bluuebunny/crossref_metadata_2025_split huggingface dataset:
Field Significance Revisited: Spatial Bias Errors in Forecasts as Applied to the Eta Model
Authors: Elmore Kimberly L. , Baldwin Michael E. , Schultz David M. 
Published: March 2006

Abstract
The spatial structure of bias errors in numerical model output is valuable to both model developers and operational forecasters, especially if the field containing the structure itself has statistical significance in the face of naturally occurring spatial correlation. A semiparametric Monte Carlo method, along with a moving blocks bootstrap method is used to determine the field significance of spatial bias errors within spatially correlated error fields. This process can be completely automated, making it an attractive addition to the verification tools already in use. The process demonstrated here results in statistically significant spatial bias error fields at any arbitrary significance level.
To demonstrate the technique, 0000 and 1200 UTC runs of the operational Eta Model and the operational Eta Model using the Kain–Fritsch convective parameterization scheme are examined. The resulting fields for forecast errors for geopotential heights and winds at 850, 700, 500, and 250 hPa over a period of 14 months (26 January 2001–31 March 2002) are examined and compared using the verifying initial analysis. Specific examples are shown, and some plausible causes for the resulting significant bias errors are proposed.
Assimilation with an Ensemble Kalman Filter of Synthetic Radial Wind Data in Anisotropic Turbulence: Perfect Model Experiments
Authors: Charron Martin , Houtekamer P. L. , Bartello Peter 
Published: March 2006

Abstract
The ensemble Kalman filter (EnKF) developed at the Meteorological Research Branch of Canada is used in the context of synthetic radial wind data assimilation at the mesoscale. A dry Boussinesq model with periodic boundary conditions is employed to provide a control run, as well as two ensembles of first guesses. Synthetic data, which are interpolated from the control run, are assimilated and simulate Doppler radar wind measurements.
Nine “radars” with a range of 120 km are placed evenly on the horizontal 1000 km × 1000 km domain. These radars measure the radial wind with assumed Gaussian error statistics at each grid point within their range provided that there is sufficient upward motion (a proxy for precipitation). These data of radial winds are assimilated every 30 min and the assimilation period extends over 4 days.
Results show that the EnKF technique with 2 × 50 members performed well in terms of reducing the analysis error for horizontal winds and temperature (even though temperature is not an observed variable) over a period of 4 days. However the analyzed vertical velocity shows an initial degradation. During the first 2 days of the assimilation period, the analysis error of the vertical velocity is greater when assimilating radar observations than when scoring forecasts initialized at t = 0 without assimilating any data. The type of assimilated data as well as the localization of the impact of the observations is thought to be the cause of this degradation of the analyzed vertical velocity. External gravity modes are present in the increments when localization is performed. This degradation can be eliminated by filtering the external gravity modes of the analysis increments.
A similar set of experiments is realized in which the model dissipation coefficient is reduced by a factor of 10. This shows the level of sensitivity of the results to the kinetic energy power spectrum, and that the quality of the analyzed vertical wind is worse when dissipation is small.
Synoptic Composites of the Extratropical Transition Life Cycle of North Atlantic Tropical Cyclones: Factors Determining Posttransition Evolution
Authors: Hart Robert E. , Evans Jenni L. , Evans Clark 
Published: March 2006

Abstract
A 34-member ensemble-mean trajectory through the cyclone phase space (CPS) is calculated using Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses for North Atlantic tropical cyclones (TCs) undergoing extratropical transition (ET). Synoptic composites at four ET milestones are examined: 24 h prior to the beginning of ET (TB − 24), the beginning of ET (TB), the end of ET (TE), and 24 h after the end of ET (TE + 24). While the extratropically transitioning TC structure is tightly constrained in its tropical phase, it has a variety of evolutions after TE. Partitioning the ensemble based upon post-ET intensity change or structure discriminates among statistically significant ET precursor conditions. Compositing the various post-ET intensity regimes provides insight into the important environmental factors governing post-ET development.
A TC that intensifies (weakens) after TE begins transition (t = TB) with a negatively (positively) tilted trough 1000 km (1500 km) upstream. The negative tilt permits a contraction and intensification of the eddy potential vorticity (PV) flux, while the positive trough tilt prevents contraction and intensification of the forcing. In 6 of the 34 cases, the posttropical cold-core cyclone develops a warm-seclusion structure, rather than remaining cold core. Anticipation of this warm-seclusion evolution is critical since it represents a dramatically increased risk of middle- to high-latitude wind and wave damage. The warm-seclusion evolution is most favored when the scale of the interacting trough closely matches the scale of the transitioned TC, focusing the eddy PV flux in the outflow layer of the transitioning TC. The sensitivity of structural evolution prior to and after TE illustrated here gives insight into the degradation of global model midlatitude forecast accuracy during a pending ET event.
Eliassen–Palm flux cross sections suggest that ET is primarily driven by the eddy angular momentum flux of the trough, rather than the eddy heat flux associated with the trough. The response of the transitioning TC to the eddy angular momentum forcing is to produce adiabatic ascent and cooling radially inward and beneath the region of the forcing to restore thermal wind balance. In the case of ET, the forcing is maximized lower in the atmosphere, and spread over much greater depth, than in the case of trough-induced TC intensification. Only after TE is the eddy heat flux forcing as significant as the eddy angular momentum forcing, further supporting a physical foundation for the CPS description of cyclone evolution.
Low-Frequency Variability and Evolution of North American Cold Air Outbreaks
Authors: Portis Diane H. , Cellitti Michael P. , Chapman William L. , Walsh John E. 
Published: March 2006

