Results for “Common Research Model” — Page 18

NASA NTRS2016

NASA Earth Observing System Data and Information System (EOSDIS): A U.S. Network of Data Centers Serving Earth Science Data: A Network Member of ICSU WDS

NASA's Earth Observing System Data and Information System (EOSDIS) has been in operation since August 1994, and serving a diverse user community around the world with Earth science data from satellites, aircraft, field campaigns and research investigations. The ESDIS Project, responsible for EOSDIS is a Network Member of the International Council for Sciences (ICSU) World Data System (WDS). Nine of the 12 Distributed Active Archive Centers (DAACs), which are part of EOSDIS, are Regular Members of the ICSUWDS. This poster presents the EOSDIS mission objectives, key characteristics of the DAACs that make them world class Earth science data centers, successes, challenges and best practices of EOSDIS focusing on the years 2014-2016, and illustrates some highlights of accomplishments of EOSDIS. The highlights include: high customer satisfaction, growing archive and distribution volumes, exponential growth in number of products distributed to users around the world, unified metadata model and common metadata repository, flexibility provided to uses by supporting data transformations to suit their applications, near-real-time capabilities to support various operational and research applications, and full resolution image browse capabilities to help users select data of interest. The poster also illustrates how the ESDIS Project is actively involved in several US and international data system organizations.

Earth Science↗

NASA NTRS2008

Development and Application of an Integrated Approach toward NASA Airspace Systems Research

The National Aeronautics and Space Administration's (NASA) Airspace Systems Program is contributing air traffic management research in support of the 2025 Next Generation Air Transportation System (NextGen). Contributions support research and development needs provided by the interagency Joint Planning and Development Office (JPDO). These needs generally call for integrated technical solutions that improve system-level performance and work effectively across multiple domains and planning time horizons. In response, the Airspace Systems Program is pursuing an integrated research approach and has adapted systems engineering best practices for application in a research environment. Systems engineering methods aim to enable researchers to methodically compare different technical approaches, consider system-level performance, and develop compatible solutions. Systems engineering activities are performed iteratively as the research matures. Products of this approach include a demand and needs analysis, system-level descriptions focusing on NASA research contributions, system assessment and design studies, and common systemlevel metrics, scenarios, and assumptions. Results from the first systems engineering iteration include a preliminary demand and needs analysis; a functional modeling tool; and initial system-level metrics, scenario characteristics, and assumptions. Demand and needs analysis results suggest that several advanced concepts can mitigate demand/capacity imbalances for NextGen, but fall short of enabling three-times current-day capacity at the nation s busiest airports and airspace. Current activities are focusing on standardizing metrics, scenarios, and assumptions, conducting system-level performance assessments of integrated research solutions, and exploring key system design interfaces.

Barhydt, Richard↗

NASA NTRS1988

An experimental comparison of several current viscoplastic constitutive models at elevated temperature

Four current viscoplastic models are compared experimentally for Inconel 718 at 593 C. This material system responds with apparent negative strain rate sensitivity, undergoes cyclic work softening, and is susceptible to low cycle fatigue. A series of tests were performed to create a data base from which to evaluate material constants. A method to evaluate the constants is developed which draws on common assumptions for this type of material, recent advances by other researchers, and iterative techniques. A complex history test, not used in calculating the constants, is then used to compare the predictive capabilities of the models. The combination of exponentially based inelastic strain rate equations and dynamic recovery is shown to model this material system with the greatest success. The method of constant calculation developed was successfully applied to the complex material response encountered. Backstress measuring tests were found to be invaluable and to warrant further development.

James, G. H.↗

NASA NTRS1993

Comparisons of cross-section predictions for relativistic iron and argon beams with semiempirical fragmentation models

Cross-section predictions with semi-empirical nuclear fragmentation models from the Langley Research Center and the Naval Research Laboratory are compared with experimental data for the breakup of relativistic iron and argon projectile nuclei in various targets. Both these models are commonly used to provide fragmentation cross-section inputs into galactic cosmic ray transport codes for shielding and exposure analyses. Overall, the Langley model appears to yield better agreement with the experimental data.

