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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

SPRINT: Scheduling Planning Routing Intersatellite Network Tool

The Scheduling Planning Routing Intersatellite Network Tool (SPRINT) software system plans and schedules the operations (observations, inter-satellite crosslink communications, and ground communications) of Earth observation (EO) constellations of hundreds of resource-constrained small satellites to enable global, near real-time science. Historically, most CubeSats have flown radios only for direct-to-ground downlink of data; as a result, data availability is delayed by as long as it takes for the capturing satellite to pass over a ground station, typically hours. Current Planning and Scheduling (P&S) systems for constellations cannot handle data routing through a large, dynamic network topology, and all planning is handled on the ground without .the ability to autonomously prioritize important observations, or respond to unexpected changes in communication window or bandwidth. SPRINT directly handles both data routing for low latency bulk observation data downlink and replanning onboard to deal with dynamic priorities and fault response, maximizing the utility of downlinked data.

Kerri Cahoy↗
NASA NTRSDate not supplied

Development of a High-Fidelity CLARREO Pathfinder Simulator

CLARREO Pathfinder is a hyperspectral instrument designed to resolve reflectance of solar radiation from Earth in the wavelength range of 350 nm to 2300 nm with an uncertainty of 0.3% (k=2) or less. Its success will demonstrate CLARREO’s ability to provide on-orbit SI-Traceable calibration of measured spectral reflectance with an advanced accuracy. The CLARREO pathfinder simulator has been developed to generate high-fidelity inter-calibration event data products that can be used for pre-launch inter-calibration algorithm study. A high-fidelity simulation for CLARREO inter-calibration events includes computationally intensive radiative transfer calculations for observations within each individual collocation footprint. A principal component based radiative transfer model is incorporated in the simulator to enable an ultra-fast forward simulation, ensuring a low latency data processing for month- to year-long intercalibration events. This paper introduces the design and development of the CLARREO pathfinder simulator and some of its applications for inter-calibration matching error evaluation.

Wan Wu↗
NASA NTRSDate not supplied

Tracking 2020 Decreases in Carbon Dioxide Due to the COVID19 Pandemic in NASA’s GEOS Modeling system: Implications for Space-Based Carbon Monitoring

The COVID19 pandemic led to abrupt, worldwide changes in human activity and related emissions of air pollutants and greenhouse gases that are unprecedented in modern times. NASA satellites have demonstrated their ability to observe some of these impacts, particularly in relation to short-lived gases like nitrogen dioxide (NO2), which is emitted primarily from transportation. Bottom-up analyses of carbon dioxide (CO2) emissions suggest that the growth of atmospheric CO2 has also slowed, but differences are much more subtle than for NO2 because of the lifetime of atmospheric CO2 and sectoral differences in emission reductions. Estimates of CO2 from NASA’s Orbiting Carbon Observatory 2 (OCO-2) provide a unique view of COVID19 impacts, but observed changes in the column-average CO2 can be difficult to interpret because of gaps in spatial coverage. Assimilating these data into the Goddard Earth Observing System (GEOS), an integrated Earth system model with an advanced Constituent Data Assimilation System (CoDAS), helped to reveal changes in CO2 that are consistent with separate, bottom-up analyses of emissions reductions. Both indicate that from February-April of 2020, the growth in CO2 over Europe, North America, and Asia was roughly 0.3 ppm less than during the previous four years. Anomalies derived from gap-filled GEOS OCO-2 CoDAS products contribute to a joint effort by the world’s space agencies to track COVID19 impacts on the Earth. However, attribution of these changes is complicated by interannual variability in atmospheric circulation and the influence of climate on ocean and land carbon sinks. We discuss these results from the perspective of space-based carbon monitoring, which has received considerable support over the past decade from NASA. Our results demonstrate the accomplishments of current sensors and data assimilation systems, but also highlight challenges in providing high quality, low latency information to the public. In particular, understanding and attributing CO2 changes during 2020 requires year-specific information about land and ocean fluxes, which is often delayed for months or even years. We discuss current limitations and potential solutions to address these lags, which would support more reliable and timely space-based carbon monitoring.

