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In this work, we propose MD Loader, a hybrid in-memory data loader for distributed deep neural networks. MDLoader introduces a model-driven performance estimator to automatically switch between one-sided and collective communication at runtime.
This submission aimed to assess differences in short-term step warming manipulations and long-term chronic response to climate change in Alaskan ecosystems. Briefly, climate warming is occurring fastest at high latitudes. Based on short-term field experiments, this warming is projected to stimulate soil organic matter decomposition, and promote a positive feedback to climate change. We show here that the tightly coupled, nonlinear nature of high-latitude ecosystems implies that short-term (< 10 year) warming experiments produce emergent ecosystem carbon stock temperature sensitivities inconsistent with emergent multi-decadal responses. We first demonstrate that a well-tested mechanistic ecosystem model accurately represents observed carbon cycle and active layer depth responses to short-term summer warming in four diverse Alaskan sites. We then show that short-term warming manipulations do not capture the non-linear, long-term dynamics of vegetation, and thereby soil organic matter, that occur in response to thermal, hydrological, and nutrient transformations belowground. Our results demonstrate significant spatial heterogeneity in multi-decadal Arctic carbon cycle trajectories and argue for more mechanistic models to improve predictive capabilities.The model used in the current study is available publicly (https://github.com/jinyun1tang/ECOSYS), and the current submission contains the python/ matlab codes for analyzing output from the model (includng a readme file to explain the codes). The benchmark data, also enclosed, was collected from a range of published and publicly available sources (extracted using GRABIT: https://www.mathworks.com/matlabcentral/fileexchange/7173-grabit). These sources describe warming induced changes in tundra/ boreal ecosystems.
The primary objective of the three-day workshop on results from the Data Assimilation Office (DAO) five-year assimilation was to provide timely feedback from the data users concerning the strengths and weaknesses of version 1 of the Goddard Earth Observing System (GEOS-1) assimilated products. A second objective was to assess user satisfaction with the current methods of data access and retrieval. There were a total of 49 presentations, with about half (23) of the presentations from scientists from outside of Goddard. The first two days were devoted to applications of data: studies of the energy diagnostics, precipitation and diabatic heating, hydrological modeling and moisture transport, cloud forcing and validation, various aspects of intraseasonal, seasonal, and interannual variability, ocean wind stress applications, and validation of surface fluxes. The last day included talks from the National Meteorological Center (NMC), the National Center for Atmospheric Research (NCAR), the Center for Ocean-Land-Atmosphere Studies (COLA), the United States Navy, and the European Center for Medium Range Weather Forecasts (ECMWF).
During the POLARCAT summer campaign in 2008, two episodes (2 5 July and 7 10 July 2008) occurred where low-pressure systems traveled from Siberia across the Arctic Ocean towards the North Pole. The two cyclones had extensive smoke plumes from Siberian forest fires and anthropogenic sources in East Asia embedded in their associated air masses, creating an excellent opportunity to use satellite and aircraft observations to validate the performance of atmospheric transport models in the Arctic, which is a challenging model domain due to numerical and other complications. Here we compare transport simulations of carbon monoxide (CO) from the Lagrangian transport model FLEXPART and the Eulerian chemical transport model TOMCAT with retrievals of total column CO from the IASI passive infrared sensor onboard the MetOp-A satellite. The main aspect of the comparison is how realistic horizontal and vertical structures are represented in the model simulations. Analysis of CALIPSO lidar curtains and in situ aircraft measurements provide further independent reference points to assess how reliable the model simulations are and what the main limitations are. The horizontal structure of mid-latitude pollution plumes agrees well between the IASI total column CO and the model simulations. However, finer-scale structures are too quickly diffused in the Eulerian model. Applying the IASI averaging kernels to the model data is essential for a meaningful comparison. Using aircraft data as a reference suggests that the satellite data are biased high, while TOMCAT is biased low. FLEXPART fits the aircraft data rather well, but due to added background concentrations the simulation is not independent from observations. The multi-data, multi-model approach allows separating the influences of meteorological fields, model realisation, and grid type on the plume structure. In addition to the very good agreement between simulated and observed total column CO fields, the results also highlight the difficulty to identify a data set that most realistically represents the actual pollution state of the Arctic atmosphere.
Mathematical model and procedure based on hidden-Markov-model concept undergoing development for use in analysis and prediction of outputs of force and torque sensors of telerobotic manipulators. In model, overall task broken down into subgoals, and transition probabilities encode ease with which operator completes each subgoal. Process portion of model encodes task-sequence/subgoal structure, and probability-density functions for forces and torques associated with each state of manipulation encode sensor signals that one expects to observe at subgoal. Parameters of model constructed from engineering knowledge of task.
Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.
High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.
A physical retrieval method is proposed for estimating precipitation water distributions and other geophysical parameters from the Special Sensor Microwave/Imager (SSM/I) data. In the proposed method, sensor antenna functions are explicitly included to accommodate varying channel resolution. An embedded one-dimensional cloud model is utilized to generate vertical distributions of precipitating and nonprecipitating water, and spectral solutions are sought for certain background parameters, such as humidity, which vary more slowly in the horizontal than the cloud and precipitation water contents. The method allows the incorporation of measurements from SSM/T and geostationary infrared measurements in addition to SSM/I data, as well as information from conventional sources (e.g., radiosondes) or numerical forecast model fields.
