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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Ozonesonde Quality Assurance: JOSIE-SHADOZ (2017) and SHALLOTS (2018)

Ozonesonde data constitute a mainstay of satellite calibration and are used for climatologies and analysis of trends, especially in the lower stratosphere where satellites are most uncertain. The electrochemical-concentration cell (ECC) ozonesonde has been deployed at ~100 stations worldwide since the 1960s, with changes over time in manufacture and procedures, including details of the cell chemical solution and data processing. As a consequence, there are biases among different stations and discontinuities in profile timeseries from individual site records. Since 1996 the Jülich [Germany] Ozone Sonde Intercomparison Experiment (JOSIE) has periodically tested ozonesondes in a simulation chamber designated the World Calibration Centre for Ozonesondes by WMO. In October- November 2017 a JOSIE campaign evaluated the sondes and procedures used in SHADOZ (Southern Hemisphere Additional Ozonesondes), a 14-station tropical and subtropical network. A distinctive feature of the 2017 JOSIE was that the tests were conducted by operators from eight SHADOZ stations; Nairobi, Natal, Irene, Costa Rica, Paramaribo, Reunion, Hanoi, Kuala Lumpur. Experimental protocols and preliminary results for the SHADOZ sonde configurations, which represent most of those in use today, are described. SHADOZ stations that follow WMO-recommended protocols record total ozone within 3% of the JOSIE reference instrument. Instrument biases noted in prior JOSIE and field tests like BESOS (2004) were noted in JOSIE-2017, with maximum effect in the stratosphere. In June 2018 we organized a series of dual launches during the OWLETS II campaign in the Maryland and Chesapeake Bay area (SHALLOTS = SHADOZ-OWLETS ParaLLel Ozonesonde Test Study). Instrument and solution types were varied as in JOSIE-2017 and three radiosonde-ozonesonde variants were tested. An example of a parallel sampling in SHALLOTS, from a Greenbelt EnSCI-iMet sonde combination flown with the Wallops SPCLMS package, is illustrated in the Figure. The result was a range of biases but in general the instrument combination (EnSCI-iMet) deployed at 11 SHADOZ stations recorded ~5-10% less ozone in the stratosphere than the SPC ECC sonde flown with a Vaisala or LMS system. These 2017 and 2018 results and prior JOSIEs demonstrate that regular testing is essential to maintain best practices in ozonesonde operations and to ensure high-quality data for the ozone assessment communities.

Quality assurance↗
DOE OSTI2024

Entanglement Renormalization for Quantum Field Theories with Discrete Wavelet Transforms

We propose an adaptation of Entanglement Renormalization for quantum field theories that, through the use of discrete wavelet transforms, strongly parallels the tensor network architecture of the Multiscale Entanglement Renormalization Ansatz (a.k.a. MERA). Our approach, called wMERA, has several advantages of over previous attempts to adapt MERA to continuum systems. In particular, (i) wMERA is formulated directly in position space, hence preserving the quasi-locality and sparsity of entanglers; and (ii) it enables a built-in RG flow in the implementation of real-time evolution and in computations of correlation functions, which is key for efficient numerical implementations. As examples, we describe in detail two concrete implementations of our wMERA algorithm for free scalar and fermionic theories in (1+1) spacetime dimensions. Possible avenues for constructing wMERAs for interacting field theories are also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2011

