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Preserving NASA Historic and Current Mission Data and Adding Value to These for Future Researchers

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has been actively involved in many aspects of ensuring the long-term preservation of NASA earth science data and knowledge. This involves both the recovery and preservation of early NASA meteorological and other earth observation data, as well as preserving the more recent Earth Observation System (EOS) mission data sets which continue or have reached their end of lifetime. The GES DISC adds value to these preserved data by adding metadata and making the data available online to future researchers. The early NASA meteorological and earth observation data sets from the 1960s and 70s were originally archived on magnetic tapes, and visualizations of these data were preserved on 70-mm film. As these media have aged, their contents have been at risk of permanent loss. NASA has given the task of preserving these early data sets to the GES DISC and making these data sets easily available to the public. The data from these early missions are potentially useful to climate researchers as these are some of the only global measurements made at their time. These old data on magnetic tapes and film strips do not contain easily readable metadata, and so to add value the GES DISC has added digital metadata to them so that the data are searchable and findable. The GES DISC is also involved in preserving the data and knowledge from the EOS era missions. The GES DISC follows the guidelines developed for the preservation of data as specified in the NASA EOS Data and Information System (EOSDIS) Earth Science Data Preservation Content Specification (423-SPEC-001) document. To date, the GES DISC has consulted with the data science teams from the following missions: UARS, Earth Probe TOMS, Aura HIRDLS, and SORCE, in order to properly preserve their data and accompanying documentation. The GES DISC is also currently working with the EOS science teams from TRMM, AIRS, MLS, OMI and additional missions to ensure that the relevant documents and data sets are properly archived for future researchers. A standardized procedure for mission data preservation following 423-SPEC-001 makes preservation among the many NASA EOSDIS data centers uniform, so that these could be transitioned easily to a common EOSDIS preservation repository. This presentation will give an overview of the preservation and recovery of the old NASA historical data sets archived at the GES DISC, as well as the data and documentation preservation efforts of the EOS era missions.

James Johnson↗

On the embedding-dimension analysis of AE and AL time series

Several authors have employed the embedding-dimension method to analyze time series of geomagnetic indices, with differing results for the value of the correlation dimension nu. It is argued that these differences may arise from corresponding differences in the length and construction of the various data sets used. Practical application of the method to sets of discretized data requires use of a delay time scale set by the autocorrelation time of the data set. It is found that a particular data set containing 35 days of AE exhibits an autocorrelation time tau(c) longer by an order of magnitude than that of a short-duration (less than 5 days) set, raising the possibility that extant analyses of long-duration sets may have employed delay times shorter than tau(c). In addition, the power spectrum of AE reveals modulation at a period of 24 hr. A numerical experiment on the logistic map shows that such modulation introduces an extra degree of freedom in the data, resulting in an augmented correlation dimension.

Shan, Lin-Hua↗

BENEFIT with Northeastern University: HVAC Hardware-in-the-Loop Experimental Testing of a Heat Pump and Air Conditioner

