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All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

The AAMP5/AAMP-FV project

This presentation describes a project, formal verification of the microcode in the AAMP5 microprocessor, conducted to explore how formal techniques for specification and verification could be introduced into an industrial process. Sponsored by the Systems Validation Branch of NASA Langley and by Collins Commercial Avionics, a division of Rockwell International, it was conducted by Collins and the SRI International Computer Science Laboratory. The project consisted of specifying in the PVS language developed by SRI a portion of a Rockwell proprietary microprocessor, the AAMP5, at both the instruction set and register-transfer levels and using the PVS theorem prover to prove the microcode correct for a representative subset of instructions. While this presentation includes a brief technical overview, its emphasis is on the lessons learned in using PVS for an example of this size and the implications for using formal methods in an industrial setting. The central result of this project was to demonstrate the feasibility of formally specifying a commercial microprocessor and the use of mechanical proofs of correctness to verify microcode. This is particularly significant since the AAMP5 was not designed for formal verification, but to provide a more than three fold performance improvement, by pipelining instruction execution, while remaining object code compatible with the earlier AAMP2. As a consequence, the AAMP5 is one of the most complex microprocessors to which formal methods have been applied. Another key result was the discovery of both actual and seeded errors. Two actual microcode errors were discovered and corrected during development of the formal specification, illustrating the value of simply creating a precise specification. Two seeded errors were systematically uncovered while doing correctness proofs. One of these was an actual error that had been discovered after first fabrication but left in the microcode provided to SRI. The other error was designed to be unlikely to be detected by walkthroughs, testing, or simulation. Several other results emerged during the project, including the ease with which practicing engineers became comfortable with PVS, the need for libraries of general purpose theories, the usefulness of formal specification in revealing errors, the natural fit between formal specification and inspections, the difficulty of selecting the best style of specification for a new problem domain, the high level of assurance provided by proofs of correctness, and the need to engineer proof strategies for reuse.

Miller, Steven P.↗
NASA NTRS1999

Its a Wind, Its a Wave, Its Two Phenomena in One: Jerry Schubert, Superrotation, and the UV Markings on Venus

In the early 1970's, ground-based astronomers had already discovered that Ultraviolet (UV) cloud markings on Venus reappeared every 4 days. When radar evidence later revealed a 243-day rotation period for the solid planet, planetary scientists were faced with a quandary: Could the Venus atmosphere really move 60 times as fast as the planet below, or were the apparent movements of the UV features just an illusion caused by propagating waves? The former explanation seemed unlikely - a planet that hardly rotates should generate only a very sluggish circulation. The historical impact of Jerry Schubert's moving flame theory was twofold: It was the forerunner of current thermal tide explanations of the cloud-level superrotation, but it was also the first plausible mechanism for explaining a seemingly inconsistent set of observations. In 1974, Mariner 10 acquired UV images of the Venus clouds at unprecedented levels of detail. Although few have noted it, this began the shift of planetary atmospheric research primarily from the domain of astronomy to that of meteorology. Jerry was among the first scientists to apply terrestrial meteorology to the analysis of planetary data. At that time a young UCLA graduate student with plans to do research on mantle convection, but having flunked the solid earth geophysics section of his departmental comprehensive exam, was gently invited by Jerry to switch to atmospheric science. Jerry suggested that the Venus UV features could be revealing both superrotating winds and planetary-scale waves at the same time, and that we could distinguish the two by looking at motions on different spatial scales. This was my first science research lesson - the complexity of real geophysical systems. Over the next couple of years I was inculcated with Jerry's philosophy of a comprehensive, rigorous approach to research, which manifested itself as a scouring of the literature and the UCLA meteorology faculty to learn about every possible type of planetary-scale wave. The resulting identification of large-scale UV features on Venus as the product of Kelvin and Rossby-type wave motions was validated by extensive Pioneer Venus observations a decade later and remains one of the best examples of the use of terrestrial knowledge to understand other planets.