Deep Moonquake Focal Mechanisms: Recovery and Implications

A defining characteristic of deep moonquakes is their tendency to occur with tidal periodicity, prompting previous studies to infer that they are related to the buildup and release of tidal stress within the Moon. In studies of tidal forcing, a key constraint is the focal mechanism: the fault parameters describing the type of failure moonquakes represent. The quality of the lunar seismic data and the limited source/receiver geometries of the Apollo seismic network prohibit the determination of deep moonquake fault parameters using first-motion polarities, as is typically done in terrestrial seismology. Without being able to resolve tidal stress onto a known failure plane, we can examine only gross qualities of the tidal stress tensor with respect to moonquake occurrence, so we cannot fully address the role of tidal stress in moonquake generation. We will examine the extent to which shear (S) and compression (P) wave amplitude ratios can constrain moonquake fault geometry by determining whether, for a given cluster, there exists a focal mechanism that can produce a radiation pattern consistent with the amplitudes measured by the Apollo instruments. Amplitudes are read in the ray coordinate frame, directly from seismograms for which the P and S arrivals are clearly identifiable on all long-period channels of the four Apollo stations. We apply an empirical station correction to account for site effects and the differences between P- and S-wave attenuation. Instead of focusing on the best fitting solution only, we formulate the inverse problem using a falsification criterion: all source orientations that do not reproduce the observed SV/P ratios within an error margin derived from the uncertainty of amplitude readings are rejected. All others are accepted as possible solutions. The inversion is carried out using an exhaustive grid search on a regular grid with predefined step size, encompassing all possible combinations of strike, dip and slip. To assess the sensitivity of the inversion for the uncertainty of the lunar interior structure, we carry out repeated inversions with different velocity structures. Our data set consist of a total of 106 events from 25 deep moonquake clusters. The largest contribution of 37 events originates from the most active cluster, A001, while other clusters are represented by 1 to 9 events. Since the definition of a cluster implies that all events share the same source orientation, a comparison of the inversion results of all events from one cluster will reduce ambiguities of the inversion. Once we obtain a suite of fault parameters for a given source, we can attempt to further constrain the focal mechanism with refined analyses of tidal stresses and predictions based on synthetic seismograms.

Knapmeyer, Martin↗
DOE OSTI2024

From Latent Dynamics to Meaningful Representations

While representation learning has been central to the rise of machine learning and artificial intelligence, a key problem remains in making the learnt representations meaningful. For this the typical approach is to regularize the learned representation through prior probability distributions. However such priors are usually unavailable or are ad hoc. To deal with this, recent efforts have shifted towards leveraging the insights from physical principles to guide the learning process. In this spirit, we propose a purely dynamics-constrained representation learning framework. Instead of relying on predefined probabilities, we restrict the latent representation to follow overdamped Langevin dynamics with a learnable transition density — a prior driven by statistical mechanics. We show this is a more natural constraint for representation learning in stochastic dynamical systems, with the crucial ability to uniquely identify the ground truth representation. We validate our framework for different systems including a real-world fluorescent DNA movie dataset. Here, we show that our algorithm can uniquely identify orthogonal, isometric and meaningful latent representations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

Characterization of voids formed during liquid impregnation of nonwoven multifilament glass networks as related to composite processing

A technique based on matching the refractive index of an invading liquid to that of a fiber mat was used to study entrapment of air ('voids') that occurs during forced in-plane radial flow into nonwoven multifilament glass networks. The usefulness of this technique is demonstrated in quantifying and mapping the air pockets. Experiments with a series of fluids with surface tensions varying from 28 x 10(exp -3) to 36 x 10(exp -3) N/m, viscosities from 45 x 10(exp -3) to 290 x 10(exp -3) Pa.s, and inlet flow rates from 0.15 x 10(exp -6) to 0.75 x 10(exp -6) m(exp 3)/s, showed that void content is a function of the capillary number characterizing the flow process. A critical value of capillary number, Ca = 2.5 x 10(exp -3), identifies a zone below which void content increases exponentially with decreasing capillary number. Above this critical value, negligible entrapment of voids is observed. Similar experiments carried out on surface treated nonwoven mats spanning a range of equilibrium contact angles from 20 deg to 78 deg showed that there is a critical contact angle above which negligible entrapment is observed. Below this value, there is no apparent effect of contact angle on the void fraction - capillary number relationship described earlier. Studies on the effect of filament wettability, and fluid velocity and viscosity on the size of the entrapment (voids) were also carried out. These indicate that larger sized entrapments which envelop more than one pore are favored by a low capillary number in comparison to smaller, pore level bubbles. Experiments were carried out on deformed mats - imposing high permeability spots at regular intervals on a background of low permeability. The effect of these spatial fluctuations in heterogeneity of the mat on entrapment is currently being studied.

