bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal Spectral Decomposition (OSD) for Ocean Data Assimilation
Authors: Chu Peter C. , Tokmakian Robin T. , Fan Chenwu , Sun L. Charles 
Published: March 2015

AbstractOptimal spectral decomposition (OSD) is applied to ocean data assimilation with variable (temperature, salinity, or velocity) anomalies (relative to background or modeled values) decomposed into generalized Fourier series, such that any anomaly is represented by a linear combination of products of basis functions and corresponding spectral coefficients. It has three steps: 1) determination of the basis functions, 2) optimal mode truncation, and 3) update of the spectral coefficients from innovation (observational increment). The basis functions, depending only on the topography of the ocean basin, are the eigenvectors of the Laplacian operator with the same lateral boundary conditions as the assimilated variable anomalies. The Vapnik–Chervonkis dimension is used to determine the optimal mode truncation. After that, the model field updates due to innovation through solving a set of a linear algebraic equations of the spectral coefficients. The strength and weakness of the OSD method are demonstrated through a twin experiment using the Parallel Ocean Program (POP) model.
Polarimetric Variables Retrieval with Clutter Suppression for Staggered PRT Sequences
Authors: Nguyen Cuong M. , Chandrasekar V. 
Published: March 2015

AbstractThis paper presents a procedure to filter ground clutter from dual-polarized staggered pulse repetition time (PRT) radar data in simultaneous and alternating transmission modes for polarimetric variables retrieval. The filter is designed in the time domain so that polarimetric variables such as the differential phase () and the copolar correlation coefficient () can be estimated directly from clutter-filtered time series data using a conventional method. In the case of the simultaneous mode, a single filter is used for both channels to maintain the signal correlation after filtering. For the alternating mode, because the polarizations are transmitted in different waveforms, two separate filters are required. However, the filters are designed so that the responses of the filters to the signals are identical within the extended Doppler range. Based on radar simulation, it is shown that the method can provide accurate retrieval of polarimetric variables even in the case of strong clutter contamination. Also, the performance of the method is illustrated on dual-polarized staggered PRT ⅔ data from the NASA dual-frequency dual-polarized Doppler radar (D3R).
Developing and Evaluating Ice Cloud Parameterizations for Forward Modeling of Radar Moments Using in situ Aircraft Observations
Authors: Maahn Maximilian , Löhnert Ulrich , Kollias Pavlos , Jackson Robert C. , McFarquhar Greg M. 
Published: March 2015

AbstractObserving ice clouds using zenith pointing millimeter cloud radars is challenging because the transfer functions relating the observables to meteorological quantities are not uniquely defined. Here, the authors use a spectral radar simulator to develop a consistent dataset containing particle mass, area, and size distribution as functions of size. This is an essential prerequisite for radar sensitivity studies and retrieval development. The data are obtained from aircraft in situ and ground-based radar observations during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) campaign in Alaska. The two main results of this study are as follows: 1) An improved method to estimate the particle mass–size relation as a function of temperature is developed and successfully evaluated by combining aircraft in situ and radar observations. The method relies on a functional relation between reflectivity and Doppler velocity. 2) The impact on the Doppler spectrum by replacing measurements of particle area and size distribution by recent analytical expressions is investigated. For this, higher-order moments such as skewness and kurtosis as well as the slopes of the Doppler spectrum are also used as a proxy for the Doppler spectrum. For the area–size relation, it is found that a power law is not sufficient to describe particle area and small deviations from a power law are essential for obtaining consistent higher moments. For particle size distributions, the normalization approach for the gamma distribution of Testud et al., adapted to maximum diameter as size descriptor, is preferred.
Using Evolutionary Programs to Maximize Minimum Temperature Forecast Skill
Authors: Roebber Paul J. 
Published: March 2015

