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At least 343 records · Page 19

EARTHDATA PUB: A Data Publication Workflow Solution for NASA’s EOSDIS

Each NASA Distributed Active Archive Center (DAAC) faces the challenge of dealing with an increasingly diverse number of publishable data products from diverse data producers. Data producers, on the other hand, may experience pain points when interacting with the EOSDIS for the first time or when publishing different data at different DAACs. As a result, there has been a growing need to develop a common software framework that serves as a common interface for data producers, rigorously defines the data publication procedure for DAAC staff, facilitates the management of various data publication processes, and tracks the progress of data publication. This software should also account for the different configurations at different DAACs. Currently, two primary data publication workflow and tracking tools exist in operation at the EOSDIS: Semi-Automated ingest System (SAuS) and Data Publication workflow Portal (DAPPeR). However, neither tool is cloud-ready. Automated data processing could be managed by Cumulus, an EOSDIS cloud-based data ingest, archive and management system. However, Cumulus does not support manual tasks or on-premise implementations. We propose to develop the Earthdata Publication Minimum Viable Product (Earthdata Pub MVP) -- a cloud-hosted solution that works with both cloud and on-premise systems and implements the communications and exchange requirements generated by the Earthdata Pub information architecture team.

Rice, Justin L.↗

Changing Climate, Changing Data: Exposing Climate Data to New Users Through GeoPlatform.gov’s Resilience Community

Over 700 climate related datasets were curated by subject matter experts into 9 thematic areas as a part of the Climate Data Initiative (CDI). NASA was tasked with maintaining the collection’s data inventory and supporting web pages at data.gov/climate. Today, the Data Curation for Discovery (DCD) team at MSFC continues to support the CDI collection. In order to expose the collection to a new and growing user community, the DCD team has partnered with GeoPlatform.gov to develop the Resilience community. The Resilience community serves as an interactive, topically-focused web portal that further promotes and shares CDI web content, datasets, services, maps, and other tools relevant to global resilience and change. This poster focuses on the team’s efforts to leverage GeoPlatform’s semantic applications to link CDI objects within the platform to improve discoverability. This poster also provides insights as to how this effort may serve as an example for building and expanding future Geoplatform.gov communities..

Sisco, Adam↗

Machine Learning-Based Atmospheric Phenomena Detection Platform

As the number of Earth pointing satellites has increased over the last several decades, the data volume retrieved from instruments onboard these satellites has also increased. It is expected that this trend will continue as more data intensive missions and small satellite constellations are launched. Currently, feature detection - namely atmospheric phenomena - in these datasets is performed manually and is thus not scalable with the growing data archives. Recent advancements in computational efficiency allow for the Earth science community to leverage machine learning to identify interesting atmospheric phenomena. Given the wide range of distinctive features in various atmospheric phenomena, a specialized machine learning model is required for accurate detection of these phenomena independently. The Phenomena Portal, developed at NASA IMPACT, is designed to provide visualization for the output from these machine learning models. In addition, detected events for each atmospheric phenomena are stored in a database that can be used to more easily use/subset larger spatiotemporal datasets. The user interface also incorporates additional features to enhance the user experience including spatiotemporal analysis, multiple base layer images, and a slider to filter events with lower probabilities of positive detection. Each detection supports user feedback on whether the detection is true or false that can then be stored and used to improve the machine learning model performance.

Gurung, Iksha↗

Bridging the Gap Between Requirements and Simulink Model Analysis

Formal verification and simulation are powerful tools for the verification of requirements against complex systems. Requirements are developed in early stages of the software lifecycle and are typically expressed in natural language. There is a gap between such requirements and their software implementations.We present a framework that bridges this gap by supporting a tight integration and feedback loop between high-level requirements and their analysis against software artifacts. Our framework implements an analysis portal within the fret requirements elicitation tool, thus forming an end-to-end, open-source environment where requirements are written in an intuitive, structured natural language, and are verified automatically against Simulink models.

