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NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗

NASA NTRSDate not supplied

X-57 Systems Engineering Lessons Learned

The X-57 Maxwell is an electric aircraft based on a 4-passenger, twin engine Tecnam P2006T General Aviation aircraft. The X-57 project originally envisioned a straightforward integration of commercial-off-the-shelf hardware components and software into a novel configuration to demonstrate the aerodynamic and performance benefits of Distributed Electric Propulsion (DEP). The project was initially started with a high-risk venture capitalist approach under NASA’s Convergent Aeronautics Solutions (CAS) project, which led to an initial philosophy of Project Management “light” (which was then interpreted as Systems Engineering (SE) “light”). As the project matured, it was forced to transition to one with increasing SE-rigor as the project scope changed, hardware and software deficiencies were found, and the team realized the magnitude of the technical and integration challenges. In hindsight, these technical challenges came in part from an overly optimistic technology readiness assessment (TRA) at the beginning of the project, which resulted in the project assuming that little to no subsystem development would be required. The project’s approach to systems engineering evolved throughout three separate informal phases of the project as it underwent two key transitions as a result of the team wrestling with the technical challenges and resultant changing project scope. This paper discusses the assumptions, approaches, and challenges encountered from a Systems Engineering standpoint in each of the three informal phases of the X-57 project. This paper also provides recommendations on how future projects can apply Systems Engineering best practices upfront along with a realistic TRA to aid projects that find themselves with similar challenges.

Systems Engineering↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Distributed↗

NASA NTRS1998

300 GeV Observations of Unidentified EGRET Sources. A Search for TeV Counterparts to BATSE Gamma-ray Bursts

There are few things more intriguing in high energy astrophysics than the study of the highest energy particles in the universe. Where and how these particles achieve their extreme energies is of interest not only to the astrophysicist but also to the particle physicist. At GeV and TeV energies the problem is manageable since the physics is known and the acceleration processes feasible. But the energy spectrum extends to 10(exp 20)Ev and there the problem of their origin is both more difficult and interesting; in fact at these high energies we do not even know what the particles are. The study of the origin and distribution of relativistic particles in the universe has been a challenge for more than 80 years but it is only in recent years that the technology has become available to really address the question. Although something can be learned from studies of composition and energy spectrum, the origins (and thence the acceleration mechanisms) can only come from the direct study of the neutral particle component (in this respect the highest energy particles are effectively neutral since they are virtually undeflected). The feasible channels of investigation are therefore the study of the arrival directions of: (1) TeV photons (covered by the following U.S. experiments: STACEE, Whipple/VERITAS, MILAGRO and, to some extent, by EGRET/GLAST); (2) neutrinos of TeV energy and above (AMANDA/KM3); (3) the highest energy cosmic rays (HiRes, Auger). While these studies represent a form of astronomy they are the astronomy of the extraordinary universe, the universe populated by the most dynamic and physically exciting objects, the universe of the high energy astrophysicist whose cosmic laboratories represent conditions beyond anything that can be duplicated in a terrestrial laboratory. This extraordinary astronomy may say little about the normal evolution of stars and galaxies but it opens windows into cosmic particle acceleration where new and strange physical processes take place.

Weekes, Trevor C.↗

NASA NTRS2013

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Hurricanes↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. There exists a gamut of approaches to solving merging and intersection crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to solving these problems that is based on distributed cooperation between the UAVs/UASs and the infrastructure. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing UAVs/UASs to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them.

Distributed↗

NASA NTRS2003

Intelligent Machines in the 21st Century: Automating the Processes of Inference and Inquiry

The last century saw the application of Boolean algebra toward the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines. in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. However, modern intelligent machines work by inferring knowledge using only their pre-programmed prior knowledge and the data provided. They lack the ability to ask questions, or request data that would aid their inferences. Recent advances in understanding the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we identified the algebra of questions as the free distributive algebra, which now allows us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper we describe this logic of inference and inquiry using the mathematics of partially ordered sets and the scaffolding of lattice theory, discuss the far-reaching implications of the methodology, and demonstrate its application with current examples in machine learning. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them to not only make inferences from data, but also decide which question to ask, experiment to perform, or measurement to take given what they have learned and what they are designed to understand.

Knuth, Kevin H.↗

NASA NTRSDate not supplied

Parameterization of Vertical Cloud Distribution from C3M and MERRA Data Using ML Method

Clouds play a key role in regulating the hydrological cycle and the Earth's radiative energy budget. However, global climate models (GCMs) with a horizontal grid spacing on the order of 100 km have limitations in representing sub-grid cloud dynamics with spatial scales on the order of 1 km, leading to potential uncertainties in cloud radiative feedback on the global scale. In our research, we will leverage the capabilities of Deep Machine Learning (DML) methods to construct parameterizations of sub-grid volumetric cloud fraction (VCF), which is the frequency of occurrence on a grid volume accumulated in the horizontal and vertical directions. Our investigation delves into the intricate relationship between VCF obtained from the NASA CALIPSO-CloudSat-CERES-MODIS (CCCM) satellite observation data and 3-D MERRA-2 reanalysis meteorological profiling data (e.g., wind, relative humidity, temperature). Through a comprehensive one-year data training utilizing the Sequence to Sequence DML method, we have successfully disentangled the complicated cloud formation dynamics across diverse meteorological conditions through a day-to-day analysis framework. Preliminary findings reveal promising statistical agreements in geographical and vertical distributions and seasonal variations of volumetric cloud fraction between ML prediction and satellite measurements. These results underscore the aptitude of our DML model to discern underlying cloud physical processes and accurately represent sub-grid cloud formation dynamics. Additionally, we have also employed trained neural network to analyze uncertainties arising from errors in meteorological data, further enhancing the robustness of our VCF parameterization.

