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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

PYSAT: Python Satellite Data Analysis Toolkit

A common problem in space science data analysis is combining complementary data sources that are provided and analyzed in different formats and programming languages. The Python Satellite Data Analysis Toolkit (pysat) addresses this issue by providing an open source toolkit that implements the general process of space science data analysis, from beginning to end, in an instrumentindependent manner. This toolkit uses an Instrument object that enables systematic analysis of science data from a variety of platforms within a single interface. Basic functions such as downloading, loading, and cleaning are included for all supported instruments. Common analysis routines are also included, which are instrument and data source independent. A nanokernel is used to provide instrument independence, it is attached to the Instrument object and mediates the systematic and arbitrary modification of loaded data. Pysat uses the nanokernel to improve the rigor of time series analysis, support onthefly orbit determination, and cleanly span file breaks. Pysat's functions and higherlevel scientific analysis features are validated through the use of unit testing. Further adoption by the community provides a set of scientific results produced by a common core, constituting a distributed heritage that supports the validity of the underlying processing and scientific output. These features are used to demonstrate consistency between derived electron density profiles and measured ion drifts, particularly downward ion drifts in the afternoon hours during extreme solar minimum. Pysat builds upon open source Python software that is freely available and encourages communitydriven development.

Stoneback, R.A.↗
NASA NTRSDate not supplied

Flight Trajectory Prediction Based on Hybrid-Recurrent Networks

The development of future technologies for the National Airspace System (NAS) will be reliant on a new communications infrastructure capable of managing the limited available spectrum for communications among aircraft and ground systems. Emerging approaches to autonomous allocation of aviation spectrum mostlyrely on machine learning techniques, where 4D (longitude, latitude, altitude, time) trajectory prediction is an important data input to enable real-time resource allocation. This study explores and evaluates effective data sources and deep recurrent neural network techniques when determining flight trajectories. Specifically, data are collected and evaluated in a 100-day and 14-day period. Sources of data include NASA Sherlock Data Warehouse, MIT Lincoln Labs Corridor Integrated Weather Service (CIWS), and assorted NOAA weather datasets. Deep learning models for 4D predictions all utilize a hybrid-recurrent technique. A baseline model is considered via the convolutional-LSTM design from the existing literature. The modified design considers Gated Recurrent Units (GRU), Independently Recurrent Neural Networks (IndRNN), and stand-alone self-attention layers. Results indicatethe effectiveness of LSTM and GRUcells for state-of-the-art data processing (interpolation). Additionally, GRUs may be quickly trained with limited data, allowing for exacting improvements with optimizer selection. Attention mechanisms provide notable performance improvements to convolutional layers and may extend dimensional capabilities of a learning model. Finally, NOAA measurements provide only a supplemental value, requiring support from tailored measurements for Air Traffic Management.

Nathan Schimpf↗
NASA NTRS1980

Efficient telemetry format

Format would simplify ground processing of telemetry data. Also, missing minor frame would create error in only one set of source data instead of disrupting all sets. Format organizes data from various sources into autonomous blocks. Data are pre-processed, in effect, so main computer only needs to determine block type and process data set as batch.

Greenberg, E.↗
NASA NTRSDate not supplied

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗
NASA NTRS1974

Thermal radiation analysis system (TRASYS)

The Thermal Radiation Analysis System, TRASYS, is a digital computer software system with generalized capability to solve the radiation-related aspects of thermal analysis problems. When used in conjunction with a generalized thermal analyzer program any thermal problem that can be expressed in terms of a lumped parameter R-C thermal network can be solved. The function of TRASYS is twofold. It provides: (1) internode radiation interchange data; and (2) incident and absorbed heat rate data from environmental radiant heat sources. Data of both types is provided in a format directly usable by the thermal analyzer programs. One of the primary features of TRASYS is that it allows the user to write his own executive or driver program which organizes and directs the program library routines toward solution of each specific problem in the most expeditious manner. The user also may write his own output routines, thus the system data output can directly interface with any thermal analyzer using the R-C network concept.