Mahale, Anant D.↗
NASA NTRS2004

Biomass-Burning Aerosols in South East-Asia: Smoke Impact Assessment (BASE-ASIA)

Biomass burning has been a regular practice for land clearing and land conversion in many countries, especially those in Africa, South America, and Southeast Asia. However, the unique climatology of Southeast Asia is very different than that of Africa and South America, such that large-scale biomass burning causes smoke to interact extensively with clouds during the peak-burning season of March to April. Significant global sources of greenhouse gases (e.g., CO2, CH4), chemically active gases (e.g., NO, CO, HC, CH3Br), and atmospheric aerosols are produced by biomass burning processes. These gases influence the Earth- atmosphere system, impacting both global climate and tropospheric chemistry. Some aerosols can serve as cloud condensation nuclei, which play an important role in determining cloud lifetime and precipitation, hence, altering the earth s radiation and water budget. Analyses from satellite measurements reveal the reflected solar (emitted thermal) radiation from clouds due to smoke aerosols can be reduced (enhanced) by 100 (20) Watts per square meter over the month of March 2000. In addition, the reduction in cloud spectral reflectance is large enough to lead to significant errors in satellite retrievals of cloud properties (e.g., optical thickness and effective radius). The fresh water distribution in this region is highly dependent on monsoon rainfall; in fact, the predictability of the tropical climate system is much reduced during the boreal spring. Therefore, to accurately assess the impact of smoke aerosols in this region requires continuous observations from satellites, aircraft, ground-based networks and dedicated field experiments. BASE-ASIA initiative has been proposed and will be discussed.

Tsay, S.-C.↗
DOE OSTI2023

Optimizing Performance on Trinity Utilizing Machine Learning, Proxy Applications and Scheduling Priorities

The sheer number of nodes continues to increase in today’s supercomputers, the first half of Trinity alone contains more than 9400 compute nodes. Since the speed of today’s clusters are limited by the slowest nodes, it more important than ever to identify slow nodes, improve their performance if it can be done, and assure minimal usage of slower nodes during performance critical runs. This is an ongoing maintenance task that occurs on a regular basis and, therefore, it is important to minimize the impact upon its users by assessing and addressing slow performing nodes and mitigating their consequences while minimizing down time. These issues can be solved, in large part, through a systematic application of fast running hardware assessment tests, the application of Machine Learning, and making use of performance data to increase efficiency of large clusters. Proxy applications utilizing both MPI and OpenMP were developed to produce data as a substitute for long runtime applications to evaluate node performance. Machine learning is applied to identify underperforming nodes, and policies are being discussed to both minimize the impact of underperforming nodes and increase the efficiency of the system. In this paper, I will describe the process used to produce quickly performing proxy tests, consider various methods to isolate the outliers, and produce ordered lists for use in scheduling to accomplish this task.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bounds on Optimal Revisit Times in Persistent Monitoring Missions With a Distinct and Remote Service Station</strong

We report persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. Unmannned aerial vehicles (UAVs) can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this article, we consider the case in which the service station is not colocated with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between successive revisits to targets, to be minimized. Here, we consider the problem of determining UAV routes that lead to the minimum revisit time. The problem is NP-hard, and its computational difficulty increases with the fuel capacity of the UAV. We develop an algorithm to construct near-optimal solutions to the problem quickly when the fuel capacity exceeds a threshold. We also develop lower bounds to the optimal revisit time and use these bounds to demonstrate (through numerical simulations) that the constructed solutions are, on an average, at most 0.01% away from the optimum.

