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DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Basin-Scale Assessment of the Land Surface Water Budget in the National Centers for Environmental Prediction Operational and Research NLDAS-2 Systems

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems.

NLDAS-2↗

DOE OSTI2023

Dataset for 'Ombadi et al. (2023). A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature'

This package contains the main codes, sample input data and main result files to reproduce the analysis and results presented in the article: “Ombadi et al. (2023), A warming-induced reduction in snow fraction amplifies rainfall extremes, Nature”. The folder consists of the following: (1) “Raw data”: a folder that contains sample input data which is used in some of the codes for demonstration purposes. It also contains data that was not pre-processed such as Elevation data; (2) “Results”: this folder contains files of the main results presented in the paper including: “Annual-Max-Series”, “Change-rainfall-extremes”, “Change-snow-fraction”, “Warming levels_By scenario_model_year” and “Masks”. Description of these folders is detailed in the "Readme.rtf" file; (3) Python jupyter notebooks (Extract_Annual Max Series (AMS).ipynb, Elevation-dependent amplification of rainfall extremes.ipynb, Sensitivity_to_global_warming.ipynb) demonstrate the main steps of analysis. Further description of those notebooks is provided in the "Readme.rtf" file; (4) R code for extreme value analysis (Extreme_Value_Analysis.R). The sample and pre-processed dataset in "Raw data" is obtained from publicly available repositories of CMIP6 and ERA5 datasets; see Methods for more detail. This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science program).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

NASA/GSFC Research Activities for the Global Ocean Carbon Cycle: A Prospectus for the 21st Century

There are increasing concerns that anthropogenic inputs of carbon dioxide into the Earth system have the potential for climate change. In response to these concerns, the GSFC Laboratory for Hydrospheric Processes has formed the Ocean Carbon Science Team (OCST) to contribute to greater understanding of the global ocean carbon cycle. The overall goals of the OCST are to: 1) detect changes in biological components of the ocean carbon cycle through remote sensing of biooptical properties, 2) refine understanding of ocean carbon uptake and sequestration through application of basic research results, new satellite algorithms, and improved model parameterizations, 3) develop and implement new sensors providing critical missing environmental information related to the oceanic carbon cycle and the flux of CO2 across the air-sea interface. The specific objectives of the OCST are to: 1) establish a 20-year time series of ocean color, 2) develop new remote sensing technologies, 3) validate ocean remote sensing observations, 4) conduct ocean carbon cycle scientific investigations directly related to remote sensing data, emphasizing physiological, empirical and coupled physical/biological models, satellite algorithm development and improvement, and analysis of satellite data sets. These research and mission objectives are intended to improve our understanding of global ocean carbon cycling and contribute to national goals by maximizing the use of remote sensing data.

Gregg, W. W.↗

NASA NTRSDate not supplied

Clouds and the Earth's Radiant Energy System

The Clouds and the Earth's Radiant Energy System (CERES) is a key component of the Earth Observing System (EOS) program. The CERES instrument provides radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999, and two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4,2002. [Mission Objectives] The scientific justification for the CERES measurements can be summarized by three assertions: (1) changes in the radiative energy balance of the Earth-atmosphere system can cause long-term climate changes (e.g., carbon dioxide inducing global warming); (2) besides the systematic diurnal and seasonal cycles of incoming solar energy, changes in cloud properties (amount, height, optical thickness) cause the largest changes of the Earth's radiative energy balance; and (3) cloud physics is one of the weakest components of current climate models used to predict potential global climate change. CERES has four main objectives: 1) For climate change analysis, provide a continuation of the ERBE record of radiative fluxes at the top of the atmosphere (TOA), analyzed using the same algorithms that produced the ERBE data. 2) Double the accuracy of estimates of radiative fluxes at TOA and the Earth's surface. 3) Provide the first long-term global estimates of the radiative fluxes within the Earth's atmosphere. 4) Provide cloud property estimates that are consistent with the radiative fluxes from surface to TOA. [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