Jensen, C. L.↗
NASA NTRS1976

Stratospheric long waves - Comparison of thermal structure in the Northern and Southern Hemispheres

Stratospheric radiance and temperature data obtained in the Northern and Southern Hemispheres during 1971 and 1972 are analyzed in order to compare planetary-wave behavior in both hemispheres. The data sources include the reduced radiance data of the Nimbus 4 selective chopper radiometer, National Meteorological Center gridded data, and Eole constant-level balloon data. Large-amplitude wave oscillations with energy in the period range from 20 to 30 days are observed along with the eastward propagation of planetary wave 2 in the Southern Hemisphere and a strong vertical coherence in wave phase at all levels between 200 mb and the upper stratosphere. The results show that: (1) despite stationary forcing, eastward phase progression occurs for wave 2 in the south as well as during the amplification phase of wave 1 in both hemispheres; (2) the upper stratosphere in midwinter contains regions of apparent barotropic instability, especially in the south; (3) wave number 2 amplitudes grow from very low values in early winter to high values in late winter; (4) the meridional amplitude structure is similar in the upper stratosphere in both hemispheres; and (5) the equatorward extent of waves is directly related to zonal wind speed in the upper stratosphere.

Leovy, C. B.↗
NASA NTRS1977

Thermal radiation analysis system (TRASYS 2), programmer's manual

TRASYS, the Thermal Radiation Analysis System, is a digital computer software system with a generalized capability to solve the radiation-related aspects of thermal analysis problems. When used in conjunction with a generalized thermal analysis program such as the Systems Improved Numerical Differencing Analyzer (SINDA) program, any thermal problem that can be expressed in terms of a lumped parameter, radiation conductor thermal network can be solved. The function of TRASYS is twofold. It provides: 1) Internode radiation interchange data; 2) Incident and absorbed heat rate data from environmental radiant heat sources. Data of both types are provided in a format directly usable by the thermal analyzer programs.

Connor, R. J.↗
NASA NTRS1981

User's Manual: Thermal Radiation Analysis System TRASYS 2

A digital computer software system with generalized capability to solve the radiation related aspects of thermal analysis problems is presented. When used in conjunction with a generalized thermal analysis program such as the systems improved numerical differencing analyzer program, any thermal problem that can be expressed in terms of a lumped parameter R-C thermal network can be solved. The function of TRASYS is twofold. It provides: (a) Internode radiation interchange data; and (b) Incident and absorbed heat rate data from environmental radiant heat sources. Data of both types is provided in a format directly usable by the thermal analyzer programs. The system allows the user to write his own executive or driver program which organizes and directs the program library routines toward solution of each specific problem in the most expeditious manner. The user also may write his own output routines, thus the system data output can directly interface with any thermal analyzer using the R-C network concept.

Jensen, C. L.↗
NASA NTRS1983

Environmental data display

Methods for using a combination of computer-generated color graphics and image processing techniques to display a large data base of environment information are described. The data source can be either field data or mathematical models, reduced to summary statistics that characterize the data field as a whole. Sharp gradients are plotted into contour plots, which can also feature shades, degree of brightness, and saturation levels for fine-tuning the image. The basic concepts of digital image processing are reviewed, including location of the pixels, intensity mapping operations, pseudocolor enhancements, neighborhood averaging, and smoothing. Sample applications are presented in terms of emissions and air quality distributions over the south coast air basin of southern California.

Hussey, K. J.↗
NASA NTRS1991

Errors of five-day mean surface wind and temperature conditions due to inadequate sampling

Surface meteorological reports of wind components, wind speed, air temperature, and sea-surface temperature from buoys located in equatorial and midlatitude regions are used in a simulation of random sampling to determine errors of the calculated means due to inadequate sampling. Subsampling the data with several different sample sizes leads to estimates of the accuracy of the subsampled means. The number N of random observations needed to compute mean winds with chosen accuracies of 0.5 (N sub 0.5) and 1.0 (N sub 1,0) m/s and mean air and sea surface temperatures with chosen accuracies of 0.1 (N sub 0.1) and 0.2 (N sub 0.2) C were calculated for each 5-day and 30-day period in the buoy datasets. Mean values of N for the various accuracies and datasets are given. A second-order polynomial relation is established between N and the variability of the data record. This relationship demonstrates that for the same accuracy, N increases as the variability of the data record increases. The relationship is also independent of the data source. Volunteer-observing ship data do not satisfy the recommended minimum number of observations for obtaining 0.5 m/s and 0.2 C accuracy for most locations. The effect of having remotely sensed data is discussed.