42 ENGINEERING↗
NASA NTRS2002

Biomass-burning Aerosols in South East-Asia: Smoke Impact Assessment(BASE-ASIA)

Biomass burning has been a regular practice for land clearing and land conversion in many countries, especially in Africa, South America, and South East Asia. Significant global sources of greenhouse gases (e.g., CO2, CH4), chemically active gases (e.g., NO, CO, HC, CH3Br), and atmospheric aerosols are produced by biomass-burning processes, which influence the Earth-atmosphere energetics and hence impact both global climate and tropospheric chemistry. Some gases and aerosols can serve as active cloud condensation nuclei, which play important role in determining the net radiation budget, precipitation rate, and cloud lifetime. Biomass burning also affects the biogeochemical cycling of nitrogen and carbon compounds from the soil to the atmosphere; the hydrological cycle (i.e., run off and evaporation); the reflectivity and emissivity of the land; and the stability of ecosystems and ecosystem biodiversity. Compared to Africa and South America, the climatology in South East Asia reveals quite different characteristics, showing distinct large-scale smoke and cloud sources and interaction regimes. The fresh water distribution in this region is highly dependent on monsoon rainfall; in fact, the predictability of the tropical climate system is much reduced during the boreal spring, which is associated with the peak season of biomass burning activities. Estimating the burning fuel (e.g., bark, branches, and wood), an important part of studying regional carbon cycle, may rely on utilizing a wide range of distinctive spectral features in the shortwave and longwave regions. Therefore, to accurately assess the impact of smoke aerosols in this region requires continuous observations from satellites, aircraft, networks of ground-based instruments and dedicated field experiments. A new initiative will be proposed and discussed.

Tsay, Si-Chee↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
NASA NTRS1999

Orbit Determination for Mars Global Surveyor During Mapping

The Mars Global Surveyor (MGS) spacecraft reached a low-altitude circular orbit on February 4, 1999, after the termination of the second phase of aerobraking. The MGS spacecraft carries the Mars Orbiter Laser Altimeter (MOLA) whose primary goal is to derive a global, geodetically referenced 0.2 deg x 0.2 deg topographic grid of Mars with a vertical accuracy of better than 30 meters. During the interim science orbits in the' Hiatus mission phase (October - November 1997), and the Science Phasing Orbits (March - April, 1998, and June - July 1998) 208 passes of altimeter data were collected by the MOLA instrument. On March 1, 1999 the first ten orbits of MOLA altimeter data from the near-circular orbit were successfully returned from MGS by the Deep Space Network (DSN). Data will be collected from MOLA throughout the Mapping phase of the MCS mission, or for at least one Mars year (687 days). Whereas the interim orbits of Hiatus and SPO were highly eccentric, and altimeter data were only collected near periapsis when the spacecraft was below 785 km, the Mapping orbit of MGS is near circular, and altimeter data will be collected continuously at a rate of 10 Hz. The proper analysis of the altimeter data requires that the orbit of the MGS spacecraft be known to an accuracy comparable to that of the quality of the altimeter data. The altimeter has an ultimate precision of 30 cm on mostly flat surfaces, so ideally the orbits of the MGS spacecraft should be known to this level. This is a stringent requirement, and more realistic goals of orbit error for MGS are ten to thirty meters. In this paper we will discuss the force and measurement modelling required to achieve this objective. Issues in force modelling include the proper modelling of the gravity field of Mars, and the modelling of non-conservatives forces, including the development of a 'macro-model', in a similar fashion to TOPEX/POSEIDON and TDRSS. During Cruise and Aerobraking, the high gain antenna (HGA) was stowed on the +X face of the spacecraft. On March 29, 1999 the HGA will be deployed on a meter long boom which will remain Earth-pointing while the instrument panel (including the MOLA instrument) remains pointed at nadir. The tracking data must be corrected for the regular motion of the high gain antenna with respect to the center of mass, and the success of the MGS determination during Mapping will depend on correctly accounting for this offset in the measurement model.

