A Framework to Predict Variability Characteristics in Building Load Profiles
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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Predicting variable gene content in Escherichia coli using conserved genes
Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%–90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943–0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including “hypothetical proteins” was accurately predicted (F1 = 0.902 [0.898–0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876–0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data.
59 BASIC BIOLOGICAL SCIENCES↗
An Ensemble Investigation of the Causes for Regional Air-Quality Model Critical Load Exceedances Prediction Variability in European and North American Domains Using Diagnostics From Phase 4 of the Air Quality Model Evaluation International Initiative
We summarize tentative findings from multi air quality model ensembles for the years 2009 and 2010 in Europe (EU), and 2010 and 2016 in North America (NA), under AQMEII-4. The model predictions of sulphur and nitrogen deposition were used to estimate exceedances of critical loads for acidification and eutrophication, to show the extent to which the ensemble members agree in the magnitude and the trend of ecologically meaningful impacts. Model exceedance variability was analyzed using AQMEII-4 diagnostics. Evaluation against concentration and wet deposition observations, coupled with these diagnostics, identified specific process representations as the causes for variability between model predictions and for reduced model performance. All models predicted reductions in ecosystem acidification impacts in North America between the years 2010 and 2016, in accord with SO2 emissions reduction legislation which started in 2010 (SO2 SIP) However, all models in EU and NA domains had net negative biases for wet deposition of sulphur and nitrogen relative to observations. The wet S deposition average mean bias for the NA ensemble was -0.17 eq ha-1 d-1, and for the EU ensemble -1.15 eq ha-1 d-1. The NA daily wet deposition average mean bias for NH4+ was -0.37 eq ha-1d-1; EU -1.19 eq ha-1 d-1. The daily NA wet NO3- deposition average mean bias was -0.24 eq ha-1d-1; EU -0.69 eq ha-1 d-1. The members of the ensemble diverged (factor of 10) in their North American predictions for Ndep and consequently their eutrophication exceedances. The models with the highest eutrophication predictions also predicted the highest levels of gas-phase ammonia dry deposition (standard deviation of ammonia dry deposition flux across ensemble members was larger than the ensemble average). These models also had negative biases of predicted ammonia concentrations; average mean biases of -0.63 (satellite NH3) and -0.85 ppbv (surface NH3) compared to ensemble averages of -0.30 and -0.34 ppbv. Diagnostics showed that these differences resulted from the manner in which bidirectional ammonia fluxes were parameterized within these models. The second largest source of NA eutrophication prediction variability were models with positive biases in particulate ammonium and nitrate concentrations, and higher particle nitrogen deposition levels ( particle ammonium concentration bias +0.35 ug m-3; ensemble bias +0.15 ug m-3). We believe two factors may have led to these latter overestimates: higher levels of fine mode particle nitrate formation compared to other models (due to the use of an inorganic heterogeneous chemistry algorithm which did not take base cation chemistry into account), and updates to particle dry deposition velocities carried out in the absence of concurrent updates to wet scavenging algorithms. The relative importance of dry gas, dry particulate, and wet deposition towards total sulphur and nitrogen deposition totals differed between EU and North American domains, though all models had negative biases in wet deposition as noted above. Parallel and subsequent work suggests that multiphase hydrometeor scavenging may improve model wet deposition performance. An increased research focus is recommended for four model processes: multiphase hydrometeor scavenging, ammonia bidirectional fluxes, base cation chemistry and emissions, and particle dry deposition.
regional air-quality model↗
Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect
Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.
Rugenstein, Maria [Colorado State University Fort ↗
Modeling and Visualizing Uncertainty in Continuous Variables Predicted using Remotely Sensed Data
The use of remotely sensed images to map continuous biophysical variables, such as those related to terrestrial vegetation amount, sea surface temperature, and many other targets of NASA s Earth Observing System (EOS), includes variable, parametric, positional, spatial support and structural sources of uncertainty. A complete description of uncertainty will lead to a probability distribution at each location, allowing the exploration of the spatial dimension of uncertainty, that is, where the field is not well quantified. To achieve this purpose, convenient visualization tools are required. We have produced such a tool, called PDFVis, that facilitates the display of probability density functions (pdfs) on a per-grid-cell basis. The density estimate from Monte-Carlo generated realizations is interactively displayed as well as parametric and non-parametric summaries of the pdf field (such as mean, median, quartiles, standard deviation, number of modes, and locations of modes). Shaded surface renderings of pdfs along a transect can also be projected onto a plane. This tool will become more useful as richer descriptions of spatial uncertainty become available.
Dungan, Jennifer L.↗
Impacts of Mesh Refinement and Number of Ensemble Members on Climate Variability Predictions in E3SM
Explore the source record for details and available documents.