Shan Zeng↗

NASA NTRS2022

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

PM2.5↗

NASA NTRS2003

Astrobiology Extends Biology into Deep Time and Space

To understand our own origins and to search for biospheres beyond Earth, we need a more robust concept of life itself. We must learn how to discriminate between attributes that are fundamental to all living systems versus those that represent principally local outcomes of long-term survival on Earth. We should identify the most basic environmental needs of life, chart the distribution of other habitable worlds, and understand the factors that created their distribution. Studies of microbial communities and the geologic record will be summarized that offer clues about the early evolution of our own biosphere as well as the signatures of life that we might find in the heavens.

Desmarais, David↗

NASA NTRS2020

Case Study of Using Flocad to Model a Ground Test Station Ln2 Heat Exchanger

This paper presents a case study of using FLOCAD to model the heat transfer and boiling flow behavior of a Liquid Nitrogen (LN2) ground station equipment heat exchanger. The unique aspect of this work is that is one of very few studies documenting the use of FLOCAD in the spacecraft thermal control community. The results of the simulations described herein were used to guide and plan the execution of thermal vacuum testing spacecraft hardware using a ground test heat exchanger such as the one modeled herein. The paper will review the theory of two-phase boiling flow and correlations used by SINDA/FLUINT as well as the terminology and nomenclature used within Thermal Desktop / FLOCAD regarding two-phase boiling flow heat transfer modeling and simulation. Results for using the ground station heat exchanger as a heat sink in a thermal vacuum test set up are shown in order to demonstrate the set-up and execution of a typical FLOCAD model. Results indicate that properly modeling of the two-phase behavior is critical in order to ascertain the time constant associated with the heat exchanger, as well as understanding the temperature distribution across the test equipment and prediction of required LN2 flowrates to be used during testing. The paper is meant to augment the FLOCAD user’s manual and serve as a tutorial for thermal engineers and analysts wishing to learn and apply FLOCAD to industrial problems.

Anderson, Kevin R.↗

NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗

NASA NTRS2021

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤ 15 cGy of individual Galactic Cosmic Radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in Attentional Set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the Simple Discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the Compound Discrimination (CD) stage. On a subject-based level, implementing Machine Learning (ML) classifiers such as the Gaussian Naïve Bayes, Support Vector Machine, and Artificial Neural Networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreenperformance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Feis the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4Hein SD and 28Siin CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for morerobust predictionsof cognitive decrements due to space radiation exposure.

space radiation↗

NASA NTRS1975

Digital computer programs for generating oblique orthographic projections and contour plots

User and programer documentation is presented for two programs for automatic plotting of digital data. One of the programs generates oblique orthographic projections of three-dimensional numerical models and the other program generates contour plots of data distributed in an arbitrary planar region. A general description of the computational algorithms, user instructions, and complete listings of the programs is given. Several plots are included to illustrate various program options, and a single example is described to facilitate learning the use of the programs.

Giles, G. L.↗

NASA NTRS1980

Photovoltaics as a terrestrial energy source. Volume 1: An introduction

Photovoltaic (PV) systems were examined their potential for terrestrial application and future development. Photovoltaic technology, existing and potential photovoltaic applications, and the National Photovoltaics Program are reviewed. The competitive environment for this electrical source, affected by the presence or absence of utility supplied power is evaluated in term of systems prices. The roles of technological breakthroughs, directed research and technology development, learning curves, and commercial demonstrations in the National Program are discussed. The potential for photovoltaics to displace oil consumption is examined, as are the potential benefits of employing PV in either central-station or non-utility owned, small, distributed systems.

Smith, J. L.↗

NASA NTRS1985

Heterogeneous distributed query processing: The DAVID system

The objective of the Distributed Access View Integrated Database (DAVID) project is the development of an easy to use computer system with which NASA scientists, engineers and administrators can uniformly access distributed heterogeneous databases. Basically, DAVID will be a database management system that sits alongside already existing database and file management systems. Its function is to enable users to access the data in other languages and file systems without having to learn the data manipulation languages. Given here is an outline of a talk on the DAVID project and several charts.

Jacobs, Barry E.↗

NASA NTRS1999

The Role of Metadata Standards in EOSDIS Search and Retrieval Applications

Metadata standards play a critical role in data search and retrieval systems. Metadata tie software to data so the data can be processed, stored, searched, retrieved and distributed. Without metadata these actions are not possible. The process of populating metadata to describe science data is an important service to the end user community so that a user who is unfamiliar with the data, can easily find and learn about a particular dataset before an order decision is made. Once a good set of standards are in place, the accuracy with which data search can be performed depends on the degree to which metadata standards are adhered during product definition. NASA's Earth Observing System Data and Information System (EOSDIS) provides examples of how metadata standards are used in data search and retrieval.

Pfister, Robin↗