Townsend, Lawrence W.↗

NASA NTRS2018

The SPASE Data Model: A Metadata Standard for Registering, Finding, Accessing, and Using Heliophysics Data Obtained from Observations and Modeling

The Space Physics Archive Search and Extract Consortium has developed and implemented the SPASE Data Model that provides a common language for registering a wide range of Heliophysics data and other products. The Data Model enables discovery and access tools such that any researcher can obtain data easily, thereby facilitating research, including on space weather. The Data Model includes descriptions of Simulation Models and Numerical Output, pioneered by the Integrated Medium for Planetary Exploration (IMPEx) group in Europe, and subsequently adopted by the Community Coordinated Modeling Center (CCMC). The SPASE group intends to register all relevant Heliophysics data resources, including space-, ground-, and model-based. Substantial progress has been made, especially for space-based observational data and associated observatories, instruments, and display data. Legacy product registrations and access go back more than 50 years. Real-time data will be included. The National Aeronautics and Space Administration (NASA) portion of the SPASE group has funding that assures continuity in the upkeep of the Data Model and aids with adding new products. Tools are being developed for making and editing data descriptions. Digital Object Identifiers (DOIs) for Data Products can now be included in the descriptions. The data access that SPASE facilitates is becoming more uniform, and work is progressing on Web Service access via a standard Application Programming Interface. The SPASE Data Model is stable; changes over the past 9 years were additions of terms and capabilities that are backward compatible. This paper provides a summary of the history, structure, use, and future of the SPASE Data Model.

Roberts, D. Aaron↗

NASA NTRSDate not supplied

Spectral Analysis of Integrated Pressures on Patches with Unsteady Pressure-Sensitive Paint Measurements

The technique of Pressure-Sensitive Paint (PSP) is commonly used in the aerospace industry to measure surface pressures on the model of launch vehicles and airplanes in the wind tunnel test. Recent research has demonstrated that Unsteady Pressure-Sensitive Paint (uPSP) can be an essential tool for the assessment of the unsteady, aerodynamic phenomena. The work described in this paper is a part of NASA’s development of a new state-of-the-art uPSP capability in production wind tunnels. This paper describes the spectral analysis of integrated pressures on patches of the scale model of the Space Launch System (SLS) Block 1 cargo vehicle with the uPSP measurements, which were collected in the Ascent Transient Aerodynamics Test (ATAT) with the Unitary Plan Wind Tunnel 11-by-11-foot Transonic Wind Tunnel in September 2019 at NASA Ames Research Center. The patches are defined with x station values, indicating the position along length of the SLS vehicle, and azimuth angles of the scale model. For each patch, the polygons are determined from the surface cells of the grid of the model, clipped with the edges of the patch, and each of the polygons is divided into triangles. The integrated pressure of the patch is determined as the ratio of the sum of the forces on the triangles over the sum of the areas of the triangles. For each run of the test, the Cross Power Spectral Density (CPSD) and magnitude squared coherence are computed from the time series of the integrated pressures on the patches. The pressure integration is coded in C++ and the spectral analysis is coded in MATLAB. The results were generated with the execution of the compiled C++ and MATLAB codes in parallel on the NASA Pleiades supercomputer. The results of pressure integration and spectral analysis are presented in this paper, and the data consistency of the test is also demonstrated. Funding for this research was provided by the NASA Aerosciences Evaluation and Test Capabilities Project.

Pressure-Sensitive Paint↗

NASA NTRS2022

Geophysical Observations Toolkit For Evaluating Coral Health (GOTECH) Fall 2021 Final Report

The NASA Langley Research Center (LaRC) Data Science Team (DST), under the Office of the Chief Information Officer (OCIO), is investigating the capacity of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite to infer the vitality of coral reefs. This report describes the Fall 2021 period of performance for the Geophysical Observations Toolkit for Evaluating Coral Health (GOTECH) project. During this effort, two student teams at Georgia Tech developed machine-learning models to predict the vitality of coral reefs in targeted geographic regions based on backscatter data from the CALIPSO satellite. To train these models, students fused data to form a common operating picture of how coral reefs have grown and decayed worldwide. This report describes the student assignment, background, and results of the semester's research.

