Aerobee NASA 4.95 GS Meteorological data report, 20 May 1966
Ballistic factors, balloon measured wind data, and impact predictions for Aerobee rocket launch
BALLISTICS↗
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Characterization and growth of epitaxial layers of Gs exhibiting high resistivity for ionic implantation
Either classical or low temperature epitaxial growth techniques can be used to control the deposition of buffer layers of GaAs on semiconducting substrates and to obtain the resistivity and purity desired. Techniques developed to study, as a function of thickness, the evolution of mobilities by photoHall, and the spectroscopy of shallow and deep centers by cathodoluminescence and current transients reveal one very pure layer of medium resistivity and high mobility, and another "dead layer" of elevated resistivity far from the surface. The highly resistive layer remains pure over several microns, which appears interesting for implantation.
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The NASA competitive placement plan for positions GS-15 and below (including trades and labor positions)
This plan provides the framework for selection based on merit from among the best qualified candidates available. Selections will be made without regard to political, religious, or labor organization affiliation or nonaffiliation, marital status, race, color, sex, national origin, nondisqualifying disability, or age. This plan does not guarantee promotion but rather ensures that all qualified available candidates receive fair and equitable consideration for positions filled under these competitive procedures. Announcing a vacancy under this plan is only one method of locating applicants for a position and can be used in conjunction with other methods. Subject to applicable law and regulation, selection of an individual to fill a position is the decision of management, as is the decision as to the method(s) to be used in identifying candidates. This plan is applicable to all NASA Installations. It covers all positions in the competitive service at (and below) the GS/GM-15 level (including all trades and labor positions), except positions in the Office of the Inspector General. The requirements herein are not intended to, nor should they be construed to limit in any way, the independent personnel authority of the Inspector General under the Inspector General Act, as Amended.
Source record↗
Outbursts from the Transient X-Ray Pulsar Cep X-4 (GS 2138+56)
Cep X-4 was discovered with the Orbiting Solar Observatory (OSO) 7 Satellite in 1973 June-July, but no pulsations were detected. In March 1988, an additional outburst was observed with Gingaq. Pulsations at a period of 66.2490 +/- .0001 s were detected during a month long outburst which peaked at about 100 mCrab (1-20 keV) in early April 1988. The source apparently did not appear again until June 1993 when it was detected by Roentgen Satellite (ROSAT) and Burst and Transient Source Experiment (BATSE). Pulsations at a period of 66.2499 +/- .0007 s were detected by BATSE. The outburst lasted about two weeks and had a peak pulsed flux of 15-20 mCrab (20-50 keV). In July 1997, BATSE and the All-Sky Monitor (ASM) on Rossi X-ray Timing Explorer (RXTE) observed a new outburst from Cep X-4. Pulsations at a period of 66.2743 +/- 0.0005 s were detected by BATSE. This outburst lasted about 2 weeks and peaked at a pulsed flux of about 10-15 mCrab (20-50 keV). Results of a search of BATSE data for additional outbursts will be presented. Pulse frequency and flux histories will be presented and compared to the flux history from the RXTE ASM. Implications of the apparent spin-down between outbursts will be discussed.
Wilson, Colleen A.↗
Algorithms for Autonomous GS Orbit Determination and Formation Flying
This final report for our study of autonomous Global Positioning System (GPS) satellite orbit determination comprises two sections. The first is the Ph.D. dissertation written by Michael C. Moreau entitled, "GPS Receiver Architecture for Autonomous Navigation in High Earth Orbits." Dr. Moreau's work was conducted under both this project and a NASA GSRP. His dissertation describes the key design features of a receiver specifically designed for autonomous operation in high earth orbits (HEO). He focused on the implementation and testing of these features for the GSFC PiVoT receiver. The second part is a memo describing a robust method for autonomous initialization of the orbit estimate given very little a priori information and sparse measurements. This is a key piece missing in the design of receivers for HEO.
Moreau, Michael C.↗
Characterization of the GS Yuasa 134 Ah Cell Thermal Runaway Events with Large Format Fractional Thermal Runaway Calorimetry (L-FTRC)
Final results for a test series conducted with the large format fractional thermal runaway calorimeter (L-FTRC) capable of measuring the total energy release and fractional energy release for Li-ion cells that have greater than 100 Ah capacities.
Lithium-ion battery; thermal runaway; calorimetry;↗
A Machine Learning Approach to Improve Air Traffic Management Initiatives
Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.
