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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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This paper attempts to summarize the current understanding of the storm/substorm relationship by clearing up a considerabel amount of controversy and addressing the question of how solar wind energy is deposited into the constituent elements that are critical to magnetospheric and ionospheric processes.
At the heart of modern data assimilation schemes is covariance propagation.Loss of variance experienced in large-scale applications such as numerical weather prediction is problematic, and the development of auxiliary methods to mitigate this issue is an active research area. The focus of this work is to understand the root causes of variance loss and show that for advective dynamics, the covariance propagation by itself typically causes significant, spurious loss of variance, even at full rank. To demonstrate this, we first study continuum covariance propagation by analyzing the covariance evolution equation for advective dynamics. The behavior of this evolution equation changes abruptly as the correlation length tends to zero, for example in the vicinity of sharp gradients in the advection field. This happens because the diagonal of the kernel of the covariance operator is a characteristic surface for advective dynamics. Our numerical experiments then confirm that the variance lost during numerical propagation greatly exceeds that due to numerical dissipation alone. The variance loss is driven primarily by inaccurate variance propagation resulting from standard, full-rank covariance propagation schemes, which have difficulty capturing the abrupt change in dynamics as the correlation length tends to zero. These results suggest that developing local covariance propagation methods may prove useful in ameliorating the variance loss observed in data assimilation schemes
Accurately representing land-atmosphere (L-A) interactions and coupling in NWP systems remains a challenge. New observations, incorporated into models via assimilation or calibration, hold the promise of improved forecast skill, but erroneous model coupling can hinder the benefits of such activities. To better understand model representation of coupled interactions and feedbacks, this study demonstrates a novel framework for coupled calibration of the Single Column Model (SCM) capability of the NASA Unified Weather Research and Forecasting (NU-WRF) system coupled to NASA’s Land Information System (LIS). The local land-atmosphere coupling (LoCo) process chain paradigm is used to assess the processes and connections revealed by calibration experiments. Two summer case studies in the U. S. Southern Great Plains are simulated in which LSM parameters are calibrated to diurnal observations of LoCo process chain components including 2-meter temperature, 2-meter humidity, surface fluxes (Bowen ratio), and PBL height. Results show a wide range of soil moisture and hydraulic parameter solutions depending on which L-A variable (i.e. observation) is used for calibration, highlighting that improvement in either SHP or ISM when not in tandem with the other can provide undesirable results. Overall, this work demonstrates that a process chain calibration approach can be used to assess L-A connections, feedbacks, strengths, and deficiencies in coupled models, as well as quantify the potential impact of new sources of observations of land-PBL variables on coupled prediction.
A number of instruments exist to measure the size, shape and orientation angle of precipitation particles near the surface; this study will specifically focus on three of them: the precipitation imaging package (PIP), the multi-angle snowflake camera (MASC), and the two-dimensional video distrometer (2DVD). The measurements of particular interest for developing precipitation microphysical retrievals from polarimetric radar and satellite-based microwave data are of the particle size, aspect ratio, and orientation angle. Due to differences in instrument design and processing software, however, the measurements made by PIP, MASC, and 2DVD are not in agreement on these key measurements. The aim of the present study is to understand how the differences between these three instruments impact their measurements of the size, aspect ratio, and orientation angle of winter precipitation particles. To this end, we have reprocessed the video files produced by PIP in a manner that replicates how the MASC and 2DVD instruments operate and process their respective data. Our findings suggest that the MASC processing software results in superior particle characteristic measurements in comparison to the other instruments, although a cursory comparison of measurements from the actual instruments suggests the PIP captures the largest number of particles of the three instruments, making PIP potentially better for computing distributions.