Wielicki, Bruce A.↗

NASA NTRS1997

Global Analysis, Interpretation and Modelling: An Earth Systems Modelling Program

The Goal of the GAIM is: To advance the study of the coupled dynamics of the Earth system using as tools both data and models; to develop a strategy for the rapid development, evaluation, and application of comprehensive prognostic models of the Global Biogeochemical Subsystem which could eventually be linked with models of the Physical-Climate Subsystem; to propose, promote, and facilitate experiments with existing models or by linking subcomponent models, especially those associated with IGBP Core Projects and with WCRP efforts. Such experiments would be focused upon resolving interface issues and questions associated with developing an understanding of the prognostic behavior of key processes; to clarify key scientific issues facing the development of Global Biogeochemical Models and the coupling of these models to General Circulation Models; to assist the Intergovernmental Panel on Climate Change (IPCC) process by conducting timely studies that focus upon elucidating important unresolved scientific issues associated with the changing biogeochemical cycles of the planet and upon the role of the biosphere in the physical-climate subsystem, particularly its role in the global hydrological cycle; and to advise the SC-IGBP on progress in developing comprehensive Global Biogeochemical Models and to maintain scientific liaison with the WCRP Steering Group on Global Climate Modelling.

Moore, Berrien, III↗

NASA NTRSDate not supplied

Enabling Space Biology Knowledge Discovery Through Biospecimen Sharing: The NASA Biological Institutional Scientific Collection and Space Microbial Culture Collection

NASA and international partners have conducted experiments in space to understand the biological impacts and address hazards to health. The resulting basic and applied science is imperative to enabling humanity to venture back to the Moon and then to Mars and beyond. Sending organisms into space is a costly endeavor. All biospecimens not required by spaceflight-relevant Principal Investigators are harvested, preserved, and archived in the NASA Biological Institutional Scientific Collection (NBISC) to maximize the scientific return. The NASA Biological and Physical Sciences (BPS) Division ‘Open Science’ endeavor includes NASA Genelab, the Space Biology Program’s Biospecimen Sharing Program, Physical Sciences Informatics, the Ames Life Sciences Data Archive, and NBISC to integrate extensive data and biospecimen resources from spaceflight and/or ground-based analog experiments. NBISC biospecimens are collected and preserved according to well-established standard operating procedures to maintain scientific quality and are available on-request by the international scientific community. NBISC currently stores over 32,000 biospecimens from Shuttle, International Space Station, and ground-based space analog investigations. Tissue sharing has resulted in at least 33 publications since 2011 and 48 requests since 2016. Many requests for NBISC biospecimen come from first-time investigators who subsequently submit grants as the port-of-entry into the field of space biology. Some NBISC biospecimens have been awarded to NASA Genelab, who then generate various ‘Open Science’ -omics data sets on their platform for bioinformatics. Other NBISC biospecimen awards have led to multiple studies such as fecal microbiome analysis, DNA damage analysis using single-cell DNA sequencing, enzymatic-pathway identification involved in spaceflight muscle atrophy, and characterization of ocular morphological changes. Of note, NBISC has expanded to include a new Space Microbial Culture Collection (SMCC) for the collection, identification, documentation, long-term preservation, and distribution of space-related microbial isolates.

biospecimens↗

NASA NTRSDate not supplied

Enabling Space Biology Knowledge Discovery Through Biospecimen Sharing: The NASA Biological Institutional Scientific Collection