Lemoine, F. G.↗
NASA NTRS2025

Airports as Energy Nodes Activity Summary

Advanced aircraft concepts that use non-traditional aviation energy storage methods such as batteries or cryogenic hydrogen are in development and expected to enter regular service at airports worldwide within the next decade. The energy needs for these aircraft may quickly overwhelm the existing energy infrastructure at airports, particularly at smaller and more remote facilities. Without energy upgrades, these airports will not be able to host these advanced vehicles, but without the advanced vehicle traffic, these airports will not have the rationale or funding to build up their energy infrastructure. The Airports as Energy Nodes (ÆNodes) activity, a collaboration between the National Aeronautics and Space Administration (NASA) and the National Renewable Energy Laboratory (NREL), was executed to understand and model the energy needs that advanced aircraft concepts may levy on these smaller airports, determine cost-effective approaches to enhance the airport energy infrastructure, and demonstrate the enhanced resilience of these energy infrastructure upgrades to the airport and surrounding community via “digital twin” simulation at relevant energy and dynamic time scales. The ÆNodes team also investigated future reference aircraft designs and materials to enable cryogenic hydrogen storage for aircraft. The ÆNodes team conducted analysis at two U.S. airport partner sites — Winchester Regional Airport in Winchester, Virginia, and Tweed/New Haven Airport in New Haven, Connecticut. The goal of this partnership was to develop data and reference infrastructure designs that could accommodate advanced aircraft in the future at these airports while also enhancing the resiliency of the energy supply to the surrounding airport community, which could be used to capture funding to enable the infrastructure upgrades. Over the course of the study, a method was developed to estimate air traffic requiring advanced energy services over the course of a year using a mix of historical data and companion studies on advanced aircraft transportation networks. The study has concluded at NASA but continues at NREL, who will develop a final report discussing the energy infrastructure upgrades and digital twin results. Preliminary results indicate that unrestricted adoption of advanced battery-electric aircraft may double traffic at these airports and increase peak daily power usage by an order of magnitude, while increase electricity energy needs by a factor of two to four. The infrastructure upgrades necessary to accommodate these increased energy needs could be used to provide enhanced energy services to the airport community to offset the cost and increase the utility of the upgrades, which will be described in the NREL final report.

Airports↗
NASA NTRS2010

Airborne Dust, "The Good Guy or the Bad Guy": How Much do We Know?

Processes in generating, transporting, and dissipating the airborne dust particles are global phenomena -African dust regularly reaching the Alps; Asian dust seasonally crossing the Pacific into North America, and ultimately the Atlantic into Europe. One of the vital biogeochemical roles dust storms play in Earth's ecosystem is routinely mobilizing mineral dust, as a source of iron, from deserts into oceans for fertilizing the growth of phytoplankton -the basis of the oceanic food chain. Similarly, these dust-laden airs also supply crucial nutrients for the soil of tropical rain forests, the so-called womb of life that hosts 50-90% of the species on Earth. With massive amounts of dust lifted from desert regions and injected into the atmosphere, however, these dust storms often affect daily activities in dramatic ways: pushing grit through windows and doors, forcing people to stay indoors, causing breathing problems, reducing visibility and delaying flights, and by and large creating chaos. Thus, both increasing and decreasing concentrations of doses result in harmful biological effects; so do the airborne dust particles to our Living Earth. Since 1997 NASA has been successfully launching a series of satellites - the Earth Observing System - to intensively study, and gain a better understanding of, the Earth as an integrated system. Through participation in many satellite remote-sensing/retrieval and validation projects over the years, we have gradually developed and refined the SMART (Surface-sensing Measurements for Atmospheric Radiative Transfer) and COMMIT (Chemical, Optical & Microphysical Measurements of In-situ Troposphere) mobile observatories, a suite of surface remote sensing and in-situ instruments that proved to be vital in providing high temporal measurements, which complement the satellite observations. In this talk, we will present SMART-COMMIT which has played key roles, serving as network or supersite, in major international research projects such as the Joint Aerosol Monsoon Experiment (JAMEX), a core element of the Asian Monsoon Years (AMY, 2008-2012). SMART-COMMIT deployments during 2008 AMY/JAMEX were conducted in northwestern China to characterize the properties of dust-laden aerosols. In 2009, SMART-COMMIT also participated in the JAMEX/RAJO-MEGHA (Radiation, Aerosol Joint Observations-Monsoon Experiment in the Gangetic-Himalayan Area; Sanskrit for Dust-Cloud) to study the aerosol properties, solar absorption and the associated atmospheric warming, and the climatic impact of elevated aerosols during the premonsoon season in South Asia. To fully characterize the properties of airborne dust in the field is an important but challenging task. In this seminar, we will present our recent measurements and retrievals of airborne dust properties.