Mavridou, Anastasia↗

The NASA Micro Pulse Lidar Network (MPLNET): Introduction of the New Version 3 Release

The NASA Micro-Pulse Lidar Network (MPLNET) is a global federated network of polarized Micro-Pulse Lidar (MPL) systems running continuously. MPLNET began in 2000, and there have been over 70 sites deployed worldwide, with 24 sites currently active and a few more planned over the next year. Seven of the long-term sites have 10+ years of data, and many more have 5+ years. Most sites are co-located with AERONET providing joint data on column and vertically resolved aerosol and cloud information. This presentation will introduce our new Version 3 MPLNET data. All sites in the network now feature eye-safe polarized backscatter MPL instruments, providing information on attenuated backscatter and particle shape. In addition to change with our signal data, we have an enhanced cloud product suite, a new PBL height product, and inclusion of the new AERONET lunar aerosol optical depth into MPLNET aerosol retrievals. A new quality flag process will be used to better describe all data products. Finally, a new data portal will provide near-real-time (NRT) access to all data products, including new quality assured NRT L1.5 products. Custom products developed for model specific applications will also be provided.

Welton, Ellsworth J.↗

TPSAS-NF1676L-12770-DND

CEOS Action (Plenary 2010, Action 24-3): CEOS Chair to work with the SEO, outgoing and incoming CEO and GEO Secretariat to study possibilities for rationalisation of the multiplicity of portals provided by CEOS in support of GEO.

Brian Killough↗

TPSAS-NF1676L-14964-DND

Radiation models and methods (software) have been developed by researchers for 35+ years. Few had access to the software and it was very difficult to combine, maintain, and update. OLTARIS – On-Line Tool for the Assessment of Radiation in Space: - Web portal created to supply the “best practices” in radiation shielding models, methods, and algorithms to the community - Rigorously developed in a modular framework that is easily maintained and updated - Easily accessible by NASA’s customers and collaborators

Chris A Sandridge↗

IDN Update

To present on recent developments of the IDN including the migration of Earth Dataset collection and services to the Common Metadata Repository (CMR), development of dMMT and IDN Search Portal. Discuss working with the other CEOS agencies to get agreements to make decisions on future interoperability approaches for IDN and for the WGISS Connected Assets systems approaches for IDN and for the WGISS Connected Assets systems.

Committee on Earth Observation Satellites (CEOS)↗

Bridging the Gap Between Requirements and Simulink Model Analysis

Formal veri fication and simulation are powerful tools for the veri fication of requirements against complex systems. Requirements are developed in early stages of the software lifecycle and are typically expressed in natural language. There is a gap between such requirements and their software implementations. We present a framework that bridges this gap by supporting a tight integration and feedback loop between high-level requirements and their analysis against software artifacts. Our framework implements an analysis portal within the fret requirements elicitation tool, thus forming an end-to-end, open-source environment where requirements are written in an intuitive, structured natural language, and are veri fied automatically against Simulink models.

FRET↗

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This paper presents a deep learning-based objective, diagnostic estimate of tropical cyclone intensity from infrared satellite imagery with 13.24 kt Root Mean Squared Error (RMSE). In addition, a visualization portal in a production system is presented that displays deep learning output and contextual information for end users, one of the first of its kind.

Manil Maskey↗

Understanding Radiation Environment at Earth and Beyond: Overview of Two Projects

The impact of radiation dramatically increases at high altitudes in the Earth’s atmosphere and in space. Therefore, monitoring and access to radiation environment measurements are critical for estimating the radiation exposure risks of aircraft and spacecraft crews and the impact of space weather disturbances on electronics. Addressing these needs requires reliable access to multi-source radiation environment data and enhanced visualization and search capabilities. The Radiation Data Portal provides an interactive web-based application for convenient search and visualization of inflight radiation measurements.