Farzan Masrour Shalmani↗
Nonlinear Gulf Stream Interaction with the Deep Western Boundary Current System: Observations and a Numerical Simulation
Gulf Stream (GS) separation near its observed Cape Hatteras (CH) separation location, and its ensuing path and dynamics, is a challenging ocean modeling problem. If a model GS separates much farther north than CH, then northward GS meanders, which pinch off warm core eddies (rings), are not possible or are strongly constrained by the Grand Banks shelfbreak. Cold core rings pinch off the southward GS meanders. The rings are often re-absorbed by the GS. The important warm core rings enhance heat exchange and, especially, affect the northern GS branch after GS bifurcation near the New England Seamount Chain. This northern branch gains heat by contact with the southern branch water upstream of bifurcation, and warms the Arctic Ocean and northern seas, thus playing a major role in ice dynamics, thermohaline circulation and possible global climate warming. These rings transport heat northward between the separated GS and shelf slope/Deep Western Boundary Current system (DWBC). This region has nearly level time mean isopycnals. The eddy heat transport convergence/divergence enhances the shelfbreak and GS front intensities and thus also increases watermass transformation. The fronts are maintained by warm advection by the Florida Current and cool advection by the DWBC. Thus, the GS interaction with the DWBC through the intermediate eddy field is climatologically important.
Dietrich, David E.↗
A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability
Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign and the 1998 First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). We investigate two related approaches to prediction of GS cloud properties from SGS variability: the critical saturation ratio method, and the critical relative humidity method. We find significant correlation between GS cloud water and SGS supersaturation when the critical saturation ratio is set at 100%, as well as a notable relationship between GS cloud water and the width of the SGS total water distribution. Critical relative humidity also compares well with GS cloud water. However, we also find that the assumptions of a static critical saturation ratio of 100% to be unrealistic, as well as a fixed SGS distribution width. Empirical calculations from the ARISE data show a large sensitivity of these SGS variables to GS relative humidity, and so a SGS parameterization allowing them to vary according to GS thermodynamic properties may result in more realistic GS cloud water values.
J. Brant Dodson↗
Topological and magnetic properties of a noncollinear spin state on a honeycomb lattice in a magnetic field
Here, this paper studies the topological and magnetic properties of a noncollinear spin state on a honeycomb lattice that evolves from coplanar to ferromagnetic with a magnetic field applied along the z axis. The coplanar state is stabilized by nearest-neighbor ferromagnetic interactions, single-ion anisotropy along z, and DzyaloshinskiiMoriya interactions between next-nearest-neighbor sites. Below the critical field H$_c$ that aligns the spins, the magnetic unit cell contains six sites and the spin dynamics contains six magnon modes. Although the classical energy is degenerate with respect to the twist angle φ between nearest-neighbor spins, the dependence of the free energy on φ at low temperatures is dominated by the magnon zero-point energy, which contains extremum at φ = πl/3 for integer l. The only unique ground states GS(φ) have l = 0 or 1. For H < H$'_c$, the zero-point energy has minima at even l and the ground state is GS(0); for H$'_c$ < H < H$'_c$, the zero-point energy has minima at odd l and the ground state is GS(π/3). In GS(0), the magnon density of states exhibits five distinct topological phases with increasing field associated with the opening and closing of energy gaps between two or three magnonic bands. While the Berry curvature vanishes for the coplanar φ = 0 phase in zero field, the Berry curvature and Chern numbers exhibit signatures of the five topological phases below H$'_c$. Whereas the Berry curvature and Chern number are sensitive to changes in the magnon density of states within GS(π/3), the inelastic spectrum S(k,ω) is sensitive to changes in the intensity of the magnon modes in the different magnetic phases GS(0) and GS(π/3) rather than the five topological phases within GS(π/3).
Fishman, Randy S. [Oak Ridge National Laboratory (↗
Analysis and Prediction of Weather Impacted Ground Stop Operations
When the air traffic demand is expected to exceed the available airport's capacity for a short period of time, Ground Stop (GS) operations are implemented by Federal Aviation Administration (FAA) Traffic Flow Management (TFM). The GS requires departing aircraft meeting specific criteria to remain on the ground to achieve reduced demands at the constrained destination airport until the end of the GS. This paper provides a high-level overview of the statistical distributions as well as causal factors for the GSs at the major airports in the United States. The GS's character, the weather impact on GSs, GS variations with delays, and the interaction between GSs and Ground Delay Programs (GDPs) at Newark Liberty International Airport (EWR) are investigated. The machine learning methods are used to generate classification models that map the historical airport weather forecast, schedule traffic, and other airport conditions to implemented GS/GDP operations and the models are evaluated using the cross-validations. This modeling approach produced promising results as it yielded an 85% overall classification accuracy to distinguish the implemented GS days from the normal days without GS and GDP operations and a 71% accuracy to differentiate the GS and GDP implemented days from the GDP only days.