Machine Learning↗

NASA NTRS2001

Weather Prediction Improvement Using Advanced Satellite Technology

We discuss in this paper some of the problems that exist today in the fall utilization of satellite data to improve weather forecasts and we propose specific recommendations to solve them. This discussion can be viewed as an aspect of the general debate on how best to organize the transition from research to operational satellites and how to evaluate the impact of a research instrument on numerical weather predictions. A method for providing this transition is offered by the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP). This mission will bridge the time between the present NOAA and Department of Defense (DOD) polar orbiting missions and the initiation of the converged NPOESS series and will evaluate some of the Earth Observing System (EOS) instruments as appropriate for operational missions. Thus, this mission can be viewed as an effort to meet the operational requirements of NOAA and DOD and the research requirements of NASA. More generally, however, it can be said that the process of going from the conception of new, more advanced instruments to their operational implementation and full utilization by the weather forecast communities is not optimal. Instruments developed for research purposes may have insufficient funding to explore their potential operational capabilities. Furthermore, instrument development programs designed for operational satellites typically have insufficient funding for assimilation algorithms needed to transform the satellite observations into data that can be used by sophisticated global weather forecast models. As a result, years often go by before satellite data are efficiently used for operational forecasts. NASA and NOAA each have unique expertise in the design of satellite instruments, their use for basic and applied research and their utilization in weather and climate research. At a time of limited resources, the two agencies must combine their efforts to work toward common goals of full utilization of satellite data. This is a challenge that requires the assimilation of myriad new data into increasingly sophisticated numerical forecast models that run on increasingly sophisticated computer systems. In section II, we briefly outline the impact of satellite data on the quality of the National Centers for Environmental Prediction (NCEP) forecasts. In section III, we describe the present status of the utilization of satellite data in NCEP models and the challenges that lie ahead. In section IV, we propose solutions whose goals are summarized in section V.

Einaudi, Franco↗

NASA NTRS2015

2015 Space Radiation Standing Review Panel

The 2015 Space Radiation Standing Review Panel (from here on referred to as the SRP) met for a site visit in Houston, TX on December 8 - 9, 2015. The SRP met with representatives from the Space Radiation Element and members of the Human Research Program (HRP) to review the updated research plan for the Risk of Radiation Carcinogenesis Cancer Risk. The SRP also reviewed the newly revised Evidence Reports for the Risk of Acute Radiation Syndromes Due to Solar Particle Events (SPEs) (Acute Risk), the Risk of Acute (In-flight) and Late Central Nervous System Effects from Radiation Exposure (CNS Risk), and the Risk of Cardiovascular Disease and Other Degenerative Tissue Effects from Radiation (Degen Risk), as well as a status update on these Risks. The SRP would like to commend Dr. Simonsen, Dr. Huff, Dr. Nelson, and Dr. Patel for their detailed presentations. The Space Radiation Element did a great job presenting a very large volume of material. The SRP considers it to be a strong program that is well-organized, well-coordinated and generates valuable data. The SRP commended the tissue sharing protocols, working groups, systems biology analysis, and standardization of models. In several of the discussed areas the SRP suggested improvements of the research plans in the future. These include the following: It is important that the team has expanded efforts examining immunology and inflammation as important components of the space radiation biological response. This is an overarching and important focus that is likely to apply to all aspects of the program including acute, CVD, CNS, cancer and others. Given that the area of immunology/inflammation is highly complex (and especially so as it relates to radiation), it warrants the expansion of investigators expertise in immunology and inflammation to work with the individual research projects and also the NASA Specialized Center of Research (NSCORs). Historical data on radiation injury to be entered into the Watson “big data” study must be used with caution. The general scientific issues of reproducibility, details of experimental methods and data analysis from preclinical and basic research laboratories have been raised broadly over the last few years (not specific to this work) and indicate that caution must be applied in the ways these data are used. This pertains to preclinical data and also to phase 3 clinical trials in radiation oncology and medical oncology. Of course, appropriate use and analysis of these “big-data” sets also offer the potential of pinpointing limitations and extracting remaining useful information. Emphasis should be placed on the latter possibility. A key target is risk reduction from radiation exposure. Progress of the entire space program, now moving towards the Mars mission, requires timely answers to key components of human risk, which are known to be complex. Periodic review of progress should be conducted with additional resources directed into achieving critical milestones. Turning the long red bars to yellow and green (or for some risks such as CNS possibly to grey) must be high priority. That such progress will require new science and not engineering means that it should be viewed in a knowledge-based light. The technology-based aspects of engineering issues are certainly as important, however, science and knowledge-based problems are solved in a different way than engineering. Timelines for engineering are more predictable, while for science, progress can be methodical with occasional major incremental findings that can rapidly change the rate of progress. As opportunities for rapid incremental changes arise, periodic enhancement of investment is strongly recommended to enable such new knowledge to be quickly and efficiently exploited. Collaborations and linkages with National Institute of Allergy and Infectious Diseases (NIAID), the Biomedical Advanced Research and Development Authority (BARDA) and the Department of Defense (DoD) are in place and more are encouraged, where possible, with the radiation injury and medical countermeasure studies. This could include utilizing some of their animal model testing contracts to facilitate obtaining results using common platforms. Such approach will facilitate the comparison of results among laboratories, and will facilitate and accelerate the development of medical countermeasures. It is particularly noteworthy that the NASA Space Radiation Element is reaching out to the Multidisciplinary European Low Dose Initiative (MELODI) platform coordinating low dose radiation risk research, and to other international agencies that are studying low dose radiation effects in an effort to fill the void generated by the cancelation of the Department of Energy (DOE) low dose radiation program. While NASA is working actively with NIAID and BARDA to integrate their relevant findings of radiation mitigator investigations to NASA programs, the committee notes its disappointment that the United States currently lacks a dedicated low dose radiation program with clear mechanistic orientation and aimed at the quantification and mitigation of human radiation risk on Earth. This void gives to the NASA Space Radiation Program Element special societal value, but also makes its overall design more challenging.