We use numerical climate simulations, paleoclimate data, and modern observations to study the effect of growing ice melt from Antarctica and Greenland. Meltwater tends to stabilize the ocean column, inducing amplifying feedbacks that increase subsurface ocean warming and ice shelf melting. Cold meltwater and induced dynamical effects cause ocean surface cooling in the Southern Ocean and North Atlantic, thus increasing Earth's energy imbalance and heat flux into most of the global ocean's surface. Southern Ocean surface cooling, while lower latitudes are warming, increases precipitation on the Southern Ocean, increasing ocean stratification, slowing deepwater formation, and increasing ice sheet mass loss. These feedbacks make ice sheets in contact with the ocean vulnerable to accelerating disintegration. We hypothesize that ice mass loss from the most vulnerable ice, sufficient to raise sea level several meters, is better approximated as exponential than by a more linear response. Doubling times of 10, 20 or 40 years yield multi-meter sea level rise in about 50, 100 or 200 years. Recent ice melt doubling times are near the lower end of the 10-40-year range, but the record is too short to confirm the nature of the response. The feedbacks, including subsurface ocean warming, help explain paleoclimate data and point to a dominant Southern Ocean role in controlling atmospheric CO2, which in turn exercised tight control on global temperature and sea level. The millennial (500-2000-year) timescale of deep-ocean ventilation affects the timescale for natural CO2 change and thus the timescale for paleo-global climate, ice sheet, and sea level changes, but this paleo-millennial timescale should not be misinterpreted as the timescale for ice sheet response to a rapid, large, human-made climate forcing. These climate feedbacks aid interpretation of events late in the prior interglacial, when sea level rose to C6-9m with evidence of extreme storms while Earth was less than 1 C warmer than today. Ice melt cooling of the North Atlantic and Southern oceans increases atmospheric temperature gradients, eddy kinetic energy and baroclinicity, thus driving more powerful storms. The modeling, paleoclimate evidence, and ongoing observations together imply that 2 C global warming above the preindustrial level could be dangerous. Continued high fossil fuel emissions this century are predicted to yield (1) cooling of the Southern Ocean, especially in the Western Hemisphere; (2) slowing of the Southern Ocean overturning circulation, warming of the ice shelves, and growing ice sheet mass loss; (3) slowdown and eventual shutdown of the Atlantic overturning circulation with cooling of the North Atlantic region; (4) increasingly powerful storms; and (5) nonlinearly growing sea level rise, reaching several meters over a timescale of 50-150 years. These predictions, especially the cooling in the Southern Ocean and North Atlantic with markedly reduced warming or even cooling in Europe, differ fundamentally from existing climate change assessments. We discuss observations and modeling studies needed to refute or clarify these assertions.
A theoretical model is being developed jointly by the NASA White Sands Test Facility (WSTF) and the University of California at Santa Barbara (UCSB) to analyze data generated from the WSTF frictional heating test facility. Analyses of the data generated in the first seconds of the frictional heating test are shown to be effective in determining the friction coefficient between the rubbing interfaces. Different friction coefficients for carobn steel and Monel K-500 are observed. The initial condition of the surface is shown to affect only the initial value of the friction coefficient but to have no significant influence on the average steady-state friction coefficient. Rotational speed and the formation of oxide film on the rotating surfaces are shown to have a significant effect on the friction coefficient.
Symptom modelling in head and neck cancer is challenged by the complexity of heterogeneous patient data, leading to an interest in deep learning approaches. Although Long Short-Term Memory Networks (LSTMs) have shown great results in patient risk prediction, their low interpretability requires data modellers to collaborate with clinical experts to validate the results. We present L-VISP, a human–machine solution that uses visual analytics for LSTM modelling in clinical research. L-VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context. We evaluate L-VISP with data modellers and a clinical oncologist and present the takeaways from this multidisciplinary collaboration.
The configuration of ION (Inter - planetary Overlay Network) network nodes is a manual task that is complex, time-consuming, and error-prone. This program seeks to accelerate this job and produce reliable configurations. The ION Configuration Editor is a model-based smart editor based on Eclipse Modeling Framework technology. An ION network designer uses this Eclipse-based GUI to construct a data model of the complete target network and then generate configurations. The data model is captured in an XML file. Intrinsic editor features aid in achieving model correctness, such as field fill-in, type-checking, lists of valid values, and suitable default values. Additionally, an explicit "validation" feature executes custom rules to catch more subtle model errors. A "survey" feature provides a set of reports providing an overview of the entire network, enabling a quick assessment of the model s completeness and correctness. The "configuration" feature produces the main final result, a complete set of ION configuration files (eight distinct file types) for each ION node in the network.