DelGenio, Anthony↗
NASA NTRS2007

The Business Case for Automated Software Engineering

Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the 'local tuning' problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.

model evaluation↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
NASA NTRS1997

AB Initio Study of the Structure and Spectroscopic Properties of Halogenated Thioperoxy Radicals

Thioperoxy (XSO or XOS) radicals exist in a variety of chemical environments, and they have as a consequence drawn some interest. HSO, an important species in the chemistry of the troposphere, has been examined both experimentally. The halogenated (X = F, Cl or Br) peroxy species and isovalent thioperoxy species have been studied less, but they too are potentially interesting because oxidized sulfur species and halogen sources are present in the atmosphere. Learning the fate of XSO and XOS radicals is important to understanding the atmospheric oxidation chemistry of sulfur compounds. Of these, FSO and ClSO are particularly interesting because they have been directly detected spectroscopically. Recent studies in our laboratory on the photochemistry of thionyl halides (X2SO; where X = F or Cl) have suggested new ways to generate XSO species. The laser-induced photodissociation of thionyl fluoride, F2SO, at 193 nm and thionyl chloride, ClSO, at 248 nm is characterized by a radical mechanism, X2SO -> XSO + X. The structure of FSO has been characterized experimentally by Endo et cd. employing microwave spectroscopy. Using the unrestricted Hartree-Fock (UHF) self-consistent field (SCF) method, Sakai and Morokuma computed the electronic structure of the ground (sup 2)A" and the first excited (sup 2)A' states of FSO. Electron correlation was not taken into account in their study. In a laser photodissociation experiment, Huber et al. identified ClSO mass spectromctrically. ClSO has also been detected in low temperature matrices by EPR and in the gas phase by far IR laser magnetic resonance. Although the structure of FSO is known in detail, the only study, experimental or theoretical, of CISO has been an ab initio HFSCF study by Hinchliffe. Electron correlation corrections were also excluded from this study. In order to better understand the isomerization and dissociation dynamics of the radical species, we have performed ab initio correlated studies of the potential energy surfaces (PES) of ClSO and its isomer ClOS at the QCISD(T)/6-31 G* level of theory. For FSO and FOS, more extensive QCISD/6-31 1G(2df) calculations have been possible, and the results are summarized here.

Munoz, Luis A.↗
NASA NTRS2000

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

Non-NASA Center↗
NASA NTRSDate not supplied

Understanding the Impact of Unobservable Variables on the Performance of Predictive Models: The Need for Feature Space Partitioning and Fusion

When developing predictive models over a dataset, the model is globally optimized across the entire feature space to learn a decision boundary. However, when unobservable variables—which cannot be measured or estimated—interact with the observable variables, this can negatively impact the optimization applied to the decision boundary since the data samples introduced by unobservable variables may have little to no association with the applied global optimization. This, consequently, penalizes the entire decision boundary and model performance. This paper examines some of the detrimental effects of unobservable variables, particularly their role in creating new modes in the distribution of observable variables and reducing the separability of class distributions. Such challenges result in skewed or warped decision boundaries and decreased accuracy of model predictions, particularly for interpretable models like logistic regression and decision trees. Through two illustrative case examples, we highlight the need to address the challenges imposed by unobservable variables. We propose a strategy to mitigate these challenges by creating local regions within the feature space through partitioning. This enables the optimization of local models within the regions to overcome the impact of unobservability in different feature space localities. Research into a more sophisticated partitioning strategy and where the partition should be relative to the sample of interest is left as future work. Through the analysis of the impact of unobservability and the development of a partitioning method, we demonstrate the clear need for a partitioning strategy that integrates knowledge from multiple local models to estimate risk factors using information fusion. Thus, we establish the foundation and motivation for using partitioning and information fusion to overcome the effects of unobservability in predictive models. Formal fusion methods, such as Dempster-Shafer theory, can better leverage the information from local regions to improve the performance of interpretable predictive models in the presence of unobservable variables.

Time Series Data↗
NASA NTRS1999

A Survey of Collective Intelligence

This chapter presents the science of "COllective INtelligence" (COIN). A COIN is a large multi-agent systems where: i) the agents each run reinforcement learning (RL) algorithms; ii) there is little to no centralized communication or control; iii) there is a provided world utility function that, rates the possible histories of tile full system. Tile conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather that approach begins with explicit modeling of the overall system's dynamics, followed by detailed hand-tuning of the interactions between the components to ensure that they "cooperate" as far as the world utility is concerned. This approach is labor-intensive, often results in highly non-robust systems, and usually results in design techniques that, have limited applicability. In contrast, with COINs we wish to solve the system design problems implicitly, via the 'adaptive' character of the RL algorithms of each of the agents. This COIN approach introduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we do not have phenomena like the tragedy of the commons, or Braess's paradox? Although still very young, the science of COINs has already resulted in successes in artificial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's "El Farol bar problem". It is expected that as it matures not only will COIN science expand greatly the range of tasks addressable by human engineers, but it will also provide much insight into already established scientific fields, such as economics, game theory, or population biology.

