bluuebunny/crossref_metadata_2025_split huggingface dataset:
Boron Oxides, Boric Acid, and Borates
Authors: Schubert David 
Published: January 2015

AbstractNearly always occurring bonded to oxygen, boron is naturally present everywhere on the earth, including in rocks, soils, and natural waters. However, large borate mineral deposits are rare. Only a few of the more than 200 known borate minerals, including borax, kernite, ulexite, and colemanite, are primary sources of industrial borates. These minerals are extracted in California and Turkey, and to a lesser extent in South America, Russia and Asia.Major industrial borate products include boric acid, borax pentahydrate, borax decahydrate, anhydrous borax, and boric oxide. Beneficiated colemanite and ulexite ores are also used in some applications. Specialized industrial borates include sodium, potassium, ammonium, barium, and zinc borates. Physical properties and nomenclature systems of borates are reviewed.Borates have many industrial uses. The largest one is the manufacture of glass, including energy saving insulation fiberglass and borosilicate glasses. Other uses include the manufacture of ceramics, building products, house wares, consumer electronics, fertilizers, alloys, magnets, lubricants, and adhesives.Boron is required by all plants and is necessary for growth and fruiting. For this reason, boron fertilizers are routinely applied throughout the world where soils are deficient in order to improve crop yields. Boron at high levels can also inhibit bacteria, decay fungi, and insects, providing the basis for their use in the protection of wood‐based building products. Environmental, health, and safety aspects of borates are reviewed.
Microbial Transformations
Authors: Michels Peter C. , Khmelnitsky Yuri L. , Rich Joseph O. 
Published: January 2015

AbstractPractical applications of microbial biotransformations to organic synthesis of a wide range of important commercial chemicals is reviewed. Specific examples of recent advances in industrial applications of biocatalysis and drug metabolite production are highlighted. Major types of chemical reactions efficiently catalyzed by microbial catalysts are summarized and reviewed, with specific examples of biotransformations applied for important synthetic processes. An emphasis is given to specific practical aspects of conducting microbial transformations, providing guidelines for successful technical implementation of synthetic reactions catalyzed by microorganisms. Covered topics include selection and handling of microbial catalysts, miniaturization and automation of microbial culture screening, and application of modern experimental techniques for analysis of microbial reactions. Additional focus is placed on formulation of microbial reaction systems to achieve optimal performance and yields, and general approaches to biotransformation scale‐up.
Microarrays
Authors: Taylor Richard F. 
Published: January 2015

AbstractMicroarrays are analytical devices that utilize a platform technology for the performance of hundreds to thousands of individual reactions or assays simultaneously. These include nucleic acid probe enzyme, and immunoassays for the detection and analysis of DNA, RNA, proteins, polysaccharides, and lipids; and to study the interactions of biomolecules with drugs and with other bioactive agents. The basic components of a microarray are the substrate, active layer containing receptors, and the detection probe. Microarrays can be characterized using a number of parameters. These include density, regularity, and receptor purity and reactivity. Fabrication methods are detailed and included is information on the various substrates used and substrate activation, array spotting, andin situsynthesis. Applications discussed in detail include genomic analysis, expression analysis, and diagnostics. Micorarrays are complex to use and costly to manufacture, but this will change within the next ten years with increasing applications.
Dependence of plasmaspheric hiss on solar wind parameters and geomagnetic activity and modeling of its global distribution
Authors: Kim Kyung‐Chan , Lee Dae‐Young , Shprits Yuri 
Published: January 2015

AbstractAccurate knowledge of the global distribution of plasmaspheric hiss is essential for the radiation belt modeling because it provides a direct link to understanding the radiation belt loss in the slot region. In this paper, we study the dependence of hiss activity on solar wind parameters and geomagnetic activity indices using Time History of Events and Macroscale Interactions during Substorms hiss measurements made from 1 July 2008 to 30 June 2012 based on a correlation analysis. We find that hiss amplitudes are well correlated with the preceding solar wind speedVSW, interplanetary magnetic field (IMF)BZ, and interplanetary electric field (IEF)EYwith delay times of 5–6 h forVSWand 3–4 h for IMFBZand IEFEY, while the best correlation with the geomagnetic indices,AE,Kp, andSYM‐H, occurs at a delay time of 2–3 h forAEandSYM‐Hand 3–4 h forKp. Of the solar wind parameters, the dawn‐to‐dusk component of IEFEYyields the best correlation with the variation of hiss wave. More interestingly, the global distribution of hiss waves shows a significant dependence on theVSWand IMFBZ: the most intense hiss region tends to occur at prenoon sector for a more southward IMFBZ, while the tendency is opposite with increasingVSW. This implies different origins of hiss activity. Also, we employ an artificial neural network technique to develop models of the global distribution of hiss amplitudes based on the solar wind parameters and geomagnetic indices. The solely solar wind parameter‐based model generally results in a higher correlation between the measured and modeled hiss amplitudes than any other models based on the geomagnetic indices. Finally, we use the solar wind parameter‐based model to investigate hiss activity during storm events by distinguishing between coronal mass ejection‐driven storms and corotating interaction region‐driven storms. The result shows that in spite of the differences in the behavior of solar wind parameters between the two storm groups, the different types of storms lead to the similar evolution of hiss waves in overall appearance even though the detailed behavior of hiss activations are different.
Stable “Waterbelt” climates controlled by tropical ocean heat transport: A nonlinear coupled climate mechanism of relevance to Snowball Earth
Authors: Rose Brian E. J. 
Published: January 2015