NASA and international partners have conducted experiments in space to understand the biological impacts and address hazards to health. The resulting basic and applied science is imperative to enabling humanity to venture back to the Moon and then to Mars and beyond. Sending organisms into space is a costly endeavor. All biospecimens not required by spaceflight-relevant Principal Investigators are harvested, preserved, and archived in the NASA Biological Institutional Scientific Collection (NBISC) to maximize the scientific return. The NASA Biological and Physical Sciences (BPS) Division has an ‘Open Science’ endeavor which includes NASA Genelab, the Space Biology Program’s Biospecimen Sharing Program, Physical Sciences Informatics, the Ames Life Sciences Data Archive, and NBISC. Its purpose is to integrate extensive data and biospecimen resources from spaceflight and/or ground-based analog experiments. NBISC biospecimens are collected and preserved according to well-established standard operating procedures to maintain scientific quality and are available on-request by the international scientific community. NBISC currently stores over 32,000 biospecimens from Shuttle, International Space Station, and ground-based space analog investigations. Tissue sharing has resulted in at least 33 publications since 2011 and 48 requests since 2016. Many requests for NBISC biospecimen come from first-time investigators who subsequently submit grants as the port-of-entry into the field of space biology. Some NBISC biospecimens have been awarded to NASA Genelab, who then generate various ‘Open Science’ -omics data sets on their platform for bioinformatics. Other NBISC biospecimen awards have led to multiple studies such as fecal microbiome analysis, DNA damage analysis using single-cell DNA sequencing, enzymatic-pathway identification involved in spaceflight muscle atrophy, and characterization of ocular morphological changes. Of note, NBISC has expanded to include a new Space Microbial Culture Collection (SMCC) for the collection, identification, documentation, long-term preservation, and distribution of space-related microbial isolates.

Ryan T. Scott↗

DOE OSTI2022

Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis

Abstract Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

42 ENGINEERING↗

DOE OSTI2023

PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems

The objective of this project is to design, develop, and evaluate scalable software to enhance resilience, data checkpointing, program restart, and analysis. The proposed tasks are to 1) develop scalable machine learning techniques to learn temporal change patterns in a scalable and in-situ manner, and to minimize data movement and maximize learning locally closest to data; 2) design a concise data representation and indexing mechanism to capture the distribution of changes in data that can guarantee point-wise user-defined tolerable errors while reducing the data storage requirements by an order of magnitude or more; 3) develop data reduction techniques as library modules; 4) exploit local SSD for minimizing data movement in storage hierarchy; 5) develop anomaly detection algorithms that can predict corruptions based on learning of emerging patterns; 6) develop software libraries to be incorporated within widely used data formats and APIs; and 7) evaluate the proposed software using DOE scientific applications. The outcomes of the proposed work are to satisfy many synergistic data reduction and resilience requirements for large-scale data intensive applications executed on extreme-scale computing systems. The developed mechanism for error-bound data approximation is directly applicable to existing scientific applications. Through machine learning from historical events and change distribution, this work will enable anomaly detection for DOE computer facility.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2002

MO and DA on the SWIE Instrument on the Wind Spacecraft

The construction of the Faraday Cup portion of the SWIE instrument on the Wind spacecraft, participation in Mission Operations, and Data Analysis (MO and DA) of observations of the solar wind has been supported by a sequence of grants. This 'final' Report represents work done on Mission Operations and Data Analysis for the Faraday Cup portion of the SWE. The work reported here was supported under NASA Grant NAG5-7359 (OSP 6701100) from June 1998 to October 2001. It should be noted that this work is continuing under NASA Grant NAG-10915, and therefore this report is 'final' only in the sense that the Grant has changed its number; a future report will cover the entire period of work. We have two types of obligations under these contracts: (1) To provide and assure the validity of "Key Parameters" which describe the basic properties of the solar wind on a daily basis. We have provided our 92 second observations daily via plots and parameters available from our Web site: http://web.mit.edu/space/www/wind/wind.html (2). To carry out scientific studies based on our observations. To document the extent of our research, we are including below a list of publications and presentations related to this project. The observations from Wind have made a major contribution to the study of the solar wind, and have every indication of continuing to do so.