Abstract
Hourly data from 17 relatively evenly distributed stations east of the Rocky Mountains during 54 winter seasons (1948/49 through 2001/02) are used to evaluate the low-frequency variability of extreme cold air outbreaks (CAOs). The results show no overall trend in CAO frequency, despite an increase in mean temperature over the Midwest and especially upstream into the CAO formation regions of high-latitude North America. However, there are regionally based trends in the intensity of long-duration (5 day) CAOs.
Daily heat budgets from reanalysis data are also used to investigate the thermodynamic and dynamic processes involved in the evolution of a subset of the major CAOs. The cooling of the air masses can be generally traced in the heat budget analysis as the air masses track southward along the Rocky Mountains into the Midwest. The earliest cooling begins in northwestern Canada more than a week before the cold air mass reaches the Midwest. Downstream in southwestern Canada, both diabatic and advective processes contribute to the cumulative cooling of the air mass. At peak intensity over the Midwest, diabatic processes and horizontal advection cool the air mass, but warming by subsidence offsets this cooling. By contrast, to the west of the CAO track into the Midwestern United States, vertical advection by orographic lifting cumulatively cools the air in the upslope flow regime associated with the low-level airflow around a cold air mass, and this cooling is offset by diabatic warming. Diabatic processes have strong positive correlations with temperature change over all regions (especially in central Canada) except for the mountainous regions in the United States that are to the west of the track of the cold air mass. Correlations of vertical advection with horizontal advection and diabatic processes are physically consistent and give credibility to the vertical advection field.
Diagnosis of Tropospheric Moisture over Saudi Arabia and Influences of IOD and ENSO
Authors: Chakraborty Arun , Behera Swadhin K. , Mujumdar Milind , Ohba Ryohji , Yamagata Toshio 
Published: March 2006

AbstractA diagnostic study of atmospheric moisture data over Saudi Arabia derived from a 43-yr National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis revealed that moisture convergence in the lower troposphere and divergence in and above the middle troposphere occurs throughout the year. Although the amount of precipitable water content in the middle troposphere is high, precipitation is less than expected over this semiarid region during a boreal summer monsoon season because of strong moisture divergence. The net tropospheric moisture flux over the arid and semiarid regions of Saudi Arabia shows seasonal and interannual variability. The seasonal variability has a strong semiannual signal with its primary peak February–April and its secondary peak June–August. This pattern is consistent with a similar semiannual signal observed in rainfall climatology. The restricted moisture supply to southwestern Saudi Arabia during summer presumably explains the lack of precipitation in other areas of the country. Winter precipitation, however, is widespread. The increased transport of net atmospheric moisture flux is higher during El Niño and positive Indian Ocean dipole (IOD) phenomena. During these events, influx across the Red Sea (west) side of Saudi Arabia increases. The net flux to the region is reduced by a slight increase of outflux across the Persian Gulf (east) side. Reanalysis data and model-sensitivity experiments show that El Niño or a concurrent positive IOD and El Niño event more strongly amplify net transport than does an independent positive IOD event. The partial-lag correlation analysis with net moisture flux from the Red Sea side shows that the positive IOD mode has a peak correlation coefficient of ∼0.5 with close to a 5-month lead and that El Niño has a peak correlation coefficient of ∼0.6 with close to a 2-month lead.
MOS, Perfect Prog, and Reanalysis
Authors: Marzban Caren , Sandgathe Scott , Kalnay Eugenia 
Published: March 2006