Abstract
Evolutionary program ensembles are developed and tested for minimum temperature forecasts at Chicago, Illinois, at forecast ranges of 36, 60, 84, 108, 132, and 156 h. For all forecast ranges examined, the evolutionary program ensemble outperforms the 21-member GFS model output statistics (MOS) ensemble when considering root-mean-square error and Brier skill score. The relative advantage in root-mean-square error widens with forecast range, from 0.18°F at 36 h to 1.53°F at 156 h while the probabilistic skill remains positive throughout. At all forecast ranges, probabilistic forecasts of abnormal conditions are particularly skillful compared to the raw GFS guidance.
The evolutionary program reliance on particular forecast inputs is distinct from that obtained from considering multiple linear regression models, with less reliance on the GFS MOS temperature and more on alternative data such as upstream temperatures at the time of forecast issuance, time of year, and forecasts of wind speed, precipitation, and cloud cover. This weighting trends away from current observations and toward seasonal (climatological) measures as forecast range increases.
Using two different forms of ensemble member subselection, a Bayesian model combination calibration is tested on both ensembles. This calibration had limited effect on evolutionary program ensemble skill but was able to improve MOS ensemble performance, reducing but not eliminating the skill gap between them. The largest skill differentials occurred at the longest forecast ranges, beginning at 132 h. A hybrid, calibrated ensemble was able to provide some further increase in skill.
A Framework for Diagnosing Seasonal Prediction through Canonical Event Analysis
Authors: Roundy Joshua K. , Yuan Xing , Schaake John , Wood Eric F. 
Published: March 2015

Abstract
Hydrologic extremes in the form of flood and drought have large impacts on society that can be reduced through preparations made possible by seasonal prediction. However, the skill of seasonal predictions from global climate models is uncertain, which severely limits their practical use. In the past, the skill assessment has been limited to a single temporal or spatial resolution for a short hindcast period, which is prone to sampling errors, and noise that leads to uncertainty. In this work a framework that uses “canonical” forecast events, or averages in space–time, to provide a more certain assessment of when and where models are skillful is developed. This framework is demonstrated by using NCEP’s Climate Forecast System, version 2, hindcast dataset for precipitation and temperature over the contiguous United States (CONUS). As part of the canonical event analyses, the probabilistic predictability metric (PPM) is used to define spatial and seasonal variability of forecast skill and its attribution to El Niño–Southern Oscillation (ENSO) over the CONUS. The PPM indicates that there are clear seasonal and spatial patterns of model skill that provide a better understanding of when and where to have confidence in model predictions as compared to a skill metric based on a single temporal and spatial scale. Furthermore, the canonical event analysis also facilitates the attribution of spatiotemporal variations of precipitation predictive skill to the antecedent ENSO conditions. This work illustrates the importance of using canonical event analysis to diagnose seasonal predictions and discusses its extensions for model development.
Rapid-Scan, Polarimetric, Doppler Radar Observations of Tornadogenesis and Tornado Dissipation in a Tornadic Supercell: The “El Reno, Oklahoma” Storm of 24 May 2011*
Authors: Houser Jana Lesak , Bluestein Howard B. , Snyder Jeffrey C. 
Published: March 2015

Abstract
On 24 May 2011, a mobile, rapid-scan, X-band, polarimetric, Doppler radar (RaXPol) collected data on a supercell as it produced two tornadoes near El Reno, Oklahoma. The first tornado, rated an EF-3, was documented from intensification to decay, and the genesis and intensification of a second tornado that was rated an EF-5 was subsequently also documented.
The objective of this study is to examine the spatiotemporal evolution of the rotation associated with the tornadoes (i) as the first tornado weakened to subtornadic intensity and (ii) as the second tornado formed and intensified. It is found that weakening did not occur monotonically. The transition from tornadic to subtornadic intensity over the depth of the radar volume (~4 km) occurred in less than 30 s, but this behavior is contingent upon the threshold for Doppler shear used to define the tornado. Similarly, the onset of a tornadic-strength Doppler velocity couplet occurred within a 30-s period over all elevations.
Additionally, the evolution of storm-scale features associated with tornado dissipation and tornadogenesis is detailed. These features evolved considerably over relatively short time intervals (1–4 min). It is shown that during the transition period between the two tornadoes, two mesocyclones were present, but neither the tornadoes nor the mesocyclones evolved in a manner entirely consistent with any published conceptual model of supercell cycling, although certain aspects were similar to classic conceptual models. The mesocyclone and the tornado evolved differently from each other, in a manner that resembles a hybrid between the occluding and nonoccluding cyclic mesocyclogenesis models presented by Adlerman and Droegemeier.
A Vertically Flow-Following Icosahedral Grid Model for Medium-Range and Seasonal Prediction. Part I: Model Description
Authors: Bleck Rainer , Bao Jian-Wen , Benjamin Stanley G. , Brown John M. , Fiorino Michael , Henderson Thomas B. , Lee Jin-Luen , MacDonald Alexander E. , Madden Paul , Middlecoff Jacques , Rosinski James , Smirnova Tanya G. , Sun Shan , Wang Ning 
Published: March 2015