Steinberg, Susan↗

NASA NTRS2022

MLtool: Universal Supervised Machine Learning Tool to Model Tabulated Data

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine learning↗

NASA NTRS2022

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗

NASA NTRS2023

An MBSE Approach for Developing an Autonomous Rover Platform

The proliferation of increasingly autonomous systems calls for new ways to address how safety is assured. As these systems become more advanced and complex, it becomes more important to model and prototype autonomous functions at the systems level and the functions that assure they are operating safely and as expected. To that effect, researchers at the National Aeronautics and Space Administration (NASA) 's Robust Software Engineering (RSE) group are working on prototyping a Research Autonomous Vehicle, commonly referred to as R-RAV. The R-RAV is an autonomous rover platform designed to act as a case study for assured autonomy research. Moreover, an overarching goal is for the R-RAV to serve as a training ground for other mission projects. In this paper, we will detail how we have used a Model-Based Systems Engineering (MBSE) approach to model a prototype of the R-RAV and test and verify its different functionalities.

MBSE↗

NASA NTRS2017

Guide for Pressure-Sensitive Paint Testing at NASA Ames Research Center Unitary Plan Wind Tunnel

Optical measurement techniques have become a standard option for wind tunnel tests. Pressure-sensitive paint (PSP) is a mature test technique and a common experimental technique in many wind tunnels to measure the global mean static pressure on a model. PSP is a valuable tool when a more detailed distribution of the pressure is needed rather than the conventional pressure taps alone. Planning for a test with optical-based techniques can present new challenges even for experienced customer. The purpose of this paper is to provide a resource to the wind tunnel testing community and customers interested in obtaining PSP measurements on a wind tunnel model at the NASA Ames Research Center’s Unitary Plan Wind Tunnel. An overview of PSP mechanics, a list of requirements for ones considering PSP measurements, and PSP deliverable details are specified.

Unitary↗

NASA NTRSDate not supplied

NASA Aeronautics Research Mission Directorate System Security Engineering Approaches

System security engineering (SSE) is a set of formal engineering methods and is considered a subset of systems engineering. It is a relatively new development in systems engineering with the initial NIST (National Institute of Standards) standard published in November of 2016 with updates in 2018, and 2022. The guiding principles in our methodology are based in NIST Special Publication 800-160 Vol. 1 “Systems Security Engineering: Considerations For A Multidisciplinary Approach In The Engineering Of Trustworthy Secure Systems” and integrate methodologies from common IT (Information Technology) threat modeling approaches utilizing MBSE (Model-Based Systems Engineering). The presentation will discuss how our teams utilize SSE and MBSE (Model-Based Systems Engineering) to develop secure architectures for systems under development in our NASA aeronautics research environment. This includes the activities to develop Protection Needs (PN) that, in turn result in security requirements in the design context and policies for the future state operational context for system protection. The process of applying SSE to analyze project architectures and ConOps (Concept of Operations) is intended to ensure the transferred research is both secure and securable in a “real-world” setting.