Analysis↗
A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability
Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). Multiple models/reanalyses use SGS supersaturated total water (relative to GS saturation) as a threshold for partitioning available water for condensation, and indeed we find significant correlation between GS cloud water and SGS supersaturation. However, we also find that the assumption of a static threshold of 100% saturation to be unrealistic. Empirical calculations from the ARISE data show a large sensitivity of this threshold to GS relative humidity, and so a microphysical parameterization allowing the threshold to vary according to GS thermodynamic properties may result in more realistic GS cloud water values. Finally, to determine how sensitive the ARISE-derived results are to that particular campaign, we include additional data from the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) conducted in 1998. The inclusion of the second dataset will help with demonstrating the robustness of the results and their utility in improving the representation of Arctic clouds in models and reanalyses.
J Brant Dodson↗
A Path to Improving Simulated Properties of Low Clouds over the Beaufort Sea using Airborne In Situ Observations of Subgrid-Scale Variability
Arctic low clouds influence the evolution of the Arctic system through their effects on radiative fluxes, boundary layer mixing, stability, turbulence, humidity, and precipitation. Unfortunately, atmospheric models and retrospective analysis (reanalysis) products struggle to accurately simulate the occurrence and properties of low clouds in the Arctic. One of the main reasons for this problem are the possible unrealistic assumptions that models/reanalyses make about the subgrid-scale (SGS) variability of meteorological properties, as well as the relationship between SGS variability and grid-scale (GS) cloud properties. We utilize cloud and thermodynamic data of low level (primarily) liquid clouds collected from two aircraft campaigns conducted over the Beaufort Sea to better understand and characterize this problem. Examining data from the September 2014 Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) airborne campaign and the 1998 First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE)–Arctic Cloud Experiment (ACE) reveals that GS cloud water variability is closely related with SGS distribution of total water (i.e. water vapor + cloud water). We examine the influence of three assumed SGS parameters on the estimation of GS cloud water: the width of the SGS PDF of total water (ΔQT), the shape of the SGS PDF, and the critical saturation ratio (Scrit), which partitions the SGS PDF into water that is available or not for conversion to cloud water. Both Scrit and ΔQT influence predicted GS cloud water strongly. ARISE and FIRE-ACE disagree somewhat in the details, but both campaigns support the possibility that more realistic representations of these two parameters may lead to more realistic GS cloud water. In particular, both parameters are sensitive to GS relative humidity, and so we examine the effects of allowing these quantities to vary as an empirically-derived linear function of GS relative humidity. In contrast, the shape of the PDF has little effect on the predicted cloud water – a surprising result that merits additional investigation.
J Brant Dodson↗
Quasi-Periodic Oscillations in Black Hole Candidates as an Indicator of Transition Between Low and High States
By comparing positions on a spectral color-color diagram from 10 black hole candidates (BHCS) observed with Ginga (1354-64, 1826-24, 1630-47, LMC X-1, LMC X-3, GS 2000+25, GS 2023+33, GS 1124-68, Cyg X-1, and GX 339-4) with the observed broad-band noise (0.001- 64 Hz; BBN) and quasi-periodic oscillation (QPO) variability, we find that the so-called "Very High State" is spectrally intermediate to the Soft/High-State and Hard/Low-State. We find a transition point in spectral hardness where the dependence of the BHC QPO centroid frequency (of GS 1124-68 and GX 339-4) on spectral hardness switches from a correlation to an anti-correlation; where the BBN variability switches from High-State to Low-State; and where the spectral hardness of the QPO relative to that of the BBN variability is a maximum. This coincidence of changing behavior in both the QPO and the broad-band variability leads us to hypothesize that the QPO is due to interaction between the physical components which dominate the behaviors of BHCs when they occupy the Hard/Low and Soft/High States. We conclude that these QPO should be observed from BHCs during transition between these two states. Comparison with QPO and BBN behavior observed during the 1996 transition of Cyg X-1 supports this hypothesis. We also report 1-3 Hz QPO observed in GS 2000+25 and Cyg X-1 in the Hard/Low State, and we compare these to the QPO observed in GS 1124-68 and GX 339-4.