AbstractOngoing controversy about Neoproterozoic Snowball Earth events motivates a theoretical study of stability and hysteresis properties of very cold climates. A coupled atmosphere‐ocean‐sea ice general circulation model (GCM) has four stable equilibria ranging from 0% to 100% ice cover, including a “Waterbelt” state with tropical sea ice. All four states are found at present‐day insolation and greenhouse gas levels and with two idealized ocean basin configurations. The Waterbelt is stabilized against albedo feedback by intense but narrow wind‐driven ocean overturning cells that deliver roughly 100 W m−2 heating to the ice edges. This requires three‐way feedback between winds, ocean circulation, and ice extent in which circulation is shifted equatorward, following the baroclinicity at the ice margins. The thermocline is much shallower and outcrops in the tropics. Sea ice is snow‐covered everywhere and has a minuscule seasonal cycle. The Waterbelt state spans a 46 W m−2 range in solar constant, has a significant hysteresis, and permits near‐freezing equatorial surface temperatures. Additional context is provided by a slab ocean GCM and a diffusive energy balance model, both with prescribed ocean heat transport (OHT). Unlike the fully coupled model, these support no more than one stable ice margin, the position of which is slaved to regions of rapid poleward decrease in OHT convergence. Wide ranges of different climates (including the stable Waterbelt) are found by varying the magnitude and spatial structure of OHT in both models. Some thermodynamic arguments for the sensitivity of climate, and ice extent to OHT are presented.
Quantifying the impacts of an updated global dimethyl sulfide climatology on cloud microphysics and aerosol radiative forcing
Authors: Mahajan Anoop S. , Fadnavis Suvarna , Thomas Manu A. , Pozzoli Luca , Gupta Smrati , Royer Sarah‐Jeanne , Saiz‐Lopez Alfonso , Simó Rafel 
Published: January 2015

AbstractOne of the critical parameters in assessing the global impacts of dimethyl sulfide (DMS) on cloud properties and the radiation budget is the estimation of phytoplankton‐induced ocean emissions, which are derived from prescribed, climatological surface seawater DMS concentrations. The most widely used global ocean DMS climatology was published 15 years ago and has recently been updated using a much larger database of observations. The updated climatology displays significant differences in terms of the global distribution and regional monthly averages of sea surface DMS. In this study, we use the ECHAM5‐HAMMOZ aerosol‐chemistry‐climate general circulation model to quantify the influence of the updated DMS climatology in computed atmospheric properties, namely, the spatial and temporal distributions of atmospheric DMS concentration, sulfuric acid concentration, sulfate aerosols, number of activated aerosols, cloud droplet number concentration, and the aerosol radiative forcing at the top of the atmosphere. Significant differences are observed for all the modeled variables. Comparison with observations of atmospheric DMS and total sulfate also shows that in places with large DMS emissions, the updated climatology shows a better match with the observations. This highlights the importance of using the updated climatology for projecting future impacts of oceanic DMS emissions, especially considering that the relative importance of the natural sulfur fluxes is likely to increase due to legislation to “clean up” anthropogenic emissions. The largest estimated differences are in the Southern Ocean, Indian Ocean, and parts of the Pacific Ocean, where the climatologies differ in seasonal concentrations over large geographical areas. The model results also indicate that the former DMS climatology underestimated the effect of DMS on the globally averaged annual aerosol radiative forcing at the top of the atmosphere by about 20%.
Chlorophyll a reconstruction from in situ measurements: 1. Method description
Authors: Fründt B. , Dippner J. W. , Waniek J. J. 
Published: January 2015