COVID-19↗
NASA NTRS2020

Gamma-Ray Urgent Archiver for Novel Opportunities (GUANO): Swift/BAT Event Data Dumps on Demand to Enable Sensitive Subthreshold GRB Searches

We introduce a new capability of the Neil Gehrels Swift Observatory to provide event-level data from the Burst Alert Telescope (BAT) on demand in response to transients detected by other instruments. We show that the availability of these data can effectively increase the rate of detections and arcminute localizations of gamma-ray bursts (GRB) like GRB 170817 by >400%. We describe an autonomous spacecraft-commanding pipeline purpose built to enable this science; to our knowledge, this is the first fully autonomous extremely low-latency commanding of a space telescope for scientific purposes. This pipeline has been successfully run in its complete form since 2020 January, and has resulted in the recovery of BAT event data for >800 externally triggered events to date (gravitational waves, GWs; neutrinos; GRBs triggered by other facilities; fast radio bursts; and very high-energy detections), now running with a success rate of ∼90%. We exemplify the utility of this new capability by using the resultant data to (1) set the most sensitive upper limits on prompt 1 s duration short GRB-like emission within ±15 s around the unmodeled GW burst candidate S200114f, and (2) provide an arcminute localization for short GRB 200325A and other bursts. We also show that using data from GUANO to localize GRBs discovered by other instruments, we can increase the net rate of arcminute-localized GRBs by 10%–20% per year. Along with the scientific yield of more sensitive searches for subthreshold GRBs, the new capabilities designed for this project will serve as the foundation for further automation and rapid target of opportunity capabilities for the Swift mission, and have implications for the design of future rapid-response space telescopes.

Aaron Tohuvavohu↗
NASA NTRSDate not supplied

NASA SPoRT Suite of Legacy and Current Satellite Products in Support of Tropical Analysis and Forecasting

The NASA Short-term Prediction, Research, and Transition (SPoRT) Program works closely with NOAA/NWS weather forecasters to transition unique satellite data and capabilities into operations in order to assist with nowcasting and short-term forecasting issues. SPoRT has applied data and capabilities from a variety of research-oriented missions to improve the operational analysis and short-term forecasting of the tropical environment and tropical cyclones(TC).Toward researching new products, SPoRT is examining the diurnal cycle of TC intensity by observing changes in precipitation, winds, and midlevel moisture, including the use of NUCAPS soundings to assess the moisture and temperature environment around TCs, as well as examining changes in sounding profiles associated with the TC diurnal cycle. SPoRT has developed capabilities to analyze the evolution and diurnal cycle of GPM/IMERG rain rates and GLM lightning characteristics associated with TC, by compass and up-/down-shear quadrants relative to the cyclone center. As Early Adopters in the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, SPoRT is also in the process of developing new experimental products utilizing TROPICS data. Furthermore, SPoRT is investigating the relationship between TC intensity and lightning flash size and optical energy. MSFC hosts a GOES ground rebroadcast station which enables the potential for very low latency GLM products. These products provide the ability to monitor tropical regions/systems in data-void oceanic regions and to fill the gaps in traditional observational systems. SPoRT seeks to expand our collaborations with operational centers and other stakeholders to provide new tools to aid in tropical analysis and forecasting.

satellite remote sensing↗
NASA NTRS2021

GstLAL: A software framework for gravitational wave discovery

The GstLAL library, derived from Gstreamer and the LIGO Algorithm Library, supports a stream-based approach to gravitational-wave data processing. Although GstLAL was primarily designed to search for gravitational-wave signatures of merging black holes and neutron stars, it has also contributed to other gravitational-wave searches, data calibration, and detector-characterization efforts. GstLAL has played an integral role in all of the LIGO-Virgo collaboration detections, and its low-latency configuration has enabled rapid electromagnetic follow-up for dozens of compact binary candidates.