Tsay, Si-Chee↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
NASA NTRS2018

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihood–based classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrence–based texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms.

Pasolli, Edoardo↗
NASA NTRS2003

Biomass-burning Aerosols in South East-Asia: Smoke Impact Assessment (BASE-ASIA)

Biomass burning has been a regular practice for land clearing and land conversion in many countries, especially those in Africa, South America, and Southeast Asia. However, the unique climatology of Southeast Asia is very different than that of Africa and South America, such that large-scale biomass burning causes smoke to interact extensively with clouds during the peak-burning season of March to April. Significant global sources of greenhouse gases (e.g., CO2, CH4), chemically active gases (e.g., NO, CO, HC, CH3Br), and atmospheric aerosols are produced by biomass burning processes. These gases influence the Earth-atmosphere system, impacting both global climate and tropospheric chemistry. Some aerosols can serve as cloud condensation nuclei, which play an important role in determining cloud lifetime and precipitation, hence, altering the earth's radiation and water budget. Biomass burning also affects the biogeochemical cycling of nitrogen and carbon compounds from the soil to the atmosphere; the hydrological cycle (i.e., run off and evaporation); land surface reflectivity and emissivity; as well as ecosystem biodiversity and stability. Analyses from satellite measurements reveal that smoke is frequently present solar (emitted thermal) radiation from clouds due to smoke aerosols can be reduced (enhanced) by as much as 100 (20) W/sq m over the month of March 2000. In addition, the reduction in cloud spectral reflectance at 670 run is large enough to lead to significant errors in retrieving cloud properties (e.g., optical thickness and effective radius) from satellite measurements. The fresh water distribution in this region is highly dependent on monsoon rainfall; in fact, the predictability of the tropical climate system is much reduced during the boreal spring. Estimating the burning fuel (e.g., bark, branches, and wood), an important part of studying regional carbon cycle, may rely on utilizing a wide range of distinctive spectral features in the shortwave and longwave regions. Therefore, to accurately assess the impact of smoke aerosols in this region requires continuous observations from satellites, aircraft, networks of ground-based instruments and dedicated field experiments. A new initiative will be proposed and discussed.

Tsay, Si-Chee↗
DOE OSTI2023

Spatiotemporal Super-Resolution with Generative Machine Learning for Creating Renewable Energy Resource Data Under Climate Change Scenarios