AbstractUnderstanding the development of primary production is essential for projections of the global carbon cycle in the context of climate change. A chlorophyll a hindcast that serves as a primary production indicator was obtained by fitting in situ measurements of nitrate, chlorophyll a, and temperature. The resulting fitting functions were adapted to a modeled temperature field. The method was applied to observations from the Madeira Basin, in the northeastern part of the oligotrophic North Atlantic Subtropical Gyre and yielded a chlorophyll a field from 1989 to 2008 with a monthly resolution validated with remotely measured surface chlorophyll a data by SeaWiFS. The chlorophyll a hindcast determined with our method resolved the seasonal and interannual variability in the phytoplankton biomass of the euphotic zone as well as the deep chlorophyll maximum. Moreover, it will allow estimation of carbon uptake over long time scales.
Mechanisms of convective cloud organization by cold pools over tropical warm ocean during the AMIE/DYNAMO field campaign
Authors: Feng Zhe , Hagos Samson , Rowe Angela K. , Burleyson Casey D. , Martini Matus N. , de Szoeke Simon P. 
Published: January 2015

AbstractThis paper investigates the mechanisms of convective cloud organization by precipitation‐driven cold pools over the warm tropical Indian Ocean during the 2011 Atmospheric Radiation Measurement (ARM) Madden‐Julian Oscillation (MJO) Investigation Experiment/Dynamics of the MJO (AMIE/DYNAMO) field campaign. A high‐resolution regional model simulation is performed using the Weather Research and Forecasting model during the transition from suppressed to active phases of the November 2011 MJO. The simulated cold pool lifetimes, spatial extent, and thermodynamic properties agree well with the radar and ship‐borne observations from the field campaign. The thermodynamic and dynamic structures of the outflow boundaries of isolated and intersecting cold pools in the simulation and the associated secondary cloud populations are examined. Intersecting cold pools last more than twice as long, are twice as large, 41% more intense (measured with buoyancy), and 62% deeper than isolated cold pools. Consequently, intersecting cold pools trigger 73% more convection than do isolated ones. This is due to stronger outflows that enhance secondary updraft velocities by up to 45%. However, cold pool‐triggered convective clouds grow into deep convection not because of the stronger secondary updrafts at cloud base, but rather due to closer spacing (aggregation) between clouds and larger cloud clusters that form along the cold pool boundaries when they intersect. The close spacing of large clouds moistens the local environment and reduces entrainment drying, increasing the probability that the clouds further develop into deep convection. Implications for the design of future convective parameterization with cold pool‐modulated entrainment rates are discussed.
Variational Lagrangian data assimilation in open channel networks
Authors: Wu Qingfang , Tinka Andrew , Weekly Kevin , Beard Jonathan , Bayen Alexandre M. 
Published: January 2015

AbstractThis article presents a data assimilation method in a tidal system, where data from both Lagrangian drifters and Eulerian flow sensors were fused to estimate water velocity. The system is modeled by first‐order, hyperbolic partial differential equations subject to periodic forcing. The estimation problem can then be formulated as the minimization of the difference between the observed variables and model outputs, and eventually provide the velocity and water stage of the hydrodynamic system. The governing equations are linearized and discretized using an implicit discretization scheme, resulting in linear equality constraints in the optimization program. Thus, the flow estimation can be formed as an optimization problem and efficiently solved. The effectiveness of the proposed method was substantiated by a large‐scale field experiment in the Sacramento‐San Joaquin River Delta in California. A fleet of 100 sensors developed at the University of California, Berkeley, were deployed in Walnut Grove, CA, to collect a set of Lagrangian data, a time series of positions as the sensors moved through the water. Measurements were also taken from Eulerian sensors in the region, provided by the United States Geological Survey. It is shown that the proposed method can effectively integrate Lagrangian and Eulerian measurement data, resulting in a suited estimation of the flow variables within the hydraulic system.
Probabilistic precipitation rate estimates with ground‐based radar networks
Authors: Kirstetter Pierre‐Emmanuel , Gourley Jonathan J. , Hong Yang , Zhang Jian , Moazamigoodarzi Saber , Langston Carrie , Arthur Ami 
Published: January 2015

AbstractThe uncertainty structure of radar quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales near the radar measurement scale. By using the WSR‐88D radar network and gauge data sets across the conterminous US, an investigation of this subject has been carried out within the framework of the NOAA/NSSL ground radar‐based Multi‐Radar Multi‐Sensor (MRMS) QPE system. A new method is proposed and called PRORATE for probabilistic QPE using radar observations of rate and typology estimates. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between radar reflectivity and the corresponding “true” precipitation. The model acknowledges the uncertainty arising from many factors operative at the radar measurement scale and from the correction algorithm. Ensembles of reflectivity‐to‐precipitation rate relationships accounting explicitly for precipitation typology were derived at a 5 min/1 km scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and type. The model components were estimated on the basis of a 1 year long data sample over the CONUS. This PQPE model provides the basis for precipitation probability maps and the generation of radar precipitation ensembles. Maps of the precipitation exceedance probability for specific thresholds (e.g., precipitation return periods) are computed. Precipitation probability maps are accumulated to the hourly time scale and compare favorably to the deterministic QPE. As an essential property of precipitation, the impact of the temporal correlation on the hourly accumulation is examined. This approach to PQPE can readily apply to other systems including space‐based passive and active sensor algorithms.
North American precipitation isotope (δ18O) zones revealed in time series modeling across Canada and northern United States
Authors: Delavau C. , Chun K. P. , Stadnyk T. , Birks S. J. , Welker J. M. 
Published: January 2015