Wolpert, David H.↗
NASA NTRS2020

Convolutional Neural Network for Transition Modeling Based on Linear Stability Theory

Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally, the modeling of transition has relied on correlation-based empirical formulas based on integral quantities such as the shape factor of the boundary layer. However, in many applications of computational fluid dynamics, the shape factor is not straightforwardly available or not well-defined. We propose using the complete velocity profile along with other quantities (e.g., frequency, Reynolds number) to predict the perturbation amplification factor. While this can be achieved with regression models based on a classical fully connected neural network, such a model can be computationally more demanding. We propose a novel convolutional neural network inspired by the underlying physics as described by the stability equations. Specifically, convolutional layers are first used to extract integral quantities from the velocity profiles, and then fully connected layers are used to map the extracted integral quantities, along with frequency and Reynolds number, to the output (amplification ratio). Numerical tests on classical boundary layers clearly demonstrate the merits of the proposed method. More importantly, we demonstrate that, for Tollmien-Schlichting instabilities in two-dimensional, low-speed boundary layers, the proposed network encodes information in the boundary layer profiles into an integral quantity that is strongly correlated to a well-known, physically defined parameter – the shape factor.

Laminar-turbulent transition↗
NASA NTRS2024

Spacecraft Modeling, Attitude Determination, and Control: Quaternion-Based Approach

Spacecraft attitude determination and control is an important part of a spacecraft to achieve its designed mission. As of today, many spacecrafts have been successfully launched, and most of them have performed well as they were designed. Many research papers have been published to address the attitude determination and control design problems. Several text books are available for students to learn the technology and for engineers to use as references. The most popular spacecraft models for attitude determination algorithms and control design methods are the Euler angle models and the quaternion models . The Euler angle models have been proved very efficient as the linearized models are controllable, and all standard linear control system design methods are directly applicable. The drawbacks related to the Euler angle methods are (a) the designs based on linearized models may not globally stabilize the original nonlinear spacecraft, i.e., the design may not work when the attitude of the spacecraft is far away from the point where the linearization is performed; (b) the models depend on the rotational sequences, this can be error prone if several teams work on the same project and they use different rotational sequences; (c) for any rotational sequence, there is a singular point where the model is not applicable; and (d) since most attitude determination methods use quaternion to represent the spacecraft attitude, there is a need to transform quaternion into Euler angles. On the other hand, for quaternion models, people have found controllers that can globally stabilize nonlinear spacecraft systems; the models do not depend on rotational sequences and they have no singular point; and the quaternion is provided by attitude determination system and ready to use. The main problem with the quaternion model based control system design is that the linearized quaternion model is not controllable. Therefore, most published design methods heavily rely on Lyapunov functions for the nonlinear spacecraft system. But there is no systematic way to obtain a desired Lyapunov functions. Moreover, the Lyapunov function based designs focus on the closed-loop system stability but pay little attention to the closed-loop system performance. In a series of papers, the author proposed some reduced quaternion models which lead to some controllable linearized spacecraft models. Therefore, all standard linear system theory can be directly applied to analyze and design the spacecraft control systems. We showed that, in some cases, the designed control system is not only optimal for the linearized system, but also globally stabilize the original nonlinear system . Clearly, the reduced quaternion models do not depend on rotational sequences. Due to the special structure of the linearized spacecraft model, some most important design methods, such as LQR design and robust pole assignment design are very simple, enjoy the analytical solutions for some problems, have direct connection to the performance measures, such as settling time, rising time , and percentage of overshoot . All these features are attractive for high quality control system designs. The idea mentioned above is then extended to more spacecraft control problems using specific actuators such as magnetic torque bars and control momentum gyroscopes. These types of actuators may not provide exactly desired torques. Most existing methods use different conversions to get approximate solutions, meaning that these actuators may generate a torque close to but not equal to the desired one. Using the reduced quaternion models that incorporate the actuators into the system model, the control inputs are not torques but the operational parameters. The main benefit of this idea is that the control actions are not approximate but accurate. As all actuators have their operational limit, design with input constraints are also considered in this book by using recently developed interior-point optimization techniques. This book grows up from my research on the spacecraft attitude determination and control design methods in more than a decade which is focused on using reduced quaternion models because of their merits stated above. The book provides all necessary background materials on orbital dynamics, rotations and quaternion, frequently used reference frames, transformations between reference frames, space environment and disturbance torques, ephemeris astronomical vector calculations and measurement instruments, spacecraft control actuators and their models, so that the readers will get a global picture and can apply all these information into the spacecraft system modeling, attitude determination, and spacecraft control system designs, which is the main purpose of this book. This book is different from existing books in that we focus on quaternion based spacecraft control system designs and we consider only attitude control system design related problems, from spacecraft modeling, to attitude determination and estimation, to control system design method selection, to control algorithm development, and to the simulation of the control system designs. Moreover, this book addresses different attitude control tasks in the spacecraft life cycle, including spacecraft maneuver, orbit raising, attitude control, and rendezvous. Finally, this book emphasizes the state space design methods rather than the classical frequency design methods.