Abstract
A hydrostatic global weather prediction model based on an icosahedral horizontal grid and a hybrid terrain-following/isentropic vertical coordinate is described. The model is an extension to three spatial dimensions of a previously developed, icosahedral, shallow-water model featuring user-selectable horizontal resolution and employing indirect addressing techniques. The vertical grid is adaptive to maximize the portion of the atmosphere mapped into the isentropic coordinate subdomain. The model, best described as a stacked shallow-water model, is being tested extensively on real-time medium-range forecasts to ready it for possible inclusion in operational multimodel ensembles for medium-range to seasonal prediction.
Predicting the Real-Time Multivariate Madden–Julian Oscillation Index through a Low-Order Nonlinear Stochastic Model
Authors: Chen Nan , Majda Andrew J. 
Published: March 2015

Abstract
A new low-order nonlinear stochastic model is developed to improve the predictability of the Real-time Multivariate Madden–Julian oscillation (MJO) index (RMM index), which is a combined measure of convection and circulation. A recent data-driven, physics-constrained, low-order stochastic modeling procedure is applied to the RMM index. The result is a four-dimensional nonlinear stochastic model for the two observed RMM variables and two hidden variables involving correlated multiplicative noise defined through energy-conserving nonlinear interaction. The special structure of the low-order model allows efficient data assimilation for the initialization of the hidden variables that facilitates the ensemble prediction algorithm. An information-theoretic framework is applied to the calibration of model parameters over a short training phase of 3 yr. This framework involves generalizations of the anomaly pattern correlation, the RMS error, and the information deficiency in the model forecast. The nonlinear stochastic models show skillful prediction for 30 days on average in these metrics. More importantly, the predictions succeed in capturing the amplitudes of the RMM index and the useful skill of forecasting strong MJO events is around 40 days. Furthermore, information barriers to prediction for linear models imply the necessity of the nonlinear interactions between the observed and hidden variables as well as the multiplicative noise in these low-order stochastic models.
Development of Anomalous Temperature Regimes over the Southeastern United States: Synoptic Behavior and Role of Low-Frequency Modes
Authors: Westby Rebecca M. , Black Robert X. 
Published: March 2015

AbstractDuring winter, anomalous temperature regimes (ATRs), which include cold-air outbreaks (CAOs) and warm waves (WWs), have important impacts in the southeastern United States. This study provides a synoptic–dynamic characterization of ATRs in the southeastern United States from 1949 to 2011 through composite time-evolution analyses. Events are categorized by the sign and amplitude of relevant low-frequency modes. During CAO (WW) onset, negative (positive) geopotential height anomalies are observed in the upper troposphere over the Southeast with oppositely signed anomalies in the lower troposphere over the central United States. In most cases, there is a surface east–west geopotential height anomaly dipole, with anomalous northerly (CAO) or southerly (WW) flow into the Southeast leading to cold or warm surface air temperature anomalies, respectively. Companion potential vorticity anomaly analyses reveal prominent features in the mid- to upper troposphere consistent with the coincident geopotential height anomaly patterns. Ultimately, synoptic-scale disturbances are found to serve as dynamic triggers for ATR events, while low-frequency modes provide a favorable environment for ATR onset. The results provide a qualitative indication of the role of low-frequency modes in ATR onset. In WW (CAO) events influenced by low-frequency modes, the North American geopotential height anomaly pattern arises in part as a downstream (regional) manifestation of the negative Pacific–North American pattern (North Atlantic Oscillation). Interestingly, the North Atlantic Oscillation contributes to both CAO onset and demise. Thus, these results indicate that low-frequency modes also affect event duration (CAOs). One general distinction found for ATRs is that CAOs involve substantial airmass transport while WW formation is more regional in nature.
The 2012 Triply Nested, High-Resolution Operational Version of the Hurricane Weather Research and Forecasting Model (HWRF): Track and Intensity Forecast Verifications
Authors: Goldenberg Stanley B. , Gopalakrishnan Sundararaman G. , Tallapragada Vijay , Quirino Thiago , Marks Frank , Trahan Samuel , Zhang Xuejin , Atlas Robert 
Published: March 2015