AbstractDelineating spatial patterns of precipitation isotopes (“isoscapes”) is becoming increasingly important to understand the processes governing the modern water isotope cycle and their application to migration forensics, climate proxy interpretation, and ecohydrology of terrestrial systems. However, the extent to which these patterns can be empirically predicted across Canada and the northern United States has not been fully articulated, in part due to a lack of time series precipitation isotope data for major regions of North America. In this study, we use multiple linear regressions of CNIP, GNIP, and USNIP observations alongside climatological variables, teleconnection indices, and geographic indicators to create empirical models that predict the δ18O of monthly precipitation (δ18Oppt) across Canada and the northern United States. Five regionalization approaches are used to separate the study domain into isotope zones to explore the effect of spatial grouping on model performance. Stepwise regression‐derived parameterizations quantified by permutation testing indicate the significance of precipitable water content and latitude as predictor variables. Within the Canadian Arctic and eastern portion of the study domain, models from all regionalizations capture the interannual and intraannual variability of δ18Oppt. The Pacific coast and northwestern portions of the study domain show less agreement between models and poorer model performance, resulting in higher uncertainty in simulations throughout these regions. Long‐term annual average δ18Oppt isoscapes are generated, highlighting the uncertainty in the regionalization approach as it compounds over time. Additionally, monthly time series simulations are presented at various locations, and model structure uncertainty and 90% bootstrapped prediction bounds are detailed for these predictions.
Estimating freshwater flows from tidally affected hydrographic data
Authors: Pagendam D. E. , Percival D. B. 
Published: January 2015

AbstractDetiding end‐of‐catchment flow data are an important step in determining the total volumes of freshwater (and associated pollutant loads) entering the ocean. We examine three approaches for separating freshwater and tidal flows from tidally affected data: (i) a simple low‐pass Butterworth filter (BWF); (ii) a robust, harmonic analysis with Kalman smoothing (RoHAKS) which is a novel approach introduced in this paper; and (iii) dynamic harmonic regression (DHR). Using hydrographic data collected in the Logan River, Australia, over a period of 452 days, we judge the accuracy of the three methods based on three criteria: consistency of freshwater flows with upstream gauges; consistency of total discharge volumes with the raw data over the event; and minimal upstream flow. A simulation experiment shows that RoHAKS outperforms both BWF and DHR on a number of criteria. In addition, RoHAKS enjoys a computational advantage over DHR in speed and use of freely available software.
Evaluating observational methods to quantify snow duration under diverse forest canopies
Authors: Dickerson‐Lange Susan E. , Lutz James A. , Martin Kael A. , Raleigh Mark S. , Gersonde Rolf , Lundquist Jessica D. 
Published: January 2015

AbstractForests cover almost 40% of the seasonally snow‐covered regions in North America. However, operational snow networks are located primarily in forest clearings, and optical remote sensing cannot see through tree canopies to detect forest snowpack. Due to the complex influence of the forest on snowpack duration, ground observations in forests are essential. We therefore consider the effectiveness of different strategies to observe snow‐covered area under forests. At our study location in the Pacific Northwest, we simultaneously deployed fiber‐optic cable, stand‐alone ground temperature sensors, and time‐lapse digital cameras in three diverse forest treatments: control second‐growth forest, thinned forest, and forest gaps (one tree height in diameter). We derived fractional snow‐covered area and snow duration metrics from the colocated instruments to assess optimal spatial resolution and sampling configuration, and snow duration differences between forest treatments. The fiber‐optic cable and the cameras indicated that mean snow duration was 8 days longer in the gap plots than in the control plots (p < 0.001). We conducted Monte Carlo experiments for observing mean snow duration in a 40 m forest plot, and found the 95% confidence interval was ±5 days for 10 m spacing between instruments and ±3 days for 6 m spacing. We further tested the representativeness of sampling one plot per treatment group by observing snow duration across replicated forest plots at the same elevation, and at a set of forest plots 250 m higher. Relative relationships between snow duration in the forest treatments are consistent between replicated plots, elevation, and two winters of data.