Houchens, Brent C.↗
Asian Monsoons: Variability, Predictability, and Sensitivity to External Forcing
In this study, we have addressed the interannual variations of Asian monsoons including both broad-scale and regional monsoon components. Particular attention is devoted to the identities of the South China Sea monsoon and Indian monsoon. We use CPC Merged Analysis of Precipitation and NCEP reanalyses to define regional monsoon indices and to depict the various monsoons. Parallel modeling studies have also been carried out to assess the role of boundary forcing and the potential predictability of the monsoons. Each monsoon is characterized by its unique features. While the South Asian monsoon represents a classical monsoon in which anomalous circulation is governed by Rossby-wave dynamics, the Southeast Asian monsoon symbolizes a "hybrid" monsoon that features multi-cellular meridional circulation over eastern Asia. The broad-scale Asian monsoon links to the basin-wide atmospheric circulation over the Indian-Pacific oceans. Both SST and land surface processes are important for determining the variations of all monsoons. For the broad-scale monsoon, SST anomalies are more important than land surface processes. For regional monsoons, however, land surface processes may become equally important. Both observation and model shows that the broad-scale monsoon is potentially more predictable than regional monsoons, and that the Southeast Asian monsoon may possess higher predictability than the South Asian monsoon.
Yang, Song↗
Asian Monsoons: Variability, Predictability, and Sensitivity to External Forcing
In this study, we have addressed the interannual variations of Asian monsoons including both broad-scale and regional monsoon components. Particular attention is devoted to the identities of the South China Sea monsoon and Indian monsoon. We use CPC Merged Analysis of Precipitation and NCEP reanalyses to define regional monsoon indices and to depict the various monsoons. Parallel modeling studies have also been carried out to assess the potential predictability of the broad-scale and regional monsoons. Each monsoon is characterized by its unique features. While the South Asian monsoon represents a classical monsoon in which anomalous circulation is governed by Rossby-wave dynamics, the Southeast Asian monsoon symbolizes a "hybrid" monsoon that features multi-cellular meridional circulation over eastern Asia. The broad-scale Asian monsoon links to the basin-wide atmospheric circulation over the Indian-Pacific oceans. Both Sea Surface Temperatures (SST) and land surface processes are important for determining the variations of all monsoons. For the broad-scale monsoon, SST anomalies are more important than land surface processes. However, for regional monsoons, land surface processes may become equally important. Both observation and model shows that the broad-scale monsoon is potentially more predictable than regional monsoons, and that the Southeast Asian monsoon may possess higher predictability than the South Asian monsoon.
Yang, Song↗
Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings
The field of prognostics and health management provides quantitative methods for monitoring and predicting the health of physical systems. Prognostics algorithms are useful in that they can be employed to assess the current state of a system, propagate the system state throughout time, and predict potential anomalies or failures that may occur. However, effective prognosis can be challenging to achieve in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for some prognostics applications. In this work, we implement three new algorithmic approaches to prediction (sampling methods, variable prediction time step, variable prediction sample size) with the goal of improving computational efficiency while minimizing decrease in model accuracy. To quantitatively analyze our results, we examine a use-case of degradation of a Lithium-ion battery. Notably, through this work it was found that none of the sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. However, our results show that prediction accuracy is highly dependent on the time step used, and that an appropriate time step can optimize both model accuracy and simulation efficiency. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment in an application-specific manner may prove useful in some applications. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency of prognosis.
Prognostics↗
Influence of Atmospheric Variability on Predicted Sonic Boom Metrics
As part of the Quesst mission, NASA will fly the X-59 aircraft over selected communities to survey community responses to low sonic booms. Previously, we developed a Kalman filter method to estimate the loudness metrics experienced by each survey participant during each flight. The Kalman filter fuses acoustic measurements with predictions from PCBoom, a sonic boom propagation model. PCBoom requires vertical profiles of the temperature, humidity, and wind to propagate sonic booms through the atmosphere and predict the loudness metrics at the ground. Prior to each X-59 flight, NASA will launch weather balloons to measure the vertical atmospheric profile. However, the atmosphere changes continually with geographic location and time. The purpose of this work is to determine where and when to launch these weather balloons to minimize the uncertainty in the predicted loudness metrics caused by atmospheric variability. We analyzed the effect of atmospheric variability on the predicted Perceived Level in three communities in different climate zones in the United States. To achieve acceptable uncertainty in the Perceived Level, weather balloons should be launched from at least two different sites within the survey area within one hour of each X-59 flight.
sonic boom↗
Variability and Predictability of Land-Atmosphere Interactions: Observational and Modeling Studies
The overall goal of this project is to increase our understanding of seasonal to interannual variability and predictability of atmosphere-land interactions. The project objectives are to: 1. Document the low frequency variability in land surface features and associated water and energy cycles from general circulation models (GCMs), observations and reanalysis products. 2. Determine what relatively wet and dry years have in common on a region-by-region basis and then examine the physical mechanisms that may account for a significant portion of the variability. 3. Develop GCM experiments to examine the hypothesis that better knowledge of the land surface enhances long range predictability. This investigation is aimed at evaluating and predicting seasonal to interannual variability for selected regions emphasizing the role of land-atmosphere interactions. Of particular interest are the relationships between large, regional and local scales and how they interact to account for seasonal and interannual variability, including extreme events such as droughts and floods. North and South America, including the Global Energy and Water Cycle Experiment Continental International Project (GEWEX GCIP), MacKenzie, and LBA basins, are currently being emphasized. We plan to ultimately generalize and synthesize to other land regions across the globe, especially those pertinent to other GEWEX projects.