Abstract
Statistical postprocessing methods have been successful in correcting many defects inherent in numerical weather prediction model forecasts. Among them, model output statistics (MOS) and perfect prog have been most common, each with its own strengths and weaknesses. Here, an alternative method (called RAN) is examined that combines the two, while at the same time utilizes the information in reanalysis data. The three methods are examined from a purely formal/mathematical point of view. The results suggest that whereas MOS is expected to outperform perfect prog and RAN in terms of mean squared error, bias, and error variance, the RAN approach is expected to yield more certain and bias-free forecasts. It is suggested therefore that a real-time RAN-based postprocessor be developed for further testing.
Sensitivity of Hurricane Intensity Forecast to Convective Momentum Transport Parameterization
Authors: Han Jongil , Pan Hua-Lu 
Published: March 2006

Abstract
A parameterization of the convection-induced pressure gradient force (PGF) in convective momentum transport (CMT) is tested for hurricane intensity forecasting using NCEP's operational Global Forecast System (GFS) and its nested Regional Spectral Model (RSM). In the parameterization the PGF is assumed to be proportional to the product of the cloud mass flux and vertical wind shear. Compared to control forecasts using the present operational GFS and RSM where the PGF effect in CMT is taken into account empirically, the new PGF parameterization helps increase hurricane intensity by reducing the vertical momentum exchange, giving rise to a closer comparison to the observations. In addition, the new PGF parameterization forecasts not only show more realistically organized precipitation patterns with enhanced hurricane intensity but also reduce the forecast track error. Nevertheless, the model forecasts with the new PGF parameterization still largely underpredict the observed intensity. One of the many possible reasons for the large underprediction may be the absence of hurricane initialization in the models.
3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part I: Cloud Analysis and Its Impact
Authors: Hu Ming , Xue Ming , Brewster Keith 
Published: March 2006

Abstract
In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model are studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) scheme that contains a mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are examined while Part II focuses on the impact of radial velocity and the mass divergence constraint.
The case studied is that of the 28 March 2000 Fort Worth, Texas, tornado outbreaks. The same case was studied by Xue et al. using the ARPS Data Analysis System (ADAS) and an earlier version of the cloud analysis procedure with WSR-88D level-III data. Since then, several modifications to the cloud analysis procedure, including those to the in-cloud temperature adjustment and the analysis of precipitation species, have been made. They are described in detail with examples.
The assimilation and predictions use a 3-km grid nested inside a 9-km one. The level-II reflectivity data are assimilated, through the cloud analysis, at 10-min intervals in a 1-h period that ends a little over 1 h preceding the first tornado outbreak. Experiments with different settings within the cloud analysis procedure are examined. It is found that the experiment using the improved cloud analysis procedure with reflectivity data can capture the important characteristics of the main tornadic thunderstorm more accurately than the experiment using the early version of cloud analysis. The contributions of different modifications to the above improvements are investigated.
3DVAR and Cloud Analysis with WSR-88D Level-II Data for the Prediction of the Fort Worth, Texas, Tornadic Thunderstorms. Part II: Impact of Radial Velocity Analysis via 3DVAR
Authors: Hu Ming , Xue Ming , Gao Jidong , Brewster Keith 
Published: March 2006

Abstract
In this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) radar reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model is studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) data assimilation scheme that contains a 3D mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are discussed. In this part, the impact of radial velocity data and the mass divergence constraint in the 3DVAR cost function are studied.
The case studied is that of the 28 March 2000 Fort Worth tornadoes. The addition of the radial velocity improves the forecasts beyond that experienced with the cloud analysis alone. The prediction is able to forecast the morphology of individual storm cells on the 3-km grid up to 2 h; the rotating supercell characteristics of the storm that spawned two tornadoes are well captured; timing errors in the forecast are less than 15 min and location errors are less than 10 km at the time of the tornadoes.
When forecasts were made with radial velocity assimilation but not reflectivity, they failed to predict nearly all storm cells. Using the current 3DVAR and cloud analysis procedure with 10-min intermittent assimilation cycles, reflectivity data are found to have a greater positive impact than radial velocity. The use of radial velocity does improve the storm forecast when combined with reflectivity assimilation, by, for example, improving the forecasting of the strong low-level vorticity centers associated with the tornadoes. Positive effects of including a mass divergence constraint in the 3DVAR cost function are also documented.
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments
Authors: Zhang Fuqing , Meng Zhiyong , Aksoy Altug 
Published: March 2006