SMD↗

GeneLab

GeneLab collects and enables analysis of spaceflight and ground-based spaceflight simulation genomic data, RNA and protein expression, and metabolic profiles. It interfaces with other existing databases containing spaceflight omic data. The 2011 National Research Council (NRC) Decadal Survey on NASA Life and Physical Sciences called for increased opportunities for multi-investigator spaceflight opportunities and greater use of genomic approaches to meet the needs of NASA researchers. To address these recommendations of the NRC Decadal Survey, the Space Life and Physical Sciences Research and Applications Division of NASA's Human Exploration and Operations Mission Directorate has initiated a transition to an Open Science architecture to increase research opportunities, and has developed the GeneLab Platform based on highly leveraged and integrated bioinformatics analytics. GeneLab is an interactive, open-access resource where scientists can upload, download, store, search, share, transfer, and analyze omics data from spaceflight and corresponding analogue experiments. Users can explore GeneLab datasets in the Data Repository, analyze data using the Analysis Platform, visualize high-order data and create collaborative projects using the Collaborative Workspace. Our primary goal is to maximize the utilization of the valuable biological research conducted aboard the International Space Station (ISS) by collecting genomic, transcriptomic, proteomic, and metabolomics data known as “omics”. By providing a portal linking processed data to flight parameters, GeneLab enables exploration of the molecular network responses of terrestrial biology to the space environment. This allows researchers to understand the complex responses of biological systems to the space environment. This technology development activity was transferred from the Human Exploration and Operations Mission Directorate to the Science Mission Directorate Division of Biological and Physical Sciences (BPS) in October 2020.

GeneLab↗

Algorithm Theoretical Basis Document (ATBD) - Stream Stage Measurements: V2.5.1 Water Level Products from Satellite Radar Altimetry

In response to the 2018 NASA ROSES Applied Sciences/Water Resources (NASA HQ Program Official: Dr. Brad Doorn) call for proposals, the “Integration of Remotely Sensed Streamflow Data into Alaska Water Resource Management Agency Operations” project with Principal Investigator (PI) Jack Eggleston USGS, was successful, and had the ultimate goal of creating a series of remotely sensed or derived Alaska river parameters for integration into NWIS. These parameters included surface water height and average reach surface water slope (from altimetry), average reach width (from Landsat imagery), and an associated river discharge derived via theoretical means. The surface water height products were required to have both archival and near real time components, noting the availability of ~25years of potential measurements, and accepting the temporal resolution (10-35days) of the suite of radar altimeters. Each surface water level product was expected to be a continuous time series of observation with a sufficient accuracy to highlight monthly, seasonal and interannual variation. The designated set of river reaches were chosen for their geographical distribution, their reach width, and the presence of a radar altimeter mission satellite overpass. This document describes the procedure associated with the creation of these altimetric surface water level products and is relevant to product Version 2.5.1 available from the Global Water Monitor (GWM) web portal.

Altimetry↗

Reaching Broad Audiences from a Large Agency Setting

NASA’s missions, engineering accomplishments, and scientific findings have inspired generations and advanced our understanding of the world we live in. NASA’s Earth science data are acquired by various sources, including satellites, aircraft, and field measurements. Captured data, their by-products, and their visual representations developed by research teams become available within few hours after satellite overpass or processing through a variety of NASA’s imaging, mapping services, and portals. Such online services as the Global Imagery Browse Services (GIBS), Worldview, LANCE, and LAADS DAAC are freely and openly available thanks to NASA’s Earth-Observing Satellite Data and Information Systems (EOSDIS). These services provide access to products created over the last 30 years, support a broad range of users from the scientific community to the general public, and cover a multitude of applications such as basic and applied scientific research, natural hazard and disaster monitoring, and social and educational outreach. In order to illustrate the significance of the overall work, the visualization products, and the broad range of users, we present three case studies: NASA’s Black Marble Product Suite, the Global Imagery Browse Services (GIBS) and Worldview, and the scientific visualization production process to communicate results to the scientific community and the general public.