Systems Security Engineering↗

NASA NTRS2014

The Parallel System for Integrating Impact Models and Sectors (pSIMS)

We present a framework for massively parallel climate impact simulations: the parallel System for Integrating Impact Models and Sectors (pSIMS). This framework comprises a) tools for ingesting and converting large amounts of data to a versatile datatype based on a common geospatial grid; b) tools for translating this datatype into custom formats for site-based models; c) a scalable parallel framework for performing large ensemble simulations, using any one of a number of different impacts models, on clusters, supercomputers, distributed grids, or clouds; d) tools and data standards for reformatting outputs to common datatypes for analysis and visualization; and e) methodologies for aggregating these datatypes to arbitrary spatial scales such as administrative and environmental demarcations. By automating many time-consuming and error-prone aspects of large-scale climate impacts studies, pSIMS accelerates computational research, encourages model intercomparison, and enhances reproducibility of simulation results. We present the pSIMS design and use example assessments to demonstrate its multi-model, multi-scale, and multi-sector versatility.

crop modeling↗

NASA NTRS2009

Forecasting Lightning Threat using Cloud-resolving Model Simulations

As numerical forecasts capable of resolving individual convective clouds become more common, it is of interest to see if quantitative forecasts of lightning flash rate density are possible, based on fields computed by the numerical model. Previous observational research has shown robust relationships between observed lightning flash rates and inferred updraft and large precipitation ice fields in the mixed phase regions of storms, and that these relationships might allow simulated fields to serve as proxies for lightning flash rate density. It is shown in this paper that two simple proxy fields do indeed provide reasonable and cost-effective bases for creating time-evolving maps of predicted lightning flash rate density, judging from a series of diverse simulation case study events in North Alabama for which Lightning Mapping Array data provide ground truth. One method is based on the product of upward velocity and the mixing ratio of precipitating ice hydrometeors, modeled as graupel only, in the mixed phase region of storms at the -15\dgc\ level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domainwide statistics of the peak values of simulated flash rate proxy fields against domainwide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. A blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Weather Research and Forecast Model simulations of selected North Alabama cases show that this model can distinguish the general character and intensity of most convective events, and that the proposed methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because models tend to have more difficulty in correctly predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models, the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of cloud-allowing forecasts become available.

McCaul, E. W., Jr.↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗

NASA NTRSDate not supplied

SAGE III/ISS v5.3 Level 2 Data Product Changes and Improvements

The Stratospheric Aerosol and Gas Experiment on the International Space Station (SAGE III/ISS) is an occultation instrument that acquires measurements of aerosols and gases within the Earth’s stratosphere and upper troposphere. SAGE III/ISS provides level 2 solar species products for aerosol extinction (9 channels), nitrogen dioxide (NO2), ozone (O3), and water vapor (H2O). The level 2 products currently provide three O3 profiles based on differing retrievals. The first O3 profile is based on measurements at short wavelengths within the Hartley-Huggins band (MesO3), the second O3 profile is based on measurements made at visible wavelengths within the Chappius band (MLR O3), and the final profile is found using a more SAGE II like approach (AO3). The SAGE III/ISS also provides level 2 lunar species products for ozone (O3), nitrogen dioxide (NO2), and nitrogen trioxide (NO3). Version 5.3 of the SAGE III/ISS retrieval algorithm introduces improvements that affect the level 2 data products. The largest change to the solar algorithm is the implementation of disturbance monitoring package (DMP) corrections to improve pointing accuracy. The DMP is comprised of a miniature inertial measurement unit that measures rotation in inertial space using ring laser gyroscopes oriented about three orthogonal axes which can be used to correct pointing errors caused by mechanical disturbances. A major change common to solar and lunar algorithms is meteorological input from the coarser 42 level Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) model data to the 72 level MERRA-2 model data. The final major change involves improving the automated quality assurance (QA) algorithm to recover events that were withheld from the public release because differences between the AO3 and MLR O3 for some events after the eruption of Tonga–Hunga Haʻapai. Other changes for v5.3 include minor bug fixes as well as restoration of some data quality flags that were removed in v5.21. This presentation presents the impacts of these changes as well as overall observations of interest from v5.3 level 2 data products.

Carter Hulsey↗