Lazarus, Alan J.↗

NASA NTRS2014

The International Space Station: A Unique Platform for Remote Sensing of Natural Disasters

Assembly of the International Space Station (ISS) was completed in 2012, and the station is now fully operational as a platform for remote sensing instruments tasked with collecting scientific data about the Earth system. Remote sensing systems are mounted inside the ISS, primarily in the U.S. Destiny Module's Window Observational Research Facility (WORF), or are located on the outside of the ISS on any of several attachment points. While NASA and other space agencies have had remote sensing systems orbiting Earth and collecting publicly available data since the early 1970s, these sensors are carried onboard free-flying, unmanned satellites. These satellites are traditionally placed into Sun-synchronous polar orbits that allow imaging of the entire surface of the Earth to be repeated with approximately the same Sun illumination (typically local solar noon) over specific areas, with set revisit times that allow uniform data to be taken over long time periods and enable straightforward analysis of change over time. In contrast, the ISS has an inclined, Sun-asynchronous orbit (the solar illumination for data collections over any location changes as the orbit precesses) that carries it over locations on the Earth between approximately 52degnorth and 52deg south latitudes (figure 1). The ISS is also unique among NASA orbital platforms in that it has a human crew. The presence of a crew provides options not available to robotic sensors and platforms, such as the ability to collect unscheduled data of an unfolding event using handheld digital cameras as part of the Crew Earth Observations (CEO) facility and on-the-fly assessment of environmental conditions, such as cloud cover, to determine whether conditions are favorable for data collection. The crew can also swap out internal sensor systems installed in the WORF as needed. The ISS orbit covers more than 90 percent of the inhabited surface of the Earth, allowing the ISS to pass over the same ground locations at different times of the day and night. This is important for two reasons: 1) certain surface processes (i.e., development of coastal fog banks) occur at times other than local solar noon, making it difficult to collect relevant data from traditional satellite platforms, and 2) it provides opportunities for the ISS to collect data for short-duration events, such as natural disasters, that polar-orbiting satellites may miss due to their orbital dynamics - in essence, the ISS can be "in the right place at the right time" to collect data. An immediate application of ISS remote sensing data collection is that the data can be used to provide information for humanitarian aid after a natural disaster. This activity contributes directly to the station's Benefits to Humanity mission. The International Charter, Space and Major Disasters (also known as the International Disaster Charter, or IDC) is an agreement between agencies of several countries to provide - on a best-effort basis - remotely sensed data related to natural disasters to requesting countries in support of disaster response. In the United States, the lead agency for interaction with the IDC is the United States Geological Survey (USGS); when an IDC request, or activation, is received, the USGS notifies the science teams for NASA instruments with targeting information for data collection. In the case of the ISS, Earth scientists in the JSC ARES Directorate, in association with the ISS Program Science Office, coordinate targeting and data collection with the USGS. If data is collected, it is passed back to the USGS for posting on its Hazards Data Distribution System and made available for download. The ISS was added to the USGS's list of NASA remote sensing assets that could respond to IDC activations in May 2012. Initially, the NASA ISS sensor systems available to respond to IDC activations included the ISS Agricultural Camera (ISSAC), an internal multispectral visible-near infrared wavelength system mounted in the WORF; CEO, a project that collects imagery through the ISS windows using off-the-shelf handheld digital visible-wavelength cameras; and the Hyperspectral Imager for the Coastal Oceans (HICO), a visible to near-infrared system mounted externally on the Japanese Experiment Module - Exposed Facility. Since May 2012, there have been 37 IDC activations; ISS sensor systems have collected data for 10 of these events.

Stefanov, William L.↗

NASA NTRSDate not supplied

Gateway Program Safety And Mission Assurance Integration - The Future Of Safe Deep Space Human Exploration