Gravitational waves↗
NASA NTRSDate not supplied

Development and Demonstration of a Wideband RF User Terminal for Roaming between Ka-band Relay Satellite Networks

The National Aeronautics and Space Administration (NASA) has provided low-latency communication relay services to science and exploration spacecraft for nearly four decades with the Tracking and Data Relay Satellite System (TDRSS). Through NASA’s Communications Services Project (CSP), the agency is pursuing a transition away from dedicated government-owned communication relay satellites and infrastructure in favour of commercially provided satellite communication (SATCOM) services. Many of these SATCOM services are offered in the K/Ka-band covering spectrum allocated to commercial, military, and civilian space operations. While these services were intended to provide broadband connectivity for terrestrial, maritime, and aviation customers, many can be tailored to support orbiting spacecraft. By introducing commercial SATCOM providers, NASA intends to create an interoperable network of networks which will enable missions to roam between multiple service providers. A key enabling technology needed for this vision is a multi-frequency (wideband), multi-waveform user terminal which can seamlessly roam between multiple providers. This paper discusses the development and ground demonstration of a new wideband RF user terminal prototype designed to roam across multiple networks over the Ka-band, allowing ubiquitous service, resiliency, and flexibility for the user.

satellite communication↗
NASA NTRS2021

The Analysis of Using Satellite Soil Moisture Observations for Flood Detection, Evaluating Over the Thailand’s Great Flood of 2011

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyses the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS (AMSR-E), the Advanced SCATterometer (ASCAT) on MetOp, the Global Land Data Assimilation System (GLDAS), and the WaterGAP Global Hydrology Model (WGHM). In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission (TRMM), and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment (GRACE) as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin (CPB) in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems.

Satellite soil moisture↗
NASA NTRS2022

A Multi-sensor Evaluation of Precipitation Uncertainty for Landslide-triggering Storm Events

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall-triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select among a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity-duration thresholds. The average intensity, peak intensity, duration, and NOAA-Atlas return periods are compared ahead of reported landslides across the continental US and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real-time and post-processed satellite (IMERG), radar (MRMS), and gauge-based (NLDAS-2). Landslide-triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296mm with an average range of 51mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8262745mm/hr and as much as 57mm/hr. The two products more reliant upon ground-based observations (MRMS and NLDAS-2) performed better at identifying landslides according to published intensity duration storm thresholds, but all products exhibited hit-ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually-verified landslide locations. We recommend practitioners consider low-latency products like MRMS for investigating landslides, given their near-real time data availability and good performance in detecting landslides. Practitioners would be well-served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.

Precipitation inter-comparison↗
NASA NTRSDate not supplied

Assessment Overview: Regional Impacts of Using the Day Cloud Phase Distinction RGB for Lightning IDSS

The new generation of GOES satellites has presented a wealth of novel observations and data products to the National Weather Service forecasters throughout the country. While high quality, low latency data is always welcome in operational environments, it is valuable to understand how new information is interpreted, and how it complements and supplements existing datasets. To that end, NASA SPoRT, in partnership with the NWS, hosted a formal assessment of the use of the Day Cloud Phase Distinction RGB to anticipate lightning initiation as indicated by either GLM, NLDN, or both, using an approach outlined by Elsenheimer and Gravelle (2019). As is typical for SPoRT assessments, we focused on a targeted time period (about 6 weeks in June and July 2021) and provided a brief training module to help forecasters become familiar with the application we were targeting and the objectives of our assessment. Unlike typical SPoRT assessments, we focused on a broad geographic region, coordinating with NWS Regional offices to garner participation from several different WFOs throughout the country. Given the field of view and observation angles of GOES-East and GOES-West, which anecdotally impacts GLM lightning observations in particular, having a geographically diverse participant list helps us interpret how these viewing differences may impact interpretation of the RGB and its value in anticipating lightning. Not unexpectedly, we found that the participating forecasters expressed that they could readily interpret the RGB to recognize glaciation and, in the absence of lightning data, use the RGB to anticipate that lightning was possible, for the purpose of impact-based decision support services (IDSS). Lead times from observations of glaciation to first flash were 19 minutes on average, and -10C isothermal reflectivities ranged from 34.5 to 52.5 dBZ. Herein, we present the detailed results of the formal assessment period and provide next steps and recommendations based on forecaster feedback for using the DCPD RGB in the context of anticipating lightning initiation and for IDSS, including regional differences in observations.

satellite remote sensing↗
NASA NTRS2021

First Demonstration of Early Warning Gravitational-wave Alerts

Gravitational-wave observations became commonplace in Advanced LIGO-Virgo's recently concluded third observing run. 56 nonretracted candidates were identified and publicly announced in near real time. Gravitational waves from binary neutron star mergers, however, remain of special interest since they can be precursors to high-energy astrophysical phenomena like γ-ray bursts and kilonovae. While late-time electromagnetic emissions provide important information about the astrophysical processes within, the prompt emission along with gravitational waves uniquely reveals the extreme matter and gravity during—and in the seconds following—merger. Rapid communication of source location and properties from the gravitational-wave data is crucial to facilitate multimessenger follow-up of such sources. This is especially enabled if the partner facilities are forewarned via an early warning (pre-merger) alert. Here we describe the commissioning and performance of such a low-latency infrastructure within LIGO-Virgo. We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger. We set expectations for these alerts in future observing runs.