AbstractThe Hurricane Weather Research and Forecasting Model (HWRF) was operationally implemented with a 27-km outer domain and a 9-km moving nest in 2007 (H007) as a tropical cyclone forecast model for the North Atlantic and eastern Pacific hurricane basins. During the 2012 hurricane season, a modified version of HWRF (H212), which increased horizontal resolution by adding a third (3 km) nest within the 9-km nest, replaced H007. H212 thus became the first operational model running at convection-permitting resolution. In addition, there were modifications to the initialization, model physics, tracking algorithm, etc. This paper compares H212 hindcast forecasts for the 2010–11 Atlantic hurricane seasons with forecasts from H007 and H3GP, a triply nested research version of HWRF. H212 reduced track forecast errors for almost all forecast times versus H007 and H3GP. H3GP was superior for intensity forecasts, although H212 showed some improvement over H007. Stratifying the cases by initial vertical wind shear revealed that the main weakness for H212 intensity forecasts was for cases with initially high shear. In these cases, H212 over- and under-intensified storms that were initially stronger and weaker, respectively. These results suggest the primary deficiency negatively impacting H212 intensity forecasts, especially in cases of rapid intensification, was that physics calls were too infrequent for the 3-km inner mesh. Correcting this deficiency along with additional modifications in the 2013 operational version yielded improved track and intensity forecasts. These intensity forecasts were comparable to statistical–dynamical models, showing that dynamical models can contribute to a decrease in operational forecast errors.
Impacts of a Storm Merger on the 24 May 2011 El Reno, Oklahoma, Tornadic Supercell
Authors: Tanamachi Robin L. , Heinselman Pamela L. , Wicker Louis J. 
Published: March 2015

AbstractOn 24 May 2011, a tornadic supercell (the El Reno, Oklahoma, storm) produced tornadoes rated as category 3 and 5 events on the enhanced Fujita scale (EF3 and EF5, respectively) during a severe weather outbreak. The transition (“handoff”) between the two tornadoes occurred as the El Reno storm merged with a weaker, ancillary storm. To examine the impacts of the merger on the dynamics of these storms, a series of three-dimensional cloud-scale analyses are created by assimilating 1-min volumetric observations from the National Weather Radar Testbed’s phased array radar into a numerical cloud model using the local ensemble transform Kalman filter technique. The El Reno storm, its updrafts, and vortices in the analyzed fields are objectively identified, and the changes in these objects before, during, and after the merger are examined. It is found that the merger did not cause the tornado handoff, which preceded the updraft merger by about 5 min. Instead, the handoff likely resulted from midlevel mesocyclone occlusion, in which the midlevel mesocyclone split and a portion is shed rearward with respect to storm motion. During the merger process, the midlevel mesocyclone and updraft structure in the El Reno storm became relatively disorganized. New updraft pulses that formed above colliding outflow boundaries between the two storms tilted environmental vorticity from low levels to generate an additional midlevel vortex that later merged with the El Reno storm’s midlevel mesocyclone. Once the ~10-min merger process was complete, the El Reno storm and its mesocyclone rapidly reintensified, as access to buoyant inflow sector air was restored.
Ipsilateral Reduced Olfactory Bulb Volume in Patients with Unilateral Nasal Obstruction
Authors: Askar Sherif M. , Elnashar Ismail S. , El‐Anwar Mohammad W. , Amer Hazem S. , El Shawadfy Mohammad A. , Hosny Sameh M. , Bazeed Mohamed F. , Ahmed Ayman F. , Ghanem Mona A. 
Published: March 2015