Helen-Nicole Kostis↗

Analysis of Historical International Space Station Logistical Mass Delivery

Crewed space exploration missions are extremely logistics dependent, as cargo requirements shape numerous program elements such as vehicle and habitat size. Logistics mass is the mass of items like food and clothing that are not a part of the vehicle or habitat, yet are required by the crew to complete the mission. Numerous studies, such as the Human Exploration Research Analog (HERA) and the Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA) 20-foot chamber analog, have been conceived to research the rates at which crews consume logistics mass. These analogs can simulate many aspects of life in space, including confinement, isolation, limited supplies, and, in certain experiments, the habitat pressure. However, some aspects of space exploration, such as the effects of low gravity and the use of space-based amenities, cannot currently be tested on the ground. In this paper, International Space Station (ISS) manifest data obtained through the National Aeronautics and Space Administration’s (NASA’s) Mission Integration Database Application System (MIDAS) portal is used as a precursor to space-based analogs. The objective of the analysis is determining the breakdown of logistics mass used in space exploration. This identifies potential areas of improvement and highlights the rates at which significant items are supplied, aiding in the weighing of alternative options such as utilizing in space manufacturing for supplies vs. manifesting spares, or cleaning clothing in flight vs. discarding it. Official flight manifests ranging a span of 878 days, just short of the three-year length of a potential human mission to Mars, were analyzed to find the rates at which astronauts consumed various logistics supplies. From this analysis we have found that contrary to our own hypothesis, ‘food’ was not the largest portion of the supplied mass. Instead, ‘Environmental Control and Life Support Systems’ (ECLSS) contributed 34% of the overall supplied mass, followed by ‘Science and Outfitting,’ which contributed 29%. Food totaled less than a quarter (21% of the resupplied mass, while other items of focus in mass reduction efforts such as ‘Hygiene’ (6%), ‘Clothing’ (3%), and ‘Operational Supplies’ (4%) each contributed less than a tenth of the supplied mass. This data suggests that by categorizing and analyzing the data based on an alternative taxonomy and analyzing the full supply manifests rather than handpicked items, we have revealed unexpectedly significant items which had not been previously tracked by exploration logistics efforts. For example, toilet hardware made up 4% of ECLSS mass, and laptop hardware and multi-tools each made up 10% of operational supplies mass. Additionally, the ‘Specialized Clothing’ subcategory containing fire protective equipment, coveralls, and penguin suits made up 15% of the overall clothing mass. By identifying these newly found significant items, we can create more realistic mass estimates for exploration missions and direct mass reduction efforts to new areas, potentially leading to lower future mission masses.

Logistics↗

Geostationary Satellite Observations Over Global Environmental Monitoring Sites

Globally, there are now hundreds of ground-based environmental monitoring stations routinely collecting data on a variety of earth-atmosphere interactions. Such observations are also being augmented with data from orbiting satellites. With the beginning of the EOS-era, the MODIS subset around flux towers has been frequently used for validating ecosystem models developed at flux towers and upscaling the observed flux data to regional scales. However, MODIS on the polar-orbiting satellites can observe target regions only once a day, while the Fluxnet eddy- covariance data are compiled as sub-hourly. Therefore, summarizing the sub-hourly flux data into daily statistics is necessary for the comparison between MODIS and Flux data. The new generation geostationary satellite sensors (GOES-16/17 ABI and Himawari-8/9 AHI) have capabilities similar to MODIS but collect data at 5-15 minute intervals. These high-frequency observations allow us to understand and scale diurnal fluxes. Some studies have already shown the effective utilization of time series of geostationary satellite data for ecosystem modeling. We are producing NEX Level-1G products, which are gridded Top-of-Atmosphere reflectance and brightness temperature data from geostationary satellite sensors. We cut out the NEX Level-1G data using the same file format with the MODIS subset except for the projection. The other data products (e.g., surface reflectance, land surface temperature, vegetation indices, and climate data) will be added upon their availability. Currently included networks are Fluxnet, PhenoCam, and AERONET. The NEX subset data will be provided through NASA NEX data portal.

