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Comparison of results of a number of methods for measuring the stream energy content in a frozen, partially dissociated, high-speed gas stream of an arc-heated wind tunnel
Monthly Maps of sea surface height are constructed for the North Atlantic Ocean using TOPEX/Poseidon altimeter data. Mean sea surface height is reconstructed using a weighted combination of historical, hydrographic data and a synthetic mean obtained by fitting a Gaussian model of the Gulf Stream jet to altimeter data. The resultant mean shows increased resolution over the hydrographic mean, and incorporates recirculation information that is absent in the synthetic mean. Monthly maps, obtained by adding the mean field to altimeter sea surface height residuals, are used to derive a set of zonal indices that describe the annual cycle of meandering as well as position and strength of the Gulf Stream.
One of the most pernicious cause for spurious emission and performance degradation in space telemetry systems is the imperfect data stream at the input of the modulator. The imperfection of the data stream can be caused by impalance between -1s and +1s (unbalanced data) and/or by data asymmetry.
We discuss a type of intense magnetospheric/auroral activity that is not always substorms: High-Intensity, Long-Duration, Continuous AE Activity (HILDCAA) events, which occur during high speed solar wind streams. The high speed streams contain large-amplitude, nonlinear Alfvtn waves. Analyses of POLAR UV images, demonstrate that the AE increases/AL decreases in HILDCAAs are not always substorm expansion phases (although some substorms may occur). The associated auroral W energy deposition is throughout a continuous (360') auroral oval. During some image intervals, the dayside aurora is the most remarkable feature. Our hypothesis is that solar wind energy transfer from the solar wind to the magnetosphere/ionosphere is primarily directly driven due to the finite wavelength Alfv6n waves and the rapid dBz/dt variability.
NASA invests in professional coaching as a way to accelerate the development of its staff. The speaker shares one foundational human development model in coaching - the Six Streams - and applies it to the challenges that new scientists face. The speaker also describes how a new scientist can develop greater capabilities in the Six Streams so that they can become a more effective scientist and feel more satisfaction with their work.
Shortwave (SW) fluxes estimated from broadband radiometry rely on empirically gathered and hemispherically resolved fields of outgoing top-of-atmosphere (TOA) radiances. This study aims to provide more accurate and precise fields of TOA SW radiances reflected from clouds over ocean by introducing a novel semiphysical model predicting radiances per narrow sun-observer geometry. This model was statistically trained using CERES-measured radiances paired with MODIS-retrieved cloud parameters as well as reanalysis-based geophysical parameters. By using radiative transfer approximations as a framework to ingest the above parameters, the new approach incorporates cloud-top effective radius and above-cloud water vapor in addition to traditionally used cloud optical depth, cloud fraction, cloud phase, and surface wind speed. A two-stream cloud albedo – serving to statistically incorporate cloud optical thickness and cloud-top effective radius – and Cox–Munk ocean reflectance were used to describe an albedo over each CERES footprint. Effective-radius-dependent asymmetry parameters were obtained empirically and separately for each viewing-illumination geometry. A simple equation of radiative transfer, with this albedo and attenuating above-cloud water vapor as inputs, was used in its log-linear form to allow for statistical optimization. We identified the two-stream functional form that minimized radiance residuals calculated against CERES observations and outperformed the state-of-the-art approach for most observer geometries outside the sun-glint and solar zenith angles between 20 and 70∘, reducing the median SD of radiance residuals per solar geometry by up to 13.2 % for liquid clouds, 1.9 % for ice clouds, and 35.8 % for footprints containing both cloud phases. Geometries affected by sun glint (constituting between 10 % and 1 % of the discretized upward hemisphere for solar zenith angles of 20 and 70∘, respectively), however, often showed weaker performance when handled with the new approach and had increased residuals by as much as 60 % compared to the state-of-the-art approach. Overall, uncertainties were reduced for liquid-phase and mixed-phase footprints by 5.76 % and 10.81 %, respectively, while uncertainties for ice-phase footprints increased by 0.34 %. Tested for a variety of scenes, we further demonstrated the plausibility of scene-wise predicted radiance fields. This new approach may prove useful when employed in angular distribution models and may result in improved flux estimates, in particular dealing with clouds characterized by small or large droplet/crystal sizes.