Attitude determination and control↗
NASA NTRS2017

The Pathway to a Safe and Effective Medication Formulary for Exploration Spaceflight

PURPOSE: Exploration space missions pose several challenges to providing a comprehensive medication formulary designed to accommodate the size and space limitations of the spacecraft; while addressing the individual medications needs and preferences of the Crew; the negative outcome of a degrading inventory over time, the inability to resupply before expiration dates; and the need to properly forecast the best possible medication candidates to treat conditions that will occur in the future. METHODS: The Pharmacotherapeutics Discipline has partnered with the Exploration Medical Capabilities (ExMC) Element to develop and propose a research pathway that is comprehensively focused on evidence-based models and theories, as well as on new diagnostic tools and treatments or preventive measures aimed at closure of the Med02 “Pharmacy” Gap; defined in the Human Research Program’s (HRP) risk-based research strategy. The Med02 Gap promotes the challenge to identify a strategy to ensure that medications used to treat medical conditions during exploration space missions are available, safe, and effective. It is abundantly clear that pharmaceutical intervention is an essential component of risk management planning for astronaut healthcare during exploration space. However, the quandary still remains of how to assemble a formulary that is comprehensive enough to prevent or treat anticipated medical events; and is also chemically stable, safe, and robust enough to have sufficient potency to last for the duration of an exploration space mission. In cases where that is not possible, addressing this Gap requires exploration of novel drug development techniques, dosage forms, and dosage delivery platforms that enhance chemical stability as well as therapeutic effectiveness. RESULTS: The proposed research pathway outlines the steps, processes, procedures, and a research portfolio aimed at identifying a capability that will provide a safe and effective pharmacy for any specific exploration Design Reference Mission (DRM). The proposed approach to building this research portfolio is to seek research projects that concentrate on four major focus areas; (1) Formulary selection, (2) Formulary potency and shelf life, (3) Formulary safety and toxicity, and (4) Novel technology and innovation such as portable real-time chemical analysis innovative drug therapies and dosage and delivery platforms. CONCLUSION: The research pathway has been completed and presented to the HRP. In spring 2017, it is scheduled to be reviewed by a panel of pharmaceutical and clinical experts that will evaluate the scientific merit and operational feasibility of the research pathway, as well as make suggestions for any warranted additions or improvements. Once finalized, the ExMC Element will proceed with the execution of this research pathway with the goal of gathering as much data, and learning as much as possible, to provide a safe and effective pharmaceutical formulary for use during exploration missions.

Daniels, V. R.↗
NASA NTRS2016

Space Launch System Vibration Analysis Support

The ultimate goal for my efforts during this internship was to help prepare for the Space Launch System (SLS) integrated modal test (IMT) with Rodney Rocha. In 2018, the Structural Engineering Loads and Dynamics Team will have 10 days to perform the IMT on the SLS Integrated Launch Vehicle. After that 10 day period, we will have about two months to analyze the test data and determine whether the integrated vehicle modes/frequencies are adequate for launching the vehicle. Because of the time constraints, NASA must have newly developed post-test analysis methods proven well and with technical confidence before testing. NASA civil servants along with help from rotational interns are working with novel techniques developed and applied external to Johnson Space Center (JSC) to uncover issues in applying this technique to much larger scales than ever before. We intend to use modal decoupling methods to separate the entangled vibrations coming from the SLS and its support structure during the IMT. This new approach is still under development. The primary goal of my internship was to learn the basics of structural dynamics and physical vibrations. I was able to accomplish this by working on two experimental test set ups, the Simple Beam and TAURUS-T, and by doing some light analytical and post-processing work. Within the Simple Beam project, my role involves changing the data acquisition system, reconfiguration of the test set up, transducer calibration, data collection, data file recovery, and post-processing analysis. Within the TAURUS-T project, my duties included cataloging and removing the 30+ triaxial accelerometers, coordinating the removal of the structure from the current rolling cart to a sturdy billet for further testing, preparing the accelerometers for remounting, accurately calibrating, mounting, and mapping of all accelerometer channels, and some testing. Hammer and shaker tests will be performed to easily visualize mode shapes at low frequencies. Short analytical projects using MATLAB were also assigned to aid in research efforts. These included integration of acceleration data for comparison to measured displacement data. Laplace and Fourier transforms were also investigated to determine viability as a method of modal decoupling. In addition to these projects, I was also able contribute work that would benefit future interns and the division as a whole. I gave a short presentation and answered questions to aid in the recruitment of subsequent interns and co-ops for the division. I also assisted in revisions and additions to Intern/Co-Op Handbook to provide incoming employees with background information on the organization they are about to work for. I further developed tutorial on Pulse software, which was used for data acquisition for both experiments and will be helpful to interns and engineers that may be unfamiliar to the software. I gained a diverse range of experience throughout my internship. I was introduced to advanced dynamics and analytical techniques. This was through new experience with both hands on experimentation and analytical post processing methods. I was exposed to the benefits of interdepartmental collaboration and developed stronger skills in time management by coordinating two different tests at once. This internship provided an excellent opportunity to see how engineering theories applied to real life scenarios, and an introduction to how NASA/JSC solves technical problems.