Ryan Magee↗
NASA NTRSDate not supplied

Advancing NASA SatCORPS Global Data Products with Cloud Computing and Machine Learning

Operational satellite imager radiances are valuable for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. The use of cloud computing has enabled recent enhancements to process a constellation of geostationary satellites at higher spatiotemporal resolutions than previously possible that meet low latency and near real-time needs. Data taken from Meteosat-8 and -11, Himawari, GOES-16, and -17 are processed and combined with operational polar orbiting satellite data and composited on a 3-km grid to provide global coverage. To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. An update on recent SatCORPS enhancements is presented, highlighting the community benefits achieved with the use of cloud computing and machine learning.

William L Smith↗
NASA NTRSDate not supplied

NASA's Deep Space Network (DSN) Lunar Exploration Upgrades (DLEU)

In the near future, the National Aeronautics and Space Administration (NASA) will return to the moon beginning the next era of human exploration. NASA’s Space Communications and Navigation (SCaN) program will play a vital role in establishing communications and navigation support to realize the ambitious goals of the Artemis program. SCaN’s overall lunar communications support plan will be covered in a separate 2023 SpaceOps paper: “NASA’s Communications and Navigation Architecture Plans to Support the Return to the Moon and a Sustainable Lunar Presence”. The plan as it currently stands, includes a three-fold approach of lunar relay services, a dedicated set of new ground stations and support through the Deep Space Network (DSN). This paper will have a more granular focus on the DSN and NASA’s plans to upgrade and expand the network to be better suited for human spaceflight on and around the lunar surface. NASA’s Deep Space Network (DSN) will be a critical communications component for the upcoming lunar activities. There will be multiple spacecraft, using different bands, and some of those spacecraft will be transmitting and receiving using multiple bands, requiring DSN support of S-band (2 GHz), X-band (7 GHz up, 8 GHz down), and K-band (22.5 GHz up, 26 GHz down). Since there may be more than one spacecraft in the beamwidth of the DSN antennas, the DSN support will require an extension of the DSN’s capability to support multiple spacecraft using one antenna, expanding it to provide two simultaneous uplinks in the different bands at each antenna. Achieving this requires using new techniques for manufacturing the frequency selective surfaces, called dichroics, which steer the different frequency beams from and to the appropriate transmitting and receiving equipment, along with the addition of a new K-band uplink system. Additionally, due to the relative closeness of the moon from Earth (as opposed to the planetary missions the DSN supports daily), significantly higher data rates on both uplink and downlink are required, specifically up to 20 Mbps on the uplink and 150 Mbps on the downlink, both using Low Density Parity Check (LDPC) error correcting codes. And, again due to the relative closeness of the moon, there is a need for low latency data delivery of the high rate downlink telemetry which requires a change in the current DSN paradigm of delivering higher rate data with higher latency.

deep space network↗
NASA NTRSDate not supplied

Hyperspectral Sounder Products: SiFSAP and ClimFiSP

Infrared hyperspectral sounders have demonstrated to provide very high value information for a range of weather and climate applications. Traditional retrieval algorithms of hyperspectral sounders use the ‘cloud clearing’ methodology in the retrieval process due to the lack of means to account for cloud scattering effect. The corresponding Level-2 data products are therefore limited by two major factors: the degradation of spatial resolution as compared with the native resolution of the instruments and the lack of ‘radiance closure’ that is critically needed for climate trend studies. The single field-of-view (FOV) sounder atmospheric products (SiFSAP) algorithm has been developed to supplement existing operational products. The SiFSAP algorithm uses the principal component based radiative transfer model (PCRTM) that is ultra-fast and has been extensively validated for the cloud scattering simulation capability. The cloud properties are physically retrieved along with profiles of temperature, moisture, and trace gases of interest, surface properties simultaneously by fitting single FOV spectral measurements. Therefore, SiFSAP ensure the radiometric consistency between the retrieved geophysical properties and the top-of-atmosphere (TOA) spectral measurements. This presentation provides a general introduction of SiFSAP and details about the physical retrieval algorithm. Also introduced here is a derivative data product of SiFSAP, the climate fingerprinting Sounder Products (ClimFiSP). ClimFiSP include the space-time averaged properties of key climate variables that are derived from the space-time averaged radiances via the spectral fingerprinting scheme. The spectral fingerprinting method allows a low latency data update on ClimFiSP following any potential calibration update on satellite measurements. ClimFiSP also include the rigorously defined uncertainty characterization for trends or anomalies of climate variables, being critical for the construction of long-term climate data record. Both SiFSAP and ClimFiSP will be available to users through NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC).

