecast probabilities in a radius around a grid point and on monthly climatology will improve accuracy. The 2004 verification shows that the hybrid forecasts had positive skill with respect to a reference forecast and performed better than forecasts defined by either the mean or maximum probability at most times. This was achieved even though an increase of resolution and change of convective parameterization scheme were made to the GEM model in May 2004.
The Use of Sounding-Derived Indices for a Neural Network Short-Term Thunderstorm Forecast
Authors: Manzato Agostino 
Published: January 2006

Abstract
In this paper several indices derived from atmospheric sounding data are used to develop a short-term thunderstorm forecast tool. The Udine (Italy) sounding (WMO code 16044, taken every 6 h, hereafter 6 h) is used to describe the initial conditions in which a thunderstorm may develop in the Friuli Venezia Giulia region. The tool forecasts the convective activity in the 6 h after the sounding is launched.
Sounding, lightning, and mesonet station data, from April to September of the years 1995–2002, were used to train and validate artificial neural networks (ANNs) and the data of 2003 were used as an independent test sample. Special emphasis was given to avoid one of the major ANN problems, that of data overfitting, which requires limiting the possible complexity of the ANN, that is, the total number of inputs (predictors) and of hidden neurons. To select the best input set, the training–validation mechanism was repeated in eight different ways.
Two types of ANNs were developed: the first is a classification model and is built for forecasting the thunderstorm occurrence, defined as the report of at least one cloud-to-ground lightning strike. If this first ANN forecasts convective activity, then a second ANN, built as a regression model, is used for forecasting the thunderstorm intensity. The relative operating characteristic (ROC) diagram and some parameters derived from a contingency table were used to compare the different classification performances. The linear cross-correlation coefficient, R2, was used to assess the performance of the regression ANNs.
The ANN inputs were not the only “raw” sounding-derived index values; in addition, two derived sets of variables, corresponding to the z scores and the “posterior probability” transformed data, were used as preprocessing methods. The last method yields the best results. The tool reported on here has been used operationally since 2001 in the regional meteorological office of the Friuli Venezia Giulia region [Osservatorio Meteorologico Regionale/Agenzia Regionale per la Protezione dell’Ambiente (OSMER/ARPA)].
Mitochondrial genetic effects on latent class variables associated with susceptibility to alcoholism
Authors: Lease Loren R , Winnier Deidre A , Williams Jeff T , Dyer Thomas D , Almasy Laura , Mahaney Michael C 
Published: January 2006

Abstract
We report the results of statistical genetic analyses of data from the Collaborative Study on the Genetics of Alcoholism prepared for the Genetic Analysis Workshop 14 to detect and characterize maternally inherited mitochondrial genetic effects on variation in latent class psychiatric/behavioral variables employed in the diagnosis of alcoholism. Using published extensions to variance decomposition methods for statistical genetic analysis of continuous and discrete traits we: 1) estimated the proportion of the variance in each trait due to the effects of mitochondrial DNA (mtDNA), 2) tested for pleiotropy, both mitochondrial genetic and residual additive genetic, between trait pairs, and 3) evaluated whether the simultaneous estimation of mitochondrial genetic effects on these traits improves our ability to detect and localize quantitative trait loci (QTL) in the nuclear genome. After correction for multiple testing, we find significant (p < 0.009) mitochondrial genetic contributions to the variance for two latent class variables. Although we do detect significant residual additive genetic correlations between the two traits, there is no evidence of a residual mitochondrial genetic correlation between them. Evidence for autosomal QTL for these traits is improved when linkage screens are conditioned on significant mitochondrial genetic effects. We conclude that mitochondrial genes may contribute to variation in some latent class psychiatric/behavioral variables associated with alcoholism.
A simple nonparametric multipoint procedure to test for linkage through mothers or fathers as well as imprinting effects in the presence of linkage
Authors: Lemire Mathieu 
Published: January 2006

Abstract
A simple multipoint procedure to test for parent-of-origin effects in samples of affected siblings is discussed. The procedure consists of artificially changing all full sibs to half-sibs, with distinct mothers or fathers depending on the parental origin to be evaluated, then analyzing these families with commonly used statistics and software. The procedure leads to tests for linkage through mothers or fathers and also leads to a test for imprinting effects in the presence of linkage. Moreover, simulations illustrate that in regions unlinked to susceptibility genes this multipoint procedure does not have an inflated type I error if a sex-averaged genetic map is used, even when large differences exist between male-specific and female-specific maps. In regions linked with susceptibility genes, the test of imprinting is biased under the null hypothesis if differences exist between sex-specific maps, irrespective of the map used in the analysis. The procedure is applied to the Collaborative Study on the Genetics of Alcoholism dataset from the Genetic Analysis Workshop 14. Results indicate that brothers categorized as affected according to the DMS-III-R and Feighner classification show evidence of linkage through fathers to the 6q25 region (p = 0.00038) as well as modest evidence of imprinting (p = 0.018). This region harbors OPRM1, a candidate gene for substance dependence.
A genome scan for parent-of-origin linkage effects in alcoholism
Authors: Liu Xiao-Qing , Greenwood Celia MT , Wang Ke-Sheng , Paterson Andrew D 
Published: January 2006

Abstract

Background
Alcoholism is a complex disease in which genomic imprinting may play an important role in its susceptibility.

Objective
To conduct a genome-wide search for loci that may have strong parent-of-origin linkage effects in alcoholism; to compare the linkage results between the microsatellites and the two single-nucleotide polymorphism (SNP) platforms.