As we plan for a future with higher penetrations of renewables and increasing electrification, it becomes more important to understand how the electricity grid will operate under a variety of weather events. We must also consider that the weather our future grid will experience will be different and possibly more extreme than the historical weather that we have extensive data for. We can use data from global climate models (GCMs) to help understand how our climate may change over the next several decades, but there is often a significant gap between the low-resolution GCM data and the high-resolution weather data required to study power systems under specific weather events. Therefore, our objective in this work is to develop tools that can bridge this gap by using low-resolution GCM data to create realistic high-resolution weather datasets that can be used to study renewable energy generation and electricity demand. To accomplish this objective, we have developed a set of generative machine learning models that can rapidly downscale GCM daily average output data at an approximate grid resolution of 100km to hourly data at an approximate 4 km grid resolution. The models can be used to create high resolution data from nearly any GCM included in the Coupled Model Intercomparison Project (CMIP) Phase 5 or 6. Our methods include all datasets regularly used to study the integration of wind and solar power plants as well as changes in electricity demand due to heating and cooling loads. These models and datasets enable power systems modelers to study climate change-influenced weather events and their impact on the grid. We have downscaled and validated wind, solar, temperature, and humidity data with very promising results. The generative machine learning methods are computationally efficient and produce data that has similar statistical characteristics to current state-of-the-art historical datasets. We have trained initial generative models and produced an initial dataset collectively referred to as Sup3rCC: Super-Resolved Renewable Energy Resource Data with Climate Change Impacts. The data covers a (mostly) historical period from 2015-2025 and a future period from 2050-2059. We have also taken hypothetical high-electrification load data and scaled the heating and cooling loads with respect to the 2050-2059 high-resolution Sup3rCC meteorology. The results show how future levels of renewable energy generation and electrified load may be impacted by climate change, setting the stage for capacity expansion models to consider a dynamic climate through model years.

climate change↗
DOE OSTI2024

Atmospheric H 2 observations from the NOAA Cooperative Global Air Sampling Network

Abstract. The NOAA Global Monitoring Laboratory (GML) measures atmospheric hydrogen (H2) in grab samples collected weekly as flask pairs at over 50 sites in the Cooperative Global Air Sampling Network. Measurements representative of background air sampling show higher H2 in recent years at all latitudes. The marine boundary layer (MBL) global mean H2 was 552.8 ppb in 2021, 20.2 ± 0.2 ppb higher compared to 2010. A 10 ppb or more increase over the 2010–2021 average annual cycle was detected in 2016 for MBL zonal means in the tropics and in the Southern Hemisphere. Carbon monoxide measurements in the same-air samples suggest large biomass burning events in different regions likely contributed to the observed interannual variability at different latitudes. The NOAA H2 measurements from 2009 to 2021 are now based on the World Meteorological Organization Global Atmospheric Watch (WMO GAW) H2 mole fraction calibration scale, developed and maintained by the Max Planck Institute for Biogeochemistry (MPI-BGC), Jena, Germany. GML maintains eight H2 primary calibration standards to propagate the WMO scale. These are gravimetric hydrogen-in-air mixtures in electropolished stainless steel cylinders (Essex Industries, St. Louis, MO), which are stable for H2. These mixtures were calibrated at the MPI-BGC, the WMO Central Calibration Laboratory (CCL) for H2, in late 2020 and span the range 250–700 ppb. We have used the CCL assignments to propagate the WMO H2 calibration scale to NOAA air measurements performed using gas chromatography and helium pulse discharge detector instruments since 2009. To propagate the scale, NOAA uses a hierarchy of secondary and tertiary standards, which consist of high-pressure whole-air mixtures in aluminum cylinders, calibrated against the primary and secondary standards, respectively. Hydrogen at the parts per billion level has a tendency to increase in aluminum cylinders over time. We fit the calibration histories of these standards with zero-, first-, or second-order polynomial functions of time and use the time-dependent mole fraction assignments on the WMO scale to reprocess all tank air and flask air H2 measurement records. The robustness of the scale propagation over multiple years is evaluated with the regular analysis of target air cylinders and with long-term same-air measurement comparison efforts with WMO GAW partner laboratories. Long-term calibrated, globally distributed, and freely accessible measurements of H2 and other gases and isotopes continue to be essential to track and interpret regional and global changes in the atmosphere composition. The adoption of the WMO H2 calibration scale and subsequent reprocessing of NOAA atmospheric data constitute a significant improvement in the NOAA H2 measurement records.

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