Abstract
Through observing system simulation experiments, this two-part study exploits the potential of using the ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation. Part I focuses on the performance of the EnKF under the perfect model assumption in which the truth simulation is produced with the same model and same initial uncertainties as those of the ensemble, while Part II explores the impacts of model error and ensemble initiation on the filter performance. In this first part, the EnKF is implemented in a nonhydrostatic mesoscale model [the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)] to assimilate simulated sounding and surface observations derived from simulations of the “surprise” snowstorm of January 2000. This is an explosive East Coast cyclogenesis event with strong error growth at all scales as a result of interactions between convective-, meso-, and subsynoptic-scale dynamics.
It is found that the EnKF is very effective in keeping the analysis close to the truth simulation under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the control experiment, in which the truth simulation was produced with the same model and same initial uncertainties as those of the ensemble, a 24-h continuous EnKF assimilation of sounding and surface observations of typical temporal and spatial resolutions is found to reduce the error by as much as 80% (compared to a 24-h forecast without data assimilation) for both observed and unobserved variables including zonal and meridional winds, temperature, and pressure. However, it is observed to be relatively less efficient in correcting errors in the vertical velocity and moisture fields, which have stronger smaller-scale components. The analysis domain-averaged root-mean-square error after 24-h assimilation is ∼1.0–1.5 m s−1 for winds and ∼1.0 K for temperature, which is comparable to or less than typical observational errors. Various sensitivity experiments demonstrated that the EnKF is quite successful in all realistic observational scenarios tested. However, as will be presented in Part II, the EnKF performance may be significantly degraded if an imperfect forecast model is used, as is likely the case when real observations are assimilated.
Effect of temperature on reduction of nanocrystalline Fe2O3 into metallic iron
Authors: Bahgat M. , Khedr M. H. , Nasr M. I. , Sedeek E. K. 
Published: March 2006

The synthesis of ultrafine magnetic material with large saturation magnetisation and high coercivity has received much attention, owing to its wide applications. The reduction of nanocrystalline Fe2O3 into metallic iron was studied. The iron oxide precursor was synthesised by thermal evaporation of iron acetate and then pressed into cylindrical shape compacts before being fired at 500°C for 1 h in a muffle furnace. The prepared nanocrystalline Fe2O3 was characterised using X-ray diffraction analysis technique, reflected light microscope and scanning electron microscope. The reduction behaviour of nanocrystalline Fe2O3 in hydrogen atmosphere at 450–600°C has been studied using thermogravimetric techniques. The reduction rate is controlled initially by the interfacial chemical reaction mechanism, while the solid state diffusion is the rate controlling mechanism at final stages. The reduction temperature has a great effect on physicochemical properties of produced metallic iron during reduction of nanocrystalline Fe2O3. Metallic iron was formed in nanosized grains of 100–200 nm, while grain growth and highly coalescence were observed at reduction temperatures higher than at firing temperatures. Diluted magnetic nanoiron metal was formed with Hc ranged from 25 to 94 Oe, Br from 0·5 to 1·8 emu g−1 and Bs from 24·5 to 41·4 emu g−1.
A multivariate prediction model for microarray cross-hybridization
Authors: Chen Yian A , Chou Cheng-Chung , Lu Xinghua , Slate Elizabeth H , Peck Konan , Xu Wenying , Voit Eberhard O , Almeida Jonas S 
Published: March 2006

Abstract

Background
Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental problem of potential cross-hybridization. This is a pervasive problem for both oligonucleotide and cDNA microarrays; it is considered particularly problematic for the latter. No comprehensive multivariate predictive modeling has been performed to understand how multiple variables contribute to (cross-) hybridization.

Results
We propose a systematic search strategy using multiple multivariate models [multiple linear regressions, regression trees, and artificial neural network analyses (ANNs)] to select an effective set of predictors for hybridization. We validate this approach on a set of DNA microarrays with cytochrome p450 family genes. The performance of our multiple multivariate models is compared with that of a recently proposed third-order polynomial regression method that uses percent identity as the sole predictor. All multivariate models agree that the 'most contiguous base pairs between probe and target sequences,' rather than percent identity, is the best univariate predictor. The predictive power is improved by inclusion of additional nonlinear effects, in particular target GC content, when regression trees or ANNs are used.

Conclusion
A systematic multivariate approach is provided to assess the importance of multiple sequence features for hybridization and of relationships among these features. This approach can easily be applied to larger datasets. This will allow future developments of generalized hybridization models that will be able to correct for false-positive cross-hybridization signals in expression experiments.
A simple method for assessing sample sizes in microarray experiments
Authors: Tibshirani Robert 
Published: March 2006

Abstract

Background
In this short article, we discuss a simple method for assessing sample size requirements in microarray experiments.

Results
Our method starts with the output from a permutation-based analysis for a set of pilot data, e.g. from the SAM package. Then for a given hypothesized mean difference and various samples sizes, we estimate the false discovery rate and false negative rate of a list of genes; these are also interpretable as per gene power and type I error. We also discuss application of our method to other kinds of response variables, for example survival outcomes.

Conclusion
Our method seems to be useful for sample size assessment in microarray experiments.