This dataset includes HVAC Hardware-in-the-Loop (HIL) experimental results for a single stage, SEER 16, HSPF 9.5, 3-ton single-speed air source heat pump with 15 kW of backup auxiliary heating tested in both cooling and heating mode, and a two stage, SEER 21, 2-ton central air conditioner tested in cooling mode for a set of outdoor temperatures and indoor setpoint temperatures. In addition to these tests, experimental tests focused on the operation of auxiliary heating for the heat pump for winter condition were also conducted. The laboratory experiments for transient testing of the heat pump and air conditioner were conducted using the two HIL systems in the Systems Performance Laboratory (SPL) at NREL’s Energy Systems Integration Facility (ESIF). Further information on laboratory design and capabilities of the SPL along with the architecture of HVAC HIL system can be found in: Sparn, B. F. 2018. Laboratory Resources and Techniques to Evaluate Smart Home Technology (No. NREL/CP-5500-71696). National Renewable Energy Laboratory (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy18osti/71696.pdf and the experimental setup and validation of HVAC HIL platform can be found in: Ramaraj, S. and Sparn, B. 2022. Validation of HVAC Hardware-In-the-Loop Simulation for Advanced Control Strategies in Smart Homes (No. NREL/CP-5500-82562). National Renewable Energy Lab (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy22osti/82562.pdf. These experimental results can be used to validate how we currently model the cycling behavior of heat pumps and air conditioners. Additionally, many demand response programs implement heat pump and air conditioner control by changing the thermostat set point – these data may also be used to verify our models for heat pump and air conditioner demand response control are implemented correctly. The Test_Matrix file describes all the indoor and outdoor test conditions for heat pump and air conditioner and the file names of data sets include information about the test conditions. A wide range of outdoor air temperatures were chosen to accommodate summer and winter conditions. In addition to operating the HVAC equipment with different outdoor temperatures, we also operate the system with different indoor temperature set points to represent different grid signals or different operating conditions. For cooling conditions, the baseline set point is 72°F. To represent Load Up signals, the setpoint is changed to 68°F. The Load Shed set point is 76°F. For heating conditions, the baseline set point was assumed to be 68°F. The Load add set point is 72°F and the Load shed set point is 64°F. The starting indoor temperature for cooling conditions was set ~2°F above the indoor setpoint temperature so that the equipment turned on quickly. Similarly, the initial indoor temperature was set ~2°F lower than setpoint for heating mode tests to ensure that heating began quickly. The return air temperature was assumed to be equal to the indoor setpoint temperature in all cases. The experimental data are sampled at 1-second intervals. The data from ecobee thermostat at 5-minute interval are resampled and added to the corresponding file. The content of each data set is as follows: • T_Return (C): Measured return air temperature [C] • T_Return_SP (C): Return air temperature setpoint from E+ model, sent to HIL [C] • T_Supply (C): Measured supply air temperature at evaporator outlet [C] • T_Outdoor (C): Measured outdoor air temperature [C] • T_Outdoor_SP (C): Outdoor air temperature setpoint from weather file, sent to HIL [C] • T_Indoor (C): Measured indoor air temperature [C] • T_Indoor_SP (C): Indoor air temperature setpoint from E+ model, sent to HIL [C] • Outdoor Unit Power (W): Measured power of the outdoor unit [W] • Indoor Unit Power (W): Measured power of the indoor unit [W] • Evaporator Airflow Rate (CFM): Measured evaporator or indoor unit airflow rate sent to E+ model [CFM] • Cooling/Heating Capacity (kW): Calculated cooling/heating capacity sent to E+ model [kW] • T_SP_Thermostat (C): Thermostat cooling/heating setpoint temperature [C] • T_Indoor_Thermostat (C): Thermostat indoor air temperature [C]

24 POWER TRANSMISSION AND DISTRIBUTION↗

WetNet operations

WetNet is an interdisciplinary Earth science data analysis and research project with an emphasis on the study of the global hydrological cycle. The project goals are to facilitate scientific discussion, collaboration, and interaction among a selected group of investigators by providing data access and data analysis software on a personal computer. The WetNet system fulfills some of the functionality of a prototype Product Generation System (PGS), Data Archive and Distribution System (DADS), and Information Management System for the Distributed Active Archive Center. The PGS functionality is satisfied in WetNet by processing the Special Sensor Microwave/Imager (SSM/I) data into a standard format (McIDAS) data sets and generating geophysical parameter Level II browse data sets. The DADS functionality is fulfilled when the data sets are archived on magneto optical cartridges and distributed to the WetNet investigators. The WetNet data sets on the magneto optical cartridges contain the complete WetNet processing, catalogue, and menu software in addition to SSM/I orbit data for the respective two week time period.