Legler, David M.↗
NASA NTRS1992

The determination of operational and support requirements and costs during the conceptual design of space systems

The first year of research to provide NASA support in predicting operational and support parameters and costs of proposed space systems is reported. Some of the specific research objectives were (1) to develop a methodology for deriving reliability and maintainability parameters and, based upon their estimates, determine the operational capability and support costs, and (2) to identify data sources and establish an initial data base to implement the methodology. Implementation of the methodology is accomplished through the development of a comprehensive computer model. While the model appears to work reasonably well when applied to aircraft systems, it was not accurate when used for space systems. The model is dynamic and should be updated as new data become available. It is particularly important to integrate the current aircraft data base with data obtained from the Space Shuttle and other space systems since subsystems unique to a space vehicle require data not available from aircraft. This research only addressed the major subsystems on the vehicle.

Ebeling, Charles↗
NASA NTRS2005

Smart Sensors' Role in Integrated System Health Management

During the last decade, there has been a major effort in the aerospace industry to reduce the cost per pond of payload and become competitive in the international market. Competition from Europe, Japan, and China has reduced this cost to almost a third from 1990 to 2000. This cost has leveled in recent years to an average price of around $12,000/pound of payload. One of NASA's goals is to promote the development of technologies to reduce this cost by a factor of 10 or more Exploration of space, specially manned exploration missions, involves very complex launch and flight vehicles, associated ground support systems, and extensive human support during all phases of the mission. When considering the Space Shuttle Program, we can see that vehicle and ground support systems' processing, operation, and maintenance represent a large percentage of the program cost and time. Reducing operating, processing and maintenance costs will greatly reduce the cost of Exploration programs. The Integrated System Health Management (ISHM) concept is one of the technologies that will help reduce these operating, processing and maintenance costs. ISHM is an integrated health monitoring system applicable to both flight and ground systems. It automatically and autonomously acquires information from sensors and actuators and processes that information using the ISHM-embedded knowledge. As a result, it establishes the health of the system based on the acquired information and its prior knowledge. When this concept is fully implemented, ISHM systems shall be able to perform failure prediction and remediation before actual hard failures occurs, preventing its costly consequences. Data sources, sensors, and their associated data acquisition systems, constitute the foundation of the system. A smart sensing architecture is required to support the acquisition of reliable, high quality data, required by the ISHM. A thorough definition of the smart sensor architectures, their embedded diagnostic agents, and communication protocols need to be established and standardized to allow the embedding and exchange of health information among sensors and ISHM. This workshop is aimed to foster the exchange of ideas and lessons learned between government, industry and academia to aid in the establishment of ISHM (and smart sensors) standards and guidelines as well as to identify present technology gaps that will have to be overcome to successfully achieve this goal.

Perotti, Jose M.↗
NASA NTRS2012

Linked Open Data in the Global Change Information System (GCIS)

The U.S. Global Change Research Program (http://globalchange.gov) coordinates and integrates federal research on changes in the global environment and their implications for society. The USGCRP is developing a Global Change Information System (GCIS) that will centralize access to data and information related to global change across the U.S. federal government. The first implementation will focus on the 2013 National Climate Assessment (NCA) . (http://assessment.globalchange.gov) The NCA integrates, evaluates, and interprets the findings of the USGCRP; analyzes the effects of global change on the natural environment, agriculture, energy production and use, land and water resources, transportation, human health and welfare, human social systems, and biological diversity; and analyzes current trends in global change, both human-induced and natural, and projects major trends for the subsequent 25 to 100 years. The NCA has received over 500 distinct technical inputs to the process, many of which are reports distilling and synthesizing even more information, coming from thousands of individuals around the federal, state and local governments, academic institutions and non-governmental organizations. The GCIS will present a web-based version of the NCA including annotations linking the findings and content of the NCA with the scientific research, datasets, models, observations, etc. that led to its conclusions. It will use semantic tagging and a linked data approach, assigning globally unique, persistent, resolvable identifiers to all of the related entities and capturing and presenting the relationships between them, both internally and referencing out to other linked data sources and back to agency data centers. The developing W3C PROV Data Model and ontology will be used to capture the provenance trail and present it in both human readable web pages and machine readable formats such as RDF and SPARQL. This will improve visibility into the assessment process, increase understanding and reproducibility, and ultimately increase credibility and trust of the resulting report. Building on the foundation of the NCA, longer term plans for the GCIS include extending these capabilities throughout the U.S. Global Change Research Program, centralizing access to global change data and information across the thirteen agencies that comprise the program.