Methods
Nonparametric linkage analyses were performed using ALLEGRO with the three sets of markers provided by the Genetic Analysis Workshop 14 for the Collaborative Study on the Genetics of Alcoholism Problem 1 data. Both sex-averaged and sex-specific genetic maps were used. We also provided a valid statistical test to determine whether the parental allele sharing differed significantly.

Results
Significant maternal linkage effects (paternal imprinting) were observed on chromosome 12 using either the microsatellite markers or the two SNP panels. The two SNP sets did not improve the linkage signals compared to the results from the microsatellite markers on chromosome 12. Possible paternal linkage effects (maternal imprinting) on chromosome 7 and maternal linkage effects (paternal imprinting) on chromosome 10 were found using the two SNP panels.

Conclusion
For diseases which may have parent-of-origin effects, linkage analysis looking at parental sharing separately may reduce locus heterogeneity and increase the ability to identify that which can not be identified with usual linkage analysis.
Genetic imprinting analysis for alcoholism genes using variance components approach
Authors: Shete Sanjay , Yu Robert 
Published: January 2006

Abstract
Genomic imprinting, which is also known as the parent-of-origin effect, is a mechanism that only expresses one copy of a gene pair depending upon the parental origin. Although many chromosomal regions in the human genome are likely to be imprinted, imprinting is not accounted for in the usual linkage analysis. In this study, using a variance-components approach with a quantitative phenotype ttth-FP1, we found significant evidence of imprinting at two loci, D7S1790 and D1S1631, on chromosome 1 and chromosome 7, respectively. Our results suggest that allowing for the possibility of imprinting can increase the power to detect linkage for localizing genes for alcoholism.
Detection of susceptibility loci by genome-wide linkage analysis
Authors: Babron Marie-Claude , Bourgain Catherine , Leutenegger Anne-Louise , Clerget-Darpoux Françoise 
Published: January 2006

Abstract
The objective of this study is to evaluate the efficacy of a model-free linkage statistics for finding evidence of linkage using two different maps and to illustrate how the comparison of results from several populations might provide insight into the underlying genetic etiology of the disease of interest. The results obtained in terms of detection of the risk loci and threshold for declaring linkage and power are very similar for a dense SNP map and a sparser microsatellite map. The populations differed in terms of family ascertainment and diagnosis criteria, leading to different power to detect the individual underlying disease loci. Our results for the individual replicates are consistent with the disease model used in the simulation.
Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14
Authors: Edenberg Howard J , Bierut Laura J , Boyce Paul , Cao Manqiu , Cawley Simon , Chiles Richard , Doheny Kimberly F , Hansen Mark , Hinrichs Tony , Jones Kevin , Kelleher Mark , Kennedy Giulia C , Liu Guoying , Marcus Gregory , McBride Celeste , Murray Sarah Shaw , Oliphant Arnold , Pettengill James , Porjesz Bernice , Pugh Elizabeth W , Rice John P , Rubano Todd , Shannon Stu , Steeke Rhoberta , Tischfield Jay A , Tsai Ya Yu , Zhang Chun , Begleiter Henri 
Published: January 2006

Abstract
The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided.
Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits
Authors: Klein Alison P , Tsai Ya-Yu , Duggal Priya , Gillanders Elizabeth M , Barnhart Michael , Mathias Rasika A , Dusenberry Ian P , Turiff Amy , Chines Peter S , Goldstein Janet , Wojciechowski Robert , Hening Wayne , Pugh Elizabeth W , Bailey-Wilson Joan E 
Published: January 2006

Abstract
Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.
Interval estimation of disease loci: development and applications of new linkage methods
Authors: Papachristou Charalampos , Lin Shili 
Published: January 2006

Abstract
Three variants of the confidence set inference (CSI) procedure were proposed and applied to both the simulated and the Collaborative Study on the Genetics of Alcoholism (COGA) data. For each of the two applications, we first performed a preliminary genome scan study based on the microsatellite markers using the GENEHUNTER+ software to identify regions that potentially harbor disease loci. For each such region, we estimated the sibling identity-by-descent sharing probability distribution at the putative disease locus. Based on these estimated probabilities, the CSI procedures were employed to further localize the disease loci using the single-nucleotide polymorphism markers, leading to confidence intervals/regions for their locations. For our analysis with the simulated data, we had knowledge of the simulating models at the time we performed the analysis.
Inclusion of unaffected sibs increases power in model-free linkage analysis of a behavioral trait
Authors: Plancoulaine Sabine , Alcaïs Alexandre , Chen Yue , Abel Laurent , Gagnon France 
Published: January 2006

Abstract
Study design strategies are of critical importance in the search for genes underlying complex diseases. Two important design choices in planning gene mapping studies are the analytic strategy to be used, which will have an impact on the type of data to be collected, and the choice of genetic markers. In the present paper, we used the simulated behavioral trait data provided in the Genetic Analysis Workshop14 to: 1) investigate the usefulness of incorporating unaffected sibs in model-free linkage analysis and, 2) compare linkage results of genome scans using a 7-cM microsatellite map with a 3-cM single nucleotide polymorphisms map. To achieve these aims, we used the maximum-likelihood-binomial method with two different coding approaches. We defined the unaffected sibs as those totally free of phenotypes correlated to the disease. Without prior knowledge of the answers, we were able to correctly localize 2 out of 5 loci (LOD > 3) in a sample of 200 families that included the unaffected sibs but only one locus when based on an affected-only strategy, using either microsatellite or SNPs genome scan. LOD scores were considerably higher using the analytic strategy which incorporated the unaffected sibs. In conclusion, including unaffected sibs in model-free linkage analysis of complex binary traits is helpful, at least when complete parental data are available, whereas there are no striking advantages in using single nucleotide polymorphisms over microsatellite map at marker densities used in the current study.