This next wave of lunar exploration will be fundamentally different than the past. The goal today is long term human presence in Earth orbit, exploration around the Moon, exploration anywhere on the Moon, and challenging exploration missions beyond the Moon. Sustainability is the key to enabling human expansion across the solar system and bringing back to Earth new knowledge and opportunities, and requires the early engagement of scientific, international, and U.S. commercial interests, innovations, and new approaches. The Gateway’s agile acquisition strategy will shape the entire system life cycle, from design and analysis through production, verification, launch, logistics and operations. This strategy will encourage new ways of doing business to accommodate new techniques, technologies and approaches; improving affordability and maximizing Gateway utility. The full range of acquisition authorities and contracting mechanisms available to NASA will be considered and appropriately tailored in response to the unique demands of each procurement activity. Gateway will provide scientific and research benefits, in addition to providing a sustainable staging location for crewed lunar surface landings and technology demonstrations that help NASA develop the knowledge and technology needed for Mars missions. These payloads are expected to provide operational and scientific benefit for the duration of their stay on Gateway, as the hardware will provide data during the transit to the Gateway and while in lunar orbit as changes in solar activity occur over time. Gateway is designed to support a variety of utilization activities during both crewed and uncrewed periods and is planned for continuous operations throughout its lifetime. These operations include demonstrating key exploration capabilities in a relevant deep space environment, conducting science beyond the influence of Earth, and providing opportunities for Gateway partners and customers for deep space exploration. Located in cislunar orbit, the Gateway capabilities can be further leveraged to enhance lunar and Mars exploration. With the Gateway and other elements of the Artemis Program, NASA is encouraging new ways of doing business to accommodate new techniques, technologies and approaches, consider affordability, and encourage innovation from U.S. industry.

Helen Vaccaro↗

NASA NTRSDate not supplied

Gateway Program Safety and Mission Assurance IAASS Conference Keynote Presentation

Terri Castillo↗

NASA NTRS2018

The Zwicky Transient Facility: System Overview, Performance, and First Results

The Zwicky Transient Facility (ZTF) is a new optical time-domain survey that uses the Palomar 48 inch Schmidt telescope. A custom-built wide-field camera provides a 47 deg ^(2) field of view and 8 s readout time, yielding more than an order of magnitude improvement in survey speed relative to its predecessor survey, the Palomar Transient Factory. We describe the design and implementation of the camera and observing system. The ZTF data system at the Infrared Processing and Analysis Center provides near-real-time reduction to identify moving and varying objects. We outline the analysis pipelines, data products, and associated archive. Finally, we present on-sky performance analysis and first scientific results from commissioning and the early survey. ZTF’s public alert stream will serve as a useful precursor for that of the Large Synoptic Survey Telescope.

Eric C. Bellm↗

DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2000

ISS Microgravity Environment Monitoring System (MEMS): System Design - Part 1

The Principal Investigator Microgravity Services project at the NASA Glenn Research center supports Principal Investigators of the microgravity science community as they evaluate the effects of acceleration on their experiments. The Principal Investigator Microgravity Services' primary responsibility is to support NASA sponsored investigators in the area of acceleration data analysis, interpretation and the monitoring of the microgravity environment on-board various carriers. The microgravity environment is a rich and very complex dynamic one. It is subject to quasi-steady accelerations, higher frequency acceleration, and transient disturbances. With the advent of the International Space Station operation, a significant amount of data is expected to be downlinked and processed for both the space station microgravity environment characterization (verification) and scientific experiments. Therefore, to help principal investigator teams monitor the microgravity environment on-board the International Space Station in order to avoid negative impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services project is currently developing an artificial intelligence monitoring system, which will notify the principal investigator teams in near real time of any change in the microgravity environment susceptible to affect their experiments.

Jules, Kenol↗

NASA NTRS2021

Passive Ground-based Optical Techniques for Monitoring the On-Orbit ICESat-2 Altimeter Geolocation and Footprint Diameter

NASA launched its second Earth observing laser altimeter in 2018 with mission objectives of studying the changes in our climate by monitoring global elevations, particularly in the polar regions. Since the mission is focused on generating accurate elevations and elevation change, the geolocation (or geodetic position) of the measurements are of upmost importance to each of the scientific disciplines supported by these observations. Geolocation validation is required to ensure that the mission is meeting its objectives with the appropriate level of geolocation accuracy. One validation technique uses small optical reflectors placed in a specific pattern along one or more satellite ground-tracks. The optics provide a unique signal back to the satellite that can be used to compare the geolocation of these returns in the data to the known position on the surface. Results of the position comparison indicate the measurement locations are accurate to within 3.5 m with a standard deviation of 1.6 m. They also provide a method for determining a representative footprint diameter using geometric analysis, which resulted in an average value of 10.9 m +- 2.1 m.

ICESat-2↗