Tilmes, Curt A.↗
NASA NTRS2012

Demystifying Kepler Data: A Primer for Systematic Artifact Mitigation

The Kepler spacecraft has collected data of high photometric precision and cadence almost continuously since operations began on 2009 May 2. Primarily designed to detect planetary transits and asteroseismological signals from solar-like stars, Kepler has provided high quality data for many areas of investigation. Unconditioned simple aperture time-series photometry are however affected by systematic structure. Examples of these systematics are differential velocity aberration, thermal gradients across the spacecraft, and pointing variations. While exhibiting some impact on Kepler's primary science, these systematics can critically handicap potentially ground-breaking scientific gains in other astrophysical areas, especially over long timescales greater than 10 days. As the data archive grows to provide light curves for 10(exp 5) stars of many years in length, Kepler will only fulfill its broad potential for stellar astrophysics if these systematics are understood and mitigated. Post-launch developments in the Kepler archive, data reduction pipeline and open source data analysis software have occurred to remove or reduce systematic artifacts. This paper provides a conceptual primer for users of the Kepler data archive to understand and recognize systematic artifacts within light curves and some methods for their removal. Specific examples of artifact mitigation are provided using data available within the archive. Through the methods defined here, the Kepler community will find a road map to maximizing the quality and employment of the Kepler legacy archive.

Kinemuchi, K.↗
NASA NTRS2014

Comparison of Computational Approaches for Rapid Aerodynamic Assessment of Small UAVs

Computational Fluid Dynamic (CFD) methods were used to determine the basic aerodynamic, performance, and stability and control characteristics of the unmanned air vehicle (UAV), Kahu. Accurate and timely prediction of the aerodynamic characteristics of small UAVs is an essential part of military system acquisition and air-worthiness evaluations. The forces and moments of the UAV were predicted using a variety of analytical methods for a range of configurations and conditions. The methods included Navier Stokes (N-S) flow solvers (USM3D, Kestrel and Cobalt) that take days to set up and hours to converge on a single solution; potential flow methods (PMARC, LSAERO, and XFLR5) that take hours to set up and minutes to compute; empirical methods (Datcom) that involve table lookups and produce a solution quickly; and handbook calculations. A preliminary aerodynamic database can be developed very efficiently by using a combination of computational tools. The database can be generated with low-order and empirical methods in linear regions, then replacing or adjusting the data as predictions from higher order methods are obtained. A comparison of results from all the data sources as well as experimental data obtained from a wind-tunnel test will be shown and the methods will be evaluated on their utility during each portion of the flight envelope.

Shafer, Theresa C.↗
NASA NTRS2016

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.

Space Biology↗
NASA NTRSDate not supplied

June 2023 Canadian Wildfire Effects on Hampton Roads VA using ASDC Data

In June of 2023 thousands of acres of wildfires spread across northwestern Canada. This event emitted huge amounts of smoke, and pollutants that travelled east across the North America, the Atlantic ocean and eventually all the way to Continental Europe. This smoke created a dangerous air quality event throughout the Atlantic Coast. As climate change increases the prevalence of wildfires more cities and states will be affected by the dangerous smoke and pollutants produced by these events. In this poster we documented and investigated the June 06th Air Quality Event over Hampton Roads, VA. To perform a comparative analysis, we utilized two data sources: NASA’s Atmospheric Science Data Center we used the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), the Deep Space Climate ObserVatoRy (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC), data product, and the EPA’s ground-based Air Quality System (AQS).

Air Quality↗
NASA NTRS2012

Flow Analysis Tool White Paper

Faster networks are continually being built to accommodate larger data transfers. While it is intuitive to think that implementing faster networks will result in higher throughput rates, this is often not the case. There are many elements involved in data transfer, many of which are beyond the scope of the network itself. Although networks may get bigger and support faster technologies, the presence of other legacy components, such as older application software or kernel parameters, can often cause bottlenecks. Engineers must be able to identify when data flows are reaching a bottleneck that is not imposed by the network and then troubleshoot it using the tools available to them. The current best practice is to collect as much information as possible on the network traffic flows so that analysis is quick and easy. Unfortunately, no single method of collecting this information can sufficiently capture the whole endto- end picture. This becomes even more of a hurdle when large, multi-user systems are involved. In order to capture all the necessary information, multiple data sources are required. This paper presents a method for developing a flow analysis tool to effectively collect network flow data from multiple sources and provide that information to engineers in a clear, concise way for analysis. The purpose of this method is to collect enough information to quickly (and automatically) identify poorly performing flows along with the cause of the problem. The method involves the development of a set of database tables that can be populated with flow data from multiple sources, along with an easyto- use, web-based front-end interface to help network engineers access, organize, analyze, and manage all the information.

Boscia, Nichole K.↗