Geostationary Satellites↗

Calculator for Agricultural Water Use Fractions in California

Improved quantification of agricultural water use efficiency is a primary focus of California's Water Conservation Act. The California Department of Water Resources (CDWR), after extensive stakeholder consultation, has published methods and indicators to help identify opportunities for improved efficiency of irrigation management at various spatial scales. Additional 2017 legislation directs water suppliers to quantify water use efficiency using the CDWR metrics to support preparation of agricultural water management plans. The IrriQuest calculator derives the Crop Consumptive Use Fraction (CCUF), a foundational metric relating seasonal crop evapotranspiration (ET) to total applied water. Satellite normalized difference vegetation index (NDVI) data are used to develop fractional canopy cover timeseries. Additional key inputs include daily reference ET, irrigation volume, irrigation method, precipitation, soil texture and crop type. IrriQuest estimates of seasonal crop ET show less than 10% mean absolute error compared to ground-based measurements of several annual and perennial crops monitored by sensor deployment in commercial fields. Two additional indicators build on CCUF. The Agronomic Water Use Fraction adds consideration of water applied for beneficial purposes such as pre-plant soil preparation, soil salinity management, and climate control. Total Water Use Fraction additionally accounts for water applied for environmental purposes such as maintenance of wetland and riparian habitat. A convenient on-line portal is under development for CCUF estimation and a standalone spreadsheet is available for computation of all three use fractions.

Calculator↗

Using Satellite Soil Moisture and Rainfall in the Landslide Hazard Assessment for Situational Awareness System

The Landslide Hazard Assessment for Situational Awareness system(LHASA)gives a global view of landslide hazard in nearly real time. Currently, it is being upgraded from version 1 to version 2, which entails improvements along several dimensions. These include the incorporation of new predictors, machine learning, and new event-based landslide inventories. As a result, LHASA version 2 substantially improves on the prior performanceand introduces a probabilistic element to the global landslide nowcast. Data from the soil moisture active-passive (SMAP) satellite has been assimilated into a globally consistent data product with a latency less than 3 days, known as SMAP Level 4. In LHASA, thesedata representthe antecedent conditions prior to landslide-triggering rainfall. In some cases, soil moisture may have accumulated over aperiod of many months. The model behind SMAP Level 4 also estimates the amount of snow on the ground, which is an important factor in some landslide events. LHASA also incorporates this information as an antecedent condition that modulates the response torainfall. Slope, lithology, and active faults were also used as predictor variables. These factors can have a strong influence on where landslides initiate.LHASA relies on precipitation estimates from the Global Precipitation Measurement mission to identify the locations where landslides are most probable. The low latency and consistent global coverage of these data make them ideal for real-time applications at continental to global scales. LHASA relies primarily on rainfall from the last 24 hours to spothazardous sites, which is rescaled by the local 99thpercentile rainfall.However, the multi-day latency of SMAP requires the use of a 2-day antecedent rainfall variable to represent the accumulation of rain between the antecedent soil moisture and current rainfall. LHASA merges these predictors with XGBoost, a commonly used machine-learning tool, relying on historical landslide inventories to develop the relationship between landslide occurrence and various risk factors. The resulting model relies heavily on current daily rainfall, but other factors also play an important role. LHASA outputsthe probability oflandslide occurrence ona grid of roughly one kilometer over all continents from 60 North to 60 South latitude. Evaluation over the period 2019-2020 showsthat LHASA version 2 doubles the accuracy of the global landslide nowcast without increasing the global false alarm rate. LHASA also identifies the areas where the human exposure to landslide hazard is most intense. Landslide hazard is divided into 4 levels: minimal, low, moderate, and high. Next, the number of persons and the length of major roads (primary and secondary roads)within each of these areas is calculated for every second-level administrative district (county). These results can be viewedthrough a web portal hosted at the Goddard Space Flight Center. In addition, users can download daily hazard and exposure data.LHASAversion 2uses machine learning and satellite data to identify areas of probable landslide hazard within hours of heavy rainfall. Itsglobal maps are significantly more accurate, and it now includes rapid estimates of exposed populations and infrastructure. In addition, a forecast mode will be implemented soon.

Thomas Stanley↗