Goodman, H. Michael↗

Seasonal variation of the vertical distribution of stratospheric ozone as observed with the Umkehr and BUV methods

Month-to-month variations evidenced by ozone profiles inferred from the classical Umkehr observations and from the back-scattered ultraviolet (BUV) satellite observations made from the Nimbus 4 satellite are examined. Upper stratospheric ozone profiles derived from BUV and Umkehr data display similar seasonal variations of about the same phase and magnitude for the 38 km to 50 km region. Between 28 km and 38 km, the seasonal variations are less marked, but the same rough picture emerges for both data sets. If both data sets indicate an increasing (or decreasing) trend over a period of years, it is not possible to conclude that a trend exists unless separate means exists for monitoring stratospheric dust. Because of the stratosphere well above the Junge layer seems less likely to be affected by volcanic debris, BUV data should be superior to Umkehr data for monitoring trends in the 38 km to 50 km range, provided that the calibration problems of flying such a monitoring instrument in space can be overcome.

Mateer, C. L.↗

Multiple Satellite Observations of Cloud Cover in Extratropical Cyclones

Using cloud observations from NASA Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and CloudSat-CALIPSO, composites of cloud fraction in southern and northern hemisphere extratropical cyclones are obtained for cold and warm seasons between 2006 and 2010, to assess differences between these three data sets, and between summer and winter cyclones. In both hemispheres and seasons, over the open ocean, the cyclone-centered cloud fraction composites agree within 5% across the three data sets, but behind the cold fronts, or over sea ice and land, the differences are much larger. To supplement the data set comparison and learn more about the cyclones, we also examine the differences in cloud fraction between cold and warm season for each data set. The difference in cloud fraction between cold and warm season southern hemisphere cyclones is small for all three data sets, but of the same order of magnitude as the differences between the data sets. The cold-warm season contrast in northern hemisphere cyclone cloud fractions is similar for all three data sets: in the warm sector, the cold season cloud fractions are lower close to the low, but larger on the equator edge than their warm season counterparts. This seasonal contrast in cloud fraction within the cyclones warm sector seems to be related to the seasonal differences in moisture flux within the cyclones. Our analysis suggests that the three different data sets can all be used confidently when studying the warm sector and warm frontal zone of extratropical cyclones but caution should be exerted when studying clouds in the cold sector.

imaging spectrometers↗

Visualizing 2D Probability Distributions from Satellite Image-Derived Data

Creating maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data is an important component of Earth science. These 2D maps have a single value at every location and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each location. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. 'Distribution data sets' are described, then a case study is presented to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Our case study data consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present several approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets.

Kao, David↗

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗

Interactive Computer Graphics

Aerospace data analysis tools that significantly reduce the time and effort needed to analyze large-scale computational fluid dynamics simulations have emerged this year. The current approach for most postprocessing and visualization work is to explore the 3D flow simulations with one of a dozen or so interactive tools. While effective for analyzing small data sets, this approach becomes extremely time consuming when working with data sets larger than one gigabyte. An active area of research this year has been the development of data mining tools that automatically search through gigabyte data sets and extract the salient features with little or no human intervention. With these so-called feature extraction tools, engineers are spared the tedious task of manually exploring huge amounts of data to find the important flow phenomena. The software tools identify features such as vortex cores, shocks, separation and attachment lines, recirculation bubbles, and boundary layers. Some of these features can be extracted in a few seconds; others take minutes to hours on extremely large data sets. The analysis can be performed off-line in a batch process, either during or following the supercomputer simulations. These computations have to be performed only once, because the feature extraction programs search the entire data set and find every occurrence of the phenomena being sought. Because the important questions about the data are being answered automatically, interactivity is less critical than it is with traditional approaches.

Kenwright, David↗

Measuring Neutron Polarisation in Deuteron Photo-disintegration with the CLAS Start Counter [Thesis]