ObjectivesTo evaluate the effect of unilateral nasal obstruction (by nasal polyps of different histopathology) on olfactory bulb volume using MRI technique.Study DesignCross‐sectional study.SettingTertiary university hospitals, Departments of Otolaryngology and Radiology.Subjects and MethodsEleven patients with a few months of complete unilateral nasal obstruction of different pathological etiologies were selected. MRI assessment of olfactory bulb volume was performed using planimetric manual contouring. The contralateral olfactory bulb was used as a comparative control for the same patient. Eleven healthy controls constituted the control group.ResultsMean olfactory bulb volume ±SD of obstructed side = 14.3 ± 3.7 mm3, mean olfactory bulb volume ±SD of nonobstructed side = 43.49 ± 10.7 mm3. The difference between the 2 sides was significant (P =. 003). The difference in olfactory bulb volume between normal subjects and nonobstructed nasal side was statistically nonsignificant (t =. 9118, P =. 3727), while the difference between normal subjects’ olfactory bulb volume and obstructed nasal side olfactory bulb volume in our patients was extremely statistically significant (t = 9.7320, P <. 0001). A strong negative correlation was found between duration of obstruction and olfactory bulb volume (R = −0.9761).ConclusionThis study shows that unilateral nasal obstruction may be associated with a lateralized ipsilateral difference of olfactory bulb volume.
Menstrual versus non-menstrual attacks of migraine without aura in women with and without menstrual migraine
Authors: Vetvik Kjersti Grøtta , Benth Jūratė Šaltytė , MacGregor E Anne , Lundqvist Christofer , Russell Michael Bjørn 
Published: March 2015

Objective The objective of this article is to compare clinical characteristics of menstrual and non-menstrual attacks of migraine without aura (MO), prospectively recorded in a headache diary, by women with and without a diagnosis of menstrual migraine without aura (MM) according to the International Classification of Headache Disorders (ICHD). Material and methods A total of 237 women from the general population with self-reported migraine in ≥50% of their menstrual periods were interviewed and classified by a physician according to the criteria of the ICHD II. Subsequently, all participants were instructed to complete a prospective headache diary for at least three menstrual cycles. Clinical characteristics of menstrual and non-menstrual attacks of MO were compared by a regression model for repeated measurements. Results In total, 123 (52%) women completed the diary. In the 56 women who were prospectively diagnosed with MM by diary, the menstrual MO-attacks were longer (on average 10.65 hours, 99% CI 3.17–18.12) and more frequently accompanied by severe nausea (OR 2.14, 99% CI 1.20–3.84) than non-menstrual MO-attacks. No significant differences between menstrual and non-menstrual MO-attacks were found among women with MO, but no MM. Conclusion In women from the general population, menstrual MO-attacks differ from non-menstrual attacks only in women who fulfil the ICHD criteria for MM.
Reduced Effect of Aspirin and Clopidogrel Following Hybrid Coronary Revascularization
Authors: Modrau Ivy Susanne , Würtz Morten , Kristensen Steen Dalby , Hvas Anne-Mette 
Published: March 2015

Objective: Reduced effect of antiplatelet therapy following coronary artery bypass grafting is associated with reduced graft patency. We sought to evaluate the impact of hybrid coronary revascularization on the effect of aspirin and clopidogrel and whether high baseline platelet aggregation, high postoperative levels of platelet turnover, and acute-phase response may contribute to the effect. Methods: We prospectively studied platelet aggregation (VerifyNow and Multiplate Analyzer), platelet turnover (immature platelets, mean platelet volume, and thrombopoietin), and acute-phase reactants (C-reactive protein, von Willebrand factor, and coagulation factor VIII) in 40 patients undergoing elective hybrid coronary revascularization (off-pump surgical revascularization through J-hemisternotomy followed by percutaneous coronary intervention [PCI]). Preoperative blood samples on- and off-aspirin were compared with blood samples obtained postoperatively, following PCI when dual antiplatelet therapy had been initiated and at 1-year follow-up. Results: The antiplatelet effect of aspirin was significantly reduced in the early postoperative period as measured by VerifyNow Aspirin but not by Multiplate ASPI test. The antiplatelet effect of clopidogrel was significantly reduced following PCI as measured by VerifyNow P2Y12 and Multiplate ADP test. The level of baseline aggregation did not predict the antiplatelet effect of aspirin or clopidogrel, and no association was found between platelet aggregation and postoperative platelet turnover or acute-phase reaction. Conclusions: A transient reduction in the antiplatelet effect of aspirin and clopidogrel was observed after hybrid coronary revascularization despite limited surgical trauma and off-pump technique. Neither baseline platelet aggregation nor postoperatively increased platelet turnover and acute-phase response could explain this finding. ClinicalTrials.gov identifier, NCT02293928.