The warm Gulf Stream sea surface temperatures (SSTs) strongly impact the evolution of winter clouds behind atmospheric cold fronts. Such cloud evolution remains challenging to model. The Gulf Stream is too wide within the ERA5 and MERRA2 reanalyses, affecting the turbulent surface fluxes. Known problems within the ERA5 boundary layer (too-dry and too-cool with too strong westerlies), ascertained primarily from ACTIVATE 2020 campaign aircraft dropsondes and secondarily from older buoy measurements, reinforce surface flux biases. In contrast, MERRA2 winter surface winds and air-sea temperature/humidity differences are slightly too weak, producing surface fluxes that are too low. Reanalyses boundary layer heights in the strongly-forced winter cold-air-outbreak regime are realistic, whereas late-summer quiescent stable boundary layers are too shallow. Nevertheless, the reanalysis biases are small, and reanalyses adequately support their use for initializing higher-resolution cloud process modeling studies of cold-air outbreaks.
This work demonstrates the progress made at Idaho National Laboratory in the neutronic modeling of neutron streaming in the TREAT reactor core. The streaming in the slotted elements is important because of its effect on the axial distribution and energy deposition in the experiment vehicle. Two methods implemented in Rattlesnake: 1) tensor diffusion coefficients and, 2) superhomogenization, yield much improved power distributions using a diffusion solution with homogenized fuel elements. The RMS difference in the power distribution decreases below 0.661% compared to a reference Monte Carlo simulation. The maximum differences are within 1.5% of this reference.
Large-scale land models assume unidirectional land-to-river hydrological interactions, without considering feedbacks between channels and land. Using a tested, physically based model with explicit multiway interactions between overland, channel, wetland, and groundwater flows, we assessed how the representation and properties of channels influence simulated land surface hydrologic, biogeochemical, and ecosystem dynamics. A zone near the channels where various fluxes and states are significantly influenced by the channels, referred to as the fan of influence (FoI) of channels, has been identified. We elucidated two mechanisms inducing the model-derived FoI: the base flow mechanism, in which incised, gaining streams lower the water table and induce more base flow, and the relatively more efficient conveyance of the channel network compared to overland flow. We systematically varied drainage density and grid resolution to quantify the size of the FoI, which is found to span a large fraction of the watershed (25-50%) for hydrologic variables including depth to water table and recharge, etc. The FoI is more pronounced with low-resolution simulations but remains noticeable in hyperresolution (25 m) subbasin simulations. The FoI and the channel influence on basin-average fluxes are also similar in simulations with alternative parameter sets. We found that high-order, entrenched streams cause larger FoI. In addition, removing the simulated channels has disproportionally large influence on modeled wetland areas and inundation duration, which has implications for coupled biogeochemical or ecological modeling. Our results suggest that explicit channel representation provides important feedbacks to land surface dynamics which should be considered in meso or large-scale simulations. Since grid refinement incurs prohibitive computational cost, subgrid channel parameterization has advantages in efficiency over grid-based representations that do not distinguish between overland flow and channel flow.
We revisit a classic ecohydrological study that showed streamside riparian trees in a semiarid mountain catchment did not use perennial stream water. The original study suggested that mature individuals of Acer negundo, Acer grandidentatum, and other species were dependent on water from “deeper strata,” possibly groundwater. We used a dual stable isotope approach (δ 18 O and δ 2 H) to further examine the water sources of these trees. We tested the hypothesis that groundwater was the main tree water source, but found that neither groundwater nor stream water matched the isotope composition of xylem water during two growing seasons. Soil water (0–1 m depth) was closest to and periodically overlapped with xylem water isotope composition, but overall, xylem water was isotopically enriched compared to all measured water sources. The “two water worlds” hypothesis postulates that soil water comprises isotopically distinct mobile and less mobile pools that do not mix, potentially explaining this disparity. We further hypothesized that isotopic effects during snowpack metamorphosis impart a distinct isotope signature to the less mobile soil water that supplies summer transpiration. Depth trends in water isotopes following snowmelt were consistent with the two water worlds hypothesis, but snow metamorphic isotope effects could not explain the highly enriched xylem water. Thus, the dual isotope approach did not unambiguously determine the water source(s) of these riparian trees. Further exploration of physical, geochemical, and biological mechanisms of water isotope fractionation and partitioning is necessary to resolve these data, highlighting critical challenges in the isotopic determination of plant water sources.