Johnson, Katie↗
NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
NASA NTRS2023

Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor

Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.

Drought↗
NASA NTRS1996

Visualization of High Latitude Ion Upflow in Support of the Image Mission

The study of the magnetosphere is a 400 year old science that began with the publication by Gilbert, in 1600, of his hypotheses that the Earth was a giant magnet. Since then we have learned many things about the magnetosphere, particularly in the last 40 years of the space age, but we still have many unanswered questions. In spite of the many thousands of observations of this system we still lack a global understanding of how it works. This is due to its large size and tenuous nature that mean that any measurement made of the fields or particles involved only give one a knowledge of the local conditions at a given time. To gain a global perspective through such observations would require the simultaneous operation of thousands of satellites spread throughout the magnetospheric system in addition to observations made on the ground. Such a program would be impractical at least from financial considerations. What is needed for the advancement of magnetospheric physics is to develop the same capabilities that astrophysicists, solar physicists and meteorologists have been using for years --- the ability to stand back from the object under study and see it in its entirety. The challenge for doing this for the magnetosphere is that the particle densities are very low and the material is, for the most part, not luminous. In the last 25 years several ideas have been proposed that would allow at least the imaging of certain portions of the magnetosphere. These include imaging of the plasmasphere through the resonant scattering of solar 304 A from He+ ions, imaging of various hot plasma populations (i.e. the ring current, plasmasheet, upflowing ionospheric ions, etc.) from the neutral atoms that result when ions of these populations charge exchange with the hydrogen geocorona, and imaging the aurora at various wavelengths in the far ultraviolet. In addition, a novel technique for probing various boundaries in the magnetosphere by bouncing low frequency radio waves off of them has been extensively studied. Such a technique is analogous to the way the under water world can be probed with sonar. About five years ago NASA convened a science working group to study the possibility of flying a magnetospheric imaging mission. This resulted in a number of proposals for such a mission, one of which was selected to be the first MIDEX mission, to be launched in early 2000. The mission is called IMAGE (Imager for Magnetopause to Aurora Global Exploration) and its P.I. is J. Burch at SwRI. The IMAGE spacecraft will carry imagers to view the plasmasphere, aurora, ring current, inner plasmasheet, and upflowing ionospheric ions as well as a radio sounder to probe the location, shape and dynamics of the magnetopause, plasmapause, etc. Between its selection last April and the non advocacy mission review, which takes place next spring, the IMAGE teams needs to further refine the design of the mission and its instruments. The theory and modeling (T&M) subgroup of this team has the task of demonstrating what kind of images the instruments on IMAGE will see as well as showing that useful scientific information can be extracted from such images. As a central element to the efforts of the T&M subgroup we have decided to simulate and create synthetic images for the magnetic cloud event of October, 1995. In this event a large cloud, with high plasma densities and strong magnetic fields, ejected from the sun collided with the earth's magnetosphere triggering a three day period of intense magnetic storms and substorms. This event was observed from a number of different spacecraft and on the ground so we have a good data set to work with. In our work we will place the IMAGE spacecraft in the magnetosphere on its proposed orbit, with its proposed instruments, to see what it would see had it been there. Existing models of the plasmasphere, ring current and magnetopause will be run for this event to give the structures for the imaging instruments. There are several models which are lacking and which need to be developed. These include a model for the cusp, the inner plasmasheet and the upflowing ions. My task this summer was to develop the upflowing ion model and use it to create synthetic images.

Wilson, Gordon R.↗