Deuteron photo-disintegration (γd → γp) is a reaction that represents the simplest case in which nuclear and hadron physics models can be tested. Despite this, associated polarization analyses are limited in terms of angular coverage and energy ranges, especially in observables related to the recoil neutron. This is largely due to a lack in dedicated polarimetry equipment, and represents a roadblock in global progress to understand high-energy phenomena such as hexaquarks, and quark-gluon degrees of freedom. To address this problem, this PhD thesis pioneers a new methodology for the parasitic measurement of nucleon polarization using kinematic reconstruction of (spin-dependent) nucleon-nucleus scattering of reaction products, prior to their detection in large acceptance particle detector apparatus. Following this novel approach, which requires no dedicated polarimeter, a determination of the double polarization observable, $C^n_{x'}$, from deuteron photo-disintegration is presented, using Jefferson Lab’s CLAS detector. The analysis utilizes the (n,p) charge exchange reaction in CLAS’s "start counter" (plastic scintillator) to determine the final state neutron polarizations. The results present the first ever data for this observable above 0.7 GeV (photon beam energy) and significantly extend the angular range of the world data set. This new data is largely statistically consistent with the previous measurement of $C^n_{x'}$ by Bashkanov et al . in the overlapping energy range of 0.4-0.7 GeV. It is planned for the statistical accuracy of the presented result to be increased by the inclusion of additional data. The analysis herein serves as a key proof of concept for future applications, including a recommended similar analysis to be implemented with data from the more modern CLAS12 detector. This paves the way for a plethora of additional analyses using existing data sets that would provide crucial new constraints for hadron and nuclear physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Viking orbiter stereo imaging catalog

The extremely long missions of the two Viking Orbiter spacecraft produced a wealth of photos of surface features. Many of which can be used to form stereo images allowing the earth-bound student of Mars to examine the subject in 3-D. This catalog is a technical guide to the use of stereo coverage within the complex Viking imaging data set. Since that data set is still growing (January, 1980, about 3 1/2 years after the mission began), a second edition of this catalog is planned with completion expected about November, 1980.

Blasius, K. R.↗

TPSAS-NF1676L-13635-DND

Objectives of AePW: Assess state-of-the-art Computational Aeroelasticity (CAe) methods as practical tools for the prediction of static and dynamic aeroelastic phenomena and responses on relevant geometries. - Perform comparative computational studies on selected test cases - Identify errors & uncertainties in computational aeroelastic methods - Identify gaps in existing aeroelastic databases - Provide roadmap of path forward (additional existing data sets, new experimental data sets, and analytical methods developments).

Walt Silva↗

Towards an Improved Understanding of the Antarctic Coastal Zone and Its Contribution to Future Global Sea Level

Understanding the coastal zone of the Antarctic Ice Sheet (AIS), where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate and sea level. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the AIS, the Southern Ocean and their global connections in the coming centuries. The AIS remains the largest source of uncertainty in future sea-level projections. Bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, and is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground- and ship-based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography data sets identifies significant data gaps and their regional distribution, framed in the context of current ice-sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next-generation data set of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice-sheet dynamics, reducing uncertainties in sea-level rise projections and improving predictions of future ocean and climate changes.

Kenichi Matsuoka↗

Merged and Gridded GPM and Atmospheric River Data Product

The Global Precipitation Measurement (GPM) Mission Core Observatory satellite launched in 2014 as a joint mission between National Aeronautics and Space Administration (NASA) and JAXA. Global Precipitation Measurement (GPM) has, since that time, provided continuous, valuable dual-frequency radar and passive microwave radiometer observations. Here, we introduce a gridded data set of collocated GPM Core Observatory observational products merged with a reanalysis-derived Atmospheric river (AR) data set in the North Atlantic and North Pacific sectors. The three data sets that are merged and gridded are: (a) the NASA Goddard Profiling (GPROF) precipitation product, which uses GPM passive microwave radiometer observations to derive surface precipitation rates, (b) a water vapor data product derived from the GPM Core Observatory radiometer, provided by Remote Sensing Systems (RSS), and (c) the Mattingly et al. (2018, https://doi.org/10.1029/2018jd028714) AR data set that is specifically tuned to the high-latitude regions. This novel merged data set spans from May 2014 to December 2022 with plans to update annually through 2026 at minimum. This gridded product combines RSS passive water vapor and precipitation estimates with coincident AR detection. This data product benefits the scientific community by providing (a) user-friendly gridded satellite data compared to standard satellite data sets, while maintaining high temporal resolution, and (b) coincident satellite observations to assess the link between ARs and precipitation.