Nutrient spiraling is an important ecosystem process characterizing nutrient transport and uptake in streams. Various nutrient addition methods are used to estimate uptake metrics; however, uncertainty in the metrics is not often evaluated. A method was developed to quantify uncertainty in ambient and saturation nutrient uptake metrics estimated from saturating pulse nutrient additions (Tracer Additions for Spiraling Curve Characterization; TASCC). Using a Monte Carlo (MC) approach, the 95% confidence interval (CI) was estimated for ambient uptake lengths (S w-amb ) and maximum areal uptake rates (U max ) based on 100,000 datasets generated from each of four nitrogen and five phosphorous TASCC experiments conducted seasonally in a forest stream in eastern Tennessee, U.S.A. Uncertainty estimates from the MC approach were compared to the CIs estimated from ordinary least squares (OLS) and non-linear least squares (NLS) models used to calculate S w-amb and U max , respectively, from the TASCC method. The CIs for Sw-amb and Umax were large, but were not consistently larger using the MC method. Despite the large CIs, significant differences (based on nonoverlapping CIs) in nutrient metrics among seasons were found with more significant differences using the OLS/NLS vs. the MC method. Lastly, we suggest that the MC approach is a robust way to estimate uncertainty, as the calculation of S w-amb and U max violates assumptions of OLS/NLS while the MC approach is free of these assumptions. The MC approach can be applied to other ecosystem metrics that are calculated from multiple parameters, providing a more robust estimate of these metrics and their associated uncertainties.
Abstract Advancing our understanding of dissolved organic matter (DOM) chemistry in aquatic systems necessitates the integration of data streams from multiple analytical platforms. Some measurements require pretreatment with solid phase extraction (SPE), while others are performed directly on whole water samples. Evidence has suggested that SPE will be biased against select DOM fractions, leading to concerns over the ability to establish data linkages across platforms with variable needs for SPE pretreatment, such as those from optical measurements and those that provide high‐resolution molecular information. Here, we directly addressed this concern by assessing the impact of SPE on DOM optical properties through excitation–emission matrices with parallel factor analysis (PARAFAC) for 47 samples across a stream network within a single watershed reflective of variable DOM sources. PARAFAC data was further paired with molecular information obtained by Fourier transform ion cyclotron resonance mass spectrometry (FTICR‐MS). A comparison of PARAFAC models first revealed no systematic qualitative differences in major components between whole water DOM and DOM isolated by SPE (SPE‐DOM); however, quantitative biases against select components were observed. Further linkages with FTICR‐MS data revealed that the molecular fingerprint associated with each PARAFAC component was consistent between the whole water DOM and SPE‐DOM. Our results suggest that bulk scale linkages across these analytical platforms could be inferred irrespective of the observed quantitative biases resulting from SPE for samples within this example watershed. This work represents a key step toward the systematic evaluation of linkages between optical and high‐resolution mass spectrometry datasets in freshwater lotic environments.
Abstract We analyzed and compared productivity and survival rates of populations of stream-type Chinook Salmon Oncorhynchus tshawytscha from the upper and middle ranges of their distribution in the Columbia River basin. These two groups of populations undergo vastly different exposures during migration through the Federal Columbia River Power System (FCRPS). Declines of the Snake River populations, listed as threatened under the U.S. Endangered Species Act, have been associated with the development and operation of the FCRPS. In contrast, John Day River stream-type Chinook Salmon populations, which were less affected by the FCRPS, have declined to a lesser extent and are not listed. Smolt-to-adult survival rates (SARs) accounted for a majority of the variation in life cycle survival rates of Snake River Chinook Salmon. Productivity declined to 13% and 44% of historical productivity levels for Snake River and John Day River populations, respectively. A synthesis of previous studies contrasting anthropogenic impacts between the two regions supports the conclusion that FCRPS impacts explain the large difference in population productivity. Our results suggest that SARs of 4% would result in an expected productivity of up to 70% of historical levels (a SAR level consistent with regional restoration objectives). The SARs have been shown to be highly influenced by conditions within the FCRPS (e.g., water velocity and passage through dam powerhouses). Marine conditions also influence SARs; however, meaningful management actions are only available to affect conditions within the FCRPS. Given the importance of SARs to overall life cycle productivity, recovery and restoration strategies need to prioritize actions that have potential to substantially increase SARs by addressing the significant impacts of main-stem dams. This study highlights the importance of considering river management options in the face of increasingly variable and warming ocean conditions.