Rutledge, Robert E↗
Quasi-Periodic Oscillations in Black Hole Candidates as an Indicator of Transition Between Low and High States
By comparing positions on a spectral color-color diagram from 10 black hole candidates (BHCS) observed with Ginga (1354-64, 1826-24, 1630-47, LMC X-1, LMC X-3, GS 2000+25, GS 2023+33, GS 1124-68, Cyg X-1, and GX 339-4) with the observed broadband noise (BBN) (0.001-64 Hz) and quasi-periodic oscillation (QPO) variability, we find that the "very high state" is spectrally intermediate to the soft/high state and hard/low state. We find a transition point in spectral hardness where the dependence of the BHC QPO centroid frequency (of GS 1124-68 and GX 339-4) on spectral hardness switches from a correlation to an anticorrelation; where the BBN variability switches from high state to low state; and where the spectral hardness of the QPO relative to that of the BBN variability is a maximum. This coincidence of changing behavior in both the QPO and the broadband variability leads us to hypothesize that the QPO is due to interaction between the physical components which dominate the behaviors of BHCs when they occupy the hard/low and soft/high states. We conclude that these QPOs should be observed from BHCs during transition between these two states. Comparison with QPO and BBN behavior observed during the 1996 transition of Cyg X-1 supports this hypothesis. We also report 1-3 Hz QPOs observed in GS 2000+25 and Cyg X-1 in the hard/low state, and we compare these to the QPOs observed in GS 1124-68 and GX 339-4.
Rutledge, Robert E.↗
Stomatal conductance of lettuce grown under or exposed to different light qualities
BACKGROUND AND AIMS: The objective of this research was to examine the effects of differences in light spectrum on the stomatal conductance (Gs) and dry matter production of lettuce plants grown under a day/night cycle with different spectra, and also the effects on Gs of short-term exposure to different spectra. METHODS: Lettuce (Lactuca sativa) plants were grown with 6 h dark and 18 h light under four different spectra, red-blue (RB), red-blue-green (RBG), green (GF) and white (CWF), and Gs and plant growth were measured. KEY RESULTS AND CONCLUSIONS: Conductance of plants grown for 23 d under CWF rose rapidly on illumination to a maximum in the middle of the light period, then decreased again before the dark period when it was minimal. However, the maximum was smaller in plants grown under RB, RGB and GF. This demonstrates that spectral quality during growth affects the diurnal pattern of stomatal conductance. Although Gs was smaller in plants grown under RGB than CWF, dry mass accumulation was greater, suggesting that Gs did not limit carbon assimilation under these spectral conditions. Temporarily changing the spectral quality of the plants grown for 23 d under CWF, affected stomatal responses reversibly, confirming studies on epidermal strips. This study provides new information showing that Gs is responsive to spectral quality during growth and, in the short-term, is not directly coupled to dry matter accumulation.
Light↗
Validation of Globsnow-2 Snow Water Equivalent Over Eastern Canada
In Qubec, Eastern Canada, snowmelt runoff contributes more than 30% of the annual energy reserve for hydroelectricity production, and uncertainties in annual maximum snow water equivalent (SWE) over the region are one of the main constraints for improved hydrological forecasting. Current satellite-based methods for mapping SWE over Qubec's main hydropower basins do not meet Hydro-Qubec operational requirements for SWE accuracies with less than 15% error. This paper assesses the accuracy of the GlobSnow-2 (GS-2) SWE product, which combines microwave satellite data and in situ measurements, for hydrological applications in Qubec. GS-2 SWE values for a 30-year period (1980 to 2009) were compared with space- and time-matched values from a comprehensive dataset of in situ SWE measurements (a total of 38,990 observations in Eastern Canada). The root mean square error (RMSE) of the GS-2 SWE product is 94.1+/- 20.3 mm, corresponding to an overall relative percentage error (RPE) of 35.9%. The main sources of uncertainty are wet and deep snow conditions (when SWE is higher than 150 mm), and forest cover type. However, compared to a typical stand-alone brightness temperature channel difference algorithm, the assimilation of surface information in the GS-2 algorithm clearly improves SWE accuracy by reducing the RPE by about 30%. Comparison of trends in annual mean and maximum SWE between surface observations and GS-2 over 1980-2009 showed agreement for increasing trends over southern Qubec, but less agreement on the sign and magnitude of trends over northern Qubec. Extended at a continental scale, the GS-2 SWE trends highlight a strong regional variability.
Snow water equivalent↗