There is a pressing need to search for novel approaches to combat biofilm formation, both in space and in medical applications. Many proteins have the ability to form ordered aggregates called amyloids. Amyloids are known to be an important part of biofilms. The use of anti-amyloid drugs is a novel venue for the development of antimicrobial agents. The ultrastructure of the amyloid aggregate shows a high packing of proteins, the second-order structure of which is dominated by β-sheets. The ability to form an amyloid aggregate is especially typical for proteins containing domains (protein fragments) with sufficient lability to arrange themselves in a tight β-sheet structure. Bioinformatics tools allow the prediction of such behavior of proteins in genomic data. We use GeneLab data of microbial populations identified aboard the International Space Station and other spacecraft to look for bacterial species that utilize amyloid aggregation in biofilm formation. We use a combined bioinformatic approach with a relatively high throughput molecular biology assay and biophysical assays to evaluate the anti-amyloid anti-biofilm approach. The significance of the research extends from understanding basic microbial community responses to spaceflight, to biofouling of the built environments in space as well as the long-term health of astronauts. Bioinformatics shows that onboard the ISS, bacterial species produce far more amyloid and prion proteins than are currently verified, hence their role in bacterial ecosystems is largely unknown. As we propose there is a link between amyloid formation in space and biofilm production, this research should lead to new paths for biofilm remediation in space.
Focusing on undesired operator behaviors is pervasive in system design and safety management cultures. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. The study of human contributions to safety represents a vast and largely unexplored opportunity to learn. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform system design, safety-related decision making and policies depends upon identifying, collecting, and interpreting safety producing behaviors in addition to safety reducing behaviors.
Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors
No food or bioregenerative life support system (BLSS) is likely to be deployed on Mars prior to being fully vetted on lunar long duration missions. BLSS systems developed for lunar missions will trade well in terms of up mass and volume with prepackaged food for Mars missions, but only if they are reliable. There are significant differences in the gravitational and radiation environments between the moon and Mars, and one key challenging aspect for a BLSS on Mars missions is the extremely long durations. This results in the exposure of seeds, spores, or other dormant stages of organisms to both natural and radiation induced degradation over time frames of two or more years before they might be needed for growth. This can result in poor or no germination and sickly crops. A poorly functioning BLSS represents a threat to crew health. To ensure that a BLSS food production system will function well, we need to understand, at an early stage, the impact of these long missions on the biology. This is important to meet the Mars Exploration Program Analysis Group (MEPAG) goals.
Carbon dioxide due to human activities such as fossil fuel burning has well-known and concerning implications for global temperatures as well as terrestrial and marine ecosystems. Hence, an important objective of NASA Earth science is to observe and understand the variations in the global carbon cycle and the associated climate and ecosystem impacts in the context of past and projected anthropogenic emissions. In my presentation, I will tell the story of how some of my collaborators and I are using autonomous robotic vehicles and super computers to try to untangle the mysteries of the carbon cycle in the ocean, which depends in fascinating ways on atmospheric and ocean physics, marine ecosystem dynamics, as well as the chemistry of the seawater.
High intensity storms and coastal development negatively impact the ecosystems of Jobos Bay, Puerto Rico, by causing reductions in mangrove forests and degradation of water quality. These changes can compromise the ecosystem services, economic value, and cultural significance provided to the community by Jobos Bay. In collaboration with the Jobos Bay National Estuarine Research Reserve (JBNERR), the Jobos Bay Water Resources II team used Earth observations to investigate water quality, watersheds land use land cover (LULC) changes, and the impact of Hurricanes Maria and Irma on mangrove forest area. This information will improve JBNERR’s understanding of the impacts of development and weather events on Jobos Bay, and will inform future shoreline management decisions that ensure continued quality of the ecosystem. Mangrove extent was examined using imagery from Landsat 8 Operational Land Imager (OLI), WorldView 2 WV110, and WorldView 3 WV110. Imagery from Sentinel-2 MultiSpectral Instrument (MSI) was used to map watersheds LULC. Landsat 8 OLI and Sentinel-2 MSI data were analyzed with in situ data collected at the time of satellite overpass to investigate water quality in Jobos Bay. Reduction from 2017 mangrove extent was observed in 2018 following the hurricane events, and area of mapped extent was greater than 2018 in 2021. Water quality derived from satellite data was compared to in situ water quality measurements to inform future methodology decisions.
Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity.
Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity
Understanding the solar dynamics is critical for improving our capabilities to forecast the evolution of space weather conditions. We take advantage of currently available computational capabilities to model solar dynamics from the deep interior to the corona and investigate mechanisms that may drive space weather conditions. The simulations are performed using the 3D radiative MHD code StellarBox. Comparison of synthetic spectroscopic observables obtained from numerical simulations and actual observations allows us to uncover physical processes associated with observed phenomena. To facilitate a transition from modeling short-term physical phenomena to developing a reliable forecast-oriented model, we suggest using the data assimilation approach. It allows us to cross-analyze dynamo model solutions and observations and to consider possible uncertainties and errors. In this presentation, we briefly summarize current multi-scale modeling capabilities and results and discuss ongoing developments to build a reliable physics-based forecast-oriented model of solar activity
By combining measurements from MODIS and the CloudSat radar, we develop a parameterization scheme to quantify the combined microphysical controls by liquid water path (LWP) and cloud droplet number concentration (CDNC) of the probability of precipitation (PoP) in marine low cloud over tropical oceans. We demonstrate that the spatial-temporal variation of grid-mean in-cloud can be largely explained by the variation of the joint probability density function of LWP and CDNC in the phase space specified by the bivariate PoP (LWP and CDNC) function. Through a series of sensitivity tests guided by this understanding, we find that in the Southeastern Pacific and Atlantic the stratocumulus to cumulus transition of the is mainly due to the variation of CDNC while the annual cycle is mainly due to the variation of LWP. The results of this study provide a viable way to diagnose the root cause of warm rain problems in global climate models.
Solar flares are a fundamental component of solar eruptive events (SEEs), along with solar energetic particles (SEPs) and coronal mass ejections (CMEs). Flare emission is the first component of a SEE to impact the Earth’s ionosphere which can set the stage for the later effects of the space weather event. Magnetic reconnection drives SEEs by restructuring the solar coronalmagnetic field, liberating a tremendous amount of energy which is partitioned into various physical manifestations: particle acceleration,mass and magnetic-field eruption, atmospheric heating, and the subsequent emission of radiation as solar flares. In this white paper we discuss the observational and theoretical advances required in order to make substantial progress in understanding the physical processes acting during the impulsive phase of a flare. That is, the initial rapid and intense period in which a tremendous amount of energy is released over the span of several minutes, resulting in the dramatic broadband increase to the solar radiative output. A second white paper by us covers the flare’s gradual phase, that is the decay phase where processes occur over longer timescales.
Magnetic reconnection underlies many explosive phenomena in the heliosphere and in laboratory plasmas. New research capabilities in theory/simulations, observations, and laboratory experiments provide the opportunity to solve the grand scientific challenges summarized in this white paper. Success will require enhanced and sustained investments from relevant funding agencies, increased interagency/international partnerships, and close collaborations among the heliophysics, astrophysics, and laboratory plasma communities. These investments will deliver transformative progress in understanding magnetic reconnection and related explosive phenomena including space weather events.
This presentation provides an introduction to aircraft icing and describes NASA’s approach to understanding it. The presentation covers various topics on icing. A general overview of different types of icing is provided, which includes airframe icing, jet engine icing, and rotorcraft icing. The presentation discusses NASA’s approach to studying the various forms of aircraft icing, which includes conducting flight tests, performing ground-based icing wind tunnel tests, and developing computational tools that simulate the icing physics. An overview on the future of aviation and icing is provided. The presentation is largely high level, but also provides some technical icing physics discussion.
Solar flares are a fundamental component of solar eruptive events (SEEs), along with solar energetic particles (SEPs) and coronal mass ejections (CMEs). Flare emission is the first component of a SEE to impact the Earth’s atmosphere which can set the stage for the later arrival of the associated SEPs, CME, and space weather event. Magnetic reconnection drives SEEs by restructuring the solar coronal magnetic field, liberating a tremendous amount of energy which is partitioned into various physical manifestations: particle acceleration, mass and magnetic-field eruption, atmospheric heating, and the subsequent emission of radiation as solar flares. In this white paper we discuss the observational and theoretical advances required in order to make substantial progress in understanding the physical processes acting during the gradual phase of a flare. That is, the decay period, following the initial rapid release of energy during the impulsive phase (see our other white paper). In particular we want to address the unknown processes that sustain the long decay phase of flares and identify the unknown mechanism and magnitude of continued energy injection during the gradual phase.