Roads, John↗
Multiple Probe Measurements at Uranus Motivated By Spatial Variability
Predictably, a major motivation for multiple atmospheric probe measurements at Uranus is the understanding of dynamic processes that create and maintain spatial variation in thermal structure, composition, and horizontal winds. But origin questions---regarding the planet's formation and evolution, and conditions in the protoplanetary disk---are also major science drivers for multiprobe exploration. Spatial variation in thermal structure reveals how the atmosphere transports heat from the interior, and measuring compositional variability in the atmosphere is key to ultimately gaining an understanding of the bulk abundances of several heavy elements. We review the current knowledge of spatial variability in Uranus' atmosphere, and we outline how multiple probe exploration would advance our understanding of this variability. The other giant planets are discussed, both to connect multiprobe exploration of those atmospheres to open questions at Uranus, and to demonstrate how multiprobe exploration of Uranus itself is motivated lessons learned about the spatial variation at Jupiter, Saturn, and Neptune. We outline the measurements of highest value from miniature secondary probes (which would complement more detailed investigation by a larger flagship probe), and present the path toward overcoming current challenges and uncertainties in areas including mission design, cost, trajectory, instrument maturity, power, and timeline.
Uranus↗
TELCOM - Time variable telecommunications performance prediction program.
Time variable telecommunications performance prediction computer program for spacecraft telemetry and communications systems
Fashano, M.↗
Interannual variability and predictability of 500 mb geopotential heights over the Northern Hemisphere
The interannual variability of monthly mean 500 mb heights in a 15-year sample of observed data is compared to the variance expected from sampling errors associated with high-frequency fluctuations using the analysis of variance approach. The monthly mean 'signal' stands out significantly from the 'noise' over a substantial fraction of the Northern Hemisphere during the winter. The expected spectrum of variance at very low frequencies is assumed to be white at frequencies lower than per (30 days) rather than the per (96 days) cutoff used by Madden. This difference is justified by observing that the effective time between independent samples T(0) is relatively insensitive to changes in the maximum lag over which the local autocorrelation is integrated to calculate T(0). Further non-randomness in the variance of 500 mb heights is evidenced by the correlation between monthly mean height and contemporaneous daily variability.
Shukla, J.↗
The Impact of Stochastic Perturbations in Physics Variables for Predicting Surface Solar Irradiance
We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m 2 ) than in the other models (22.7–23.6 W/m 2 ). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided.
14 SOLAR ENERGY↗
Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures
Deep Learning (DL) models are increasingly used throughout the sciences. However, their performance and usefulness depend greatly on their architecture which is defined by hyperparameters such as the number of nodes, layers, the learning rate, etc. Tuning these hyperparameters is time-consuming because evaluating their performance requires a lengthy training step. Stochastic optimizers used in training lead to performance variability and potentially prediction reliability issues. In this talk, we will describe an automated optimization method based on surrogate models and active learning strategies for tuning DL model architectures. We take into account the prediction variability with the goal to identify architectures that make reliable and robust predictions. We demonstrate our developments on an application arising in particle physics.
deep learning↗
Variability of sound propagation prediction due to atmospheric variability Final report
Sound propagation prediction affected by meteorological parameters such as atmospheric variability
SOUND PROPAGATION↗
The Variability and Predictability of Precipitation over Southeast Asian
The large scale atmospheric moisture budget, including precipitation, moisture flux convergence, and evaporation is described over southeast Asian. ne results are from the Goddard Earth Observing System (GEOS-1) data assimilation system for the period March 1981 - February 1995. On average, in the late spring and early summer season, precipitation is largely balanced by the atmospheric moisture flux convergence over southeast Asian; evaporation is also an important contributor. The seasonal variability of precipitation is best related to variations in the moisture flux convergence. On the monthly time scales, both the precipitable water and precipitation anomalies are most closely related to moisture flux convergence anomalies and less closely related to evaporation anomalies. The impact of the low-level jet (LLJ) on the strong low-level moisture flux convergence will be discussed. An ensemble of GEOS-2 atmospheric General Circulation Model (GCM) seasonal forecasts and long-term simulation are analyzed to assess the controlling influences of boundary forcing and memory of initial conditions. We also assess the variability and potential predictability from these experiments.
Chang, Yehui↗