ABSTRACT Under the United States Clean Water Act, states are required to periodically assess state waters to determine compliance with water quality criteria (including temperature) and then to develop total maximum daily loads (TMDLs) for impaired waters as necessary to bring them into compliance. We compared the performance of mechanistic stream temperature models (HeatSource, QUAL2K, and QUAL2Kw) applied to the mainstem of three TMDL watersheds (Middle Fork John Day, OR; Wind River, WA; South Fork Nooksack, WA) with that of spatial stream network (SSN) models applied to the full watersheds and used these to evaluate the potential effectiveness of restoration strategies. SSN models performed well with slightly lesser accuracy (RMSE = 0.47–0.87) for mainstem predictions than mechanistic models (RMSE = 0.4) but provided additional benefits to inform management, including information on spatial and temporal heterogeneity of restoration effectiveness throughout the watershed. Of the four scenarios considered (restoration of riparian zones to potential natural vegetation, channel narrowing, increasing flow by restricting irrigation withdrawals, and combined applications), riparian zone restoration was consistently the most effective in reducing temperatures at the outlet, mainstem, and throughout the watersheds. Predicted restoration effectiveness for thermal regimes varied significantly both within and among watersheds. A focus on water quality criteria exceedance only at the watershed outlet or along the mainstem reach can obscure knowledge of restoration potential for fish habitat in tributaries and headwaters, potential for creation of thermal refuge areas along the mainstem critical for maintaining migration corridors, and thermal regime heterogeneity across space and time.
Summary There are many data sources that produce large volumes of data. The Big Data nature requires new distributed processing approaches to extract the valuable information. Real‐time sentiment analysis is one of the most demanding research areas that requires powerful Big Data analytics tools such as Spark. Prior literature survey work has shown that, though there are many conventional sentiment analysis researches, there are only few works realizing sentiment analysis in real time. One major point that affects the quality of real‐time sentiment analysis is the confidence of the generated data. In more clear terms, it is a valuable research question to determine whether the owner that generates sentiment is genuine or not. Since data generated by fake personalities may decrease accuracy of the outcome, a smart/intelligent service that can identify the source of data is one of the key points in the analysis. In this context, we include a fake account detection service to the proposed framework. Both sentiment analysis and fake account detection systems are trained and tested using Naïve Bayes model from Apache Spark's machine learning library. The developed system consists of four integrated software components, ie, (i) machine learning and streaming service for sentiment prediction, (ii) a Twitter streaming service to retrieve tweets, (iii) a Twitter fake account detection service to assess the owner of the retrieved tweet, and (iv) a real‐time reporting and dashboard component to visualize the results of sentiment analysis. The sentiment classification performances of the system for offline and real‐time modes are 86.77% and 80.93%, respectively.
A simple closed contour object identification scheme has been applied to the zonal mean monthly mean zonal wind fields from nine global reanalysis data sets for 31 years of the satellite era (1979–2009) to identify objects corresponding to westerly jet streams. The results cluster naturally into six individual jet streams but only the mid-latitude upper-tropospheric jets are considered here. The time series of the jet properties from all reanalyses are decomposed into seasonal means and anomalies, and correlations between variables are evaluated, with the aim of identifying robust features which can form the basis of evaluation metrics for climate model simulations of the twentieth century. There is substantial agreement between all the reanalyses for all jet properties although there are some systematic differences with particular data sets. Some of the results from the object identification applied to the reanalyses are used in a simple example of a model evaluation score for the zonal mean jet seasonal cycle.
Radioiodine interferes significantly in short-lived and low yield fission product detection by radiometric means. A simple method has been developed for removing radiotellurium and its radioiodine daughters from process HEU analyte streams. The method uses metallic copper relying on the electrochemical behavior between Cu and Te to deposit Te on the surface of Cu packed chromatography columns. Method development on stable metal samples exhibited >93% Te removal from high HCl conditions (>5 M). The method was applied to processed HEU streams providing > 90% Te removal (examining 129mTe and 132Te), and providing > 99.9% radioiodine removal (examining 131I and 132I).
Zero-gap microbial electrolysis cells (MECs) have demonstrated large current and hydrogen production rates from defined substrates in synthetic media, but operation with real waste streams has yet to be proved. This study evaluated the performance and 30-days stability of zero-gap MECs operated with effluent from a single-stage anaerobic digester. The system achieved a maximum current density of 8.8 ± 0.3 A/m 2 with a hydrogen production rate of 32 ± 6 L/L-d, and during 30 days of continuous operation, sustained an average current density of 7 ± 2 A/m 2 and a hydrogen production rate of 20.8 ± 0.2 L/L-d. Carbonate precipitation was identified as a major challenge to long-term stability, and mild acid washing effectively mitigated its adverse effects. The low buffer capacity of the effluent was primarily limiting performance. Furthermore, these findings underscore the significant impact of wastewater chemistry on MEC operation and validate the feasibility of utilizing real waste streams as viable feedstocks for biohydrogen production in zero-gap configurations.