Electric aviation faces a major challenge of avoiding potentially catastrophic consequences of the battery’s thermal runaway while keeping the weight of the battery low. Detection of early warning signals of battery failures requires accurate monitoring of the battery’s health throughout its lifespan. However, identifying the parameters of the battery from field data is notoriously difficult. We investigate this problem within the framework of modeling the temperature dynamics of a Li-ion cell during tests simulating loading in electric aircraft flights. It is found that the parameters of a higher-fidelity physics-based thermal model cannot be identified from the simulated flight data. To resolve this issue, we reduce the higher-fidelity thermal model to a model with fewer parameters. The resulting reduced-order model can predict temperature dynamics accurately and is identifiable throughout the cell’s lifespan which allows using the model’s parameters to monitor the state-of-health of the aging cell and detect anomalies in thermal behavior.
While currently available relational database management systems (RDBMS) allow inclusion of spatial information in a data model, they lack tools for presenting this information in an easily comprehensible form. Computer-aided design (CAD) software packages provide adequate functions to produce drawings, but still require manual placement of symbols and features. This project has demonstrated a bridge between the data model of an RDBMS and the graphic display of a CAD system. It is shown that the CAD system can be used to control the selection of data with spatial components from the database and then quickly plot that data on a map display. It is shown that the CAD system can be used to extract data from a drawing and then control the insertion of that data into the database. These demonstrations were successful in a test environment that incorporated many features of known working environments, suggesting that the techniques developed could be adapted for practical use.
Assimilated ozone is produced at the NASA/Goddard Data Assimilation Office by blending ozone retrieved from the Solar Backscatter UltraViolet/2 (SBUV/2) instrument and the Earth Probe Total Ozone Mapping Spectrometer (EP TOMS) measurements into an off-line transport model. The current system tends to overestimate the amount of lower stratospheric ozone. This is a region where ozone plays a key role in the forcing of climate. A biased ozone field in this region will adversely impact calculations of the stratosphere-troposphere exchange and, when used as a first guess in retrievals, the values determined from satellite observations. Since these are all important applications of assimilated ozone products, effort is being directed towards reducing this bias. The SBUV ozone data have a coarse vertical resolution with increased uncertainty below the ozone maximum, and TOMS provides only total ozone columns. Thus, the assimilated ozone in the lower stratosphere, and its vertical distribution in particular, are only weakly constrained by the incoming SBUV and TOMS data. Consequently, the assimilated ozone distribution should be sensitive to changes in inputs to the statistical analysis scheme. Accordingly, the sensitivity of the assimilated lower stratospheric ozone fields to changes in the TOMS error-covariance modeling and the SBUV data selection has been investigated. The use of a spatially correlated TOMS error covariance model led to improvements in the product. However, withholding the SBUV/2 data for the layer between 63 and 126 hPa typically degraded the product, a result which vindicates the use of this layer ozone product, despite its known errors. These efforts to improve the lower stratospheric distribution will be extended to include a more advanced forecast error covariance model, and by assimilating ozone products from new instruments on Envisat and EOS Aura.
In-depth gap heating ratios were predicted down RSI tile sidewalls based on temperature measurements obtained from the JSC arc-jet Wing Glove model tests in order to develop gap heating ratios which resulted in the best possible fit of test data and to produce a set of engineering verification heating ratios similar in shape to one another which could be used at various body points on the Orbiter during reentry. The Rockwell TPS Multidimensional heat conduction program was used to perform 3-D thermal analyses using a 3.0 in. thick section of a curved RSI tile with 283 nodal points. Correlation with test data shows that the predicted heating ratios were significantly higher down in the gap than the zero pressure values for T/C stacks 39 and 38 on the Wing Glove model. For stack 37 (in a low pressure region), the baseline heating ratio overpredicted the temperature data. This analysis, which showed that the heating ratios were a strong function of the product of pressure and pressure gradient, will be used to compare with recent Gap/Step and Ames Double Wedge test/analysis results in the effort to identify the Orbiter gap response to high delta P flight environment.
This document evaluates a hypothetical nuclear powerplant and associated protective force personnel using modern modeling and simulation tools. The facility incorporates security early in the design to consider and integrate methods to resolve security issues and vulnerabilities via the facility’s inherent design characteristics before construction. The evaluation in this document is an example only. It is not intended to recommend or evaluate the effectiveness of existing physical security requirements or identify any method that the U.S. Nuclear Regulatory Commission staff may find acceptable for complying with existing requirements.
Aspects of the angular momentum cycle, energetics, and related diagnostics from a number of models, including some from the Goddard Laboratory for Atmospheres, and from the Atmospheric Model Intercomparison Project (AMIP) are examined. Torques that dynamically excite changes in angular momentum, including strong torques at mountains were studied. The measure of how atmospheric mass from a strong weather signal can notably change the angular momentum is studied. For AMIP, there is a spread in the angular momentum amongst models, while the GLA model does reasonably well compared to the other models in the diagnostics examined, namely angular momentum and water vapor. Trends and interannual variability in water vapor over a lengthy period was examined. The role of the diabatic heating components, especially latent heating, in the energy cycle and the terms converting available potential energy to kinetic energy, among other parts of the energy cycle, are studied. Modes of climate of the atmosphere, especially the Arctic and North Atlantic Oscillations, are analyzed as well.