Marian E Mateling↗

CanSIS Regional Soils Data in Vector Format

This data set is the original vector data set received from Canada Soil Information System (CanSIS). The data include the provinces of Saskatchewan and Manitoba. Attribute tables provide the various soil data for the polygons; there is one attribute table for Saskatchewan and one for Manitoba. The data are stored in ARC/INFO export format files. Based on agreements made with Agriculture Canada, these data are available only to individuals and groups that have an official relationship with the BOREAS project. These data are not included on the BOReal Ecosystem-Atmosphere Study (BOREAS) CD-ROM set. A raster version of this data set titled 'BOREAS Regional Soils Data in Raster Format and AEAC Projection' is publicly available and is included on the BOREAS CD-ROM set.

Monette, Bryan↗

Monitoring Global Precipitation Using Satellite Observations: Status and Future

The current status of monitoring global precipitation amounts and patterns is described using data sets from the Global Precipitation Climatology Project (GPCP) of the World Climate Research Program (WCRP) and from recent research satellites, especially the Tropical Rainfall Measuring Mission (TRMM). The GPCP monthly (and pentad) data set is a 23-year, globally complete precipitation analysis that is used to explore global and regional variations and trends. The data set is a blend of data mainly from low-orbit microwave satellites and geosynchronous infrared satellites, with additional input from satellite sounder data, Outgoing Longwave Radiation (OLR) data and raingauges. The monthly GPCP data set shows no significant global trend in precipitation over the twenty years, unlike the positive trend in global surface temperatures over the past century. Regional trends are also analyzed. A trend pattern that is a combination of both El Nino and La Nina precipitation features is evident in the 23-year data set. This pattern is related to an increase with time in the number of combined months of El Nino and La Nina during the 23-year period. This apparent trend may be a short-term variation, but also might be related to the increase with time of extreme precipitation events reported elsewhere. Patterns of precipitation variation related to ENSO and other phenomena are shown with clear signals extending from the Tropics into middle and high latitudes of both hemispheres. Also shown, as an example of higher time resolution data is the GPCP daily analysis, which is available for the last six years. A second focus of the talk is on TRMM precipitation data and how these newer data sets incorporating information from the first space-borne meteorological radar compare with the established GPCP data sets.

Adler, Robert F.↗

Alternate physical formats for storing data in HDF

Since its inception HDF has evolved to meet new demands by the scientific community to support new kinds of data and data structures, larger data sets, and larger numbers of data sets. The first generation of HDF supported simple objects and simple storage schemes. These objects were used to build more complex objects such as raster images and scientific data sets. The second generation of HDF provided alternate methods of storing data elements, making it possible to do such things as store extendible objects with in HDF, to store data externally from HDF files, and support data compression effectively. As we look to the next generation of HDF, we are considering fundamental changes to HDF, including a redefinition of the basic HDF object from a simple object to a more general, higher-level scientific data object that has certain characteristics, such as dimensionality, a more general atomic number type, and attributes. These changes suggest corresponding changes to the HDF file format itself.

Folk, Mike↗

Satellite Imagery of PV Site Storm Damage

"This repository contains multiple data sets focused on visible damage to photovoltaic (PV) installations following extreme weather events such as hailstorms and hurricanes. Data sets are split into two categories: the first category, the ‘manually labeled’ data, was compiled by researchers manually, and contains manually identified PV sites exposed to storms. The second data set, the ‘aggregated’ data, is a compilation of the manually labeled PV sites and deep learning-identified PV sites. The hail damage data set focuses on post-storm PV damage following a September 24, 2023 hailstorm in Austin, TX, which caused over $600 million in damages in the Austin metro area. The hurricane damage data set focuses on post-storm PV damage following Hurricanes Irma and Maria in Puerto Rico and the US Virgin Islands. Hurricanes Irma and Maria were back-to-back category 5 hurricanes, which pummeled the Caribbean and southeastern United States in September 2017, causing an estimated $115.2 billion in damages."

14 SOLAR ENERGY↗