Wan Wu↗
NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.

Joseph C Coughlan↗
NASA NTRS2022

Introducing and Evaluating the Climate Hazards Center IMERG with Stations (CHIMES) Timely Station-Enhanced Integrated Multisatellite Retrievals for Global Precipitation Measurement

As human exposure to hydroclimatic extremes increase and the number of in situ precipitation observations declines, precipitation estimates, such as those provided by the Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) mission, provide a critical source of information. Here, we present a new gauge-enhanced dataset [the Climate Hazards Center IMERG with Stations (CHIMES)] designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center (CHC) high-resolution climatology (CHPclim) and low-latency rain gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim ’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES. The CHC’s curated rain gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERGlate fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERG late performs well (r = 0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50% to 63% and decreases the mean absolute error from 48 to 39 mm month −1. Gauge enhancement then increases the variance explained to 75%, reducing the mean absolute error to 27 mm month −1.

Chris C funk↗
NASA NTRSDate not supplied

Supporting Space Weather with the Geospace Dynamics Constellation

The Geospace Dynamics Constellation (GDC) mission, planned to launch at the end of the decade, is a six-satellite constellation that will fly through the ionosphere and thermosphere at around 400 km. While GDC is a science mission, its comprehensive instrumentation will not only contribute to our understanding of space weather phenomena in the ionosphere-thermosphere system, but will also provide valuable, space weather-relevant data. Data from GDC will be made available at low latency via real-time space weather data stream. We are working with operational partners to identify space weather data products and coordinate reception of the space weather beacon data. Alongside preparations for real-time GDC data streams, we are working to identify current model capabilities and needs, to ensure that space weather models that can make use of GDC measurements are moving along the Research-toOperations pipeline. Before GDC launch, Observing System Simulation Experiments (OSSEs) carried out with synthetic GDC data can be used to demonstrate the capabilities of models and to predict the impact of GDC data. Following launch, Observing System Experiments (OSEs) will demonstrate the impact of GDC space weather data. In preparation for GDC, the ITM space weather community should establish baseline metrics for space weather parameters of scientific and operational interest. These metrics, tracked over time before and after the launch of GDC, will allow us to track advancements in forecasting, nowcasting, and hindcasting of the ITM system and to trace the impact of scientific progress from space weather research into operations. By demonstrating the impact of real-time GDC data, specific data needs can be identified and prioritized for long term investment on future observing systems.

Katherine Garcia-Sage↗
NASA NTRSDate not supplied

Open Data Integration (ODIN): A Concurrent, Distributed Message-Based Architecture and Framework for Disaster Response

The Runtime for Airspace Concept Evaluation (RACE) is an open-source software architecture and framework to build configurable, highly concurrent and distributed message-based systems that offer scalable, low-latency performance on commodity hardware. RACE was used in commercial aviation applications to rapidly build systems that span several machines (including synchronized displays), interface existing hardware simulators and other live data feeds, and incorporate sophisticated visualization components such as NASA WorldWind. These RACE applications validated elements of the FAA’s System Wide Information Management (SWIM) Program, handling up to 1000 messages/sec from diverse sources (SFDPS, TFM-DATA, TAIS, ASDE-X, ITWS and local ADS) for 4,500 simultaneous flights tracked in the next-generation air transportation system’s digital backbone. We have since generalized RACE to support Open Data Integration (ODIN) applications outside aviation. Systems built with RACE/ODIN can be deployed in the field, on commodity hardware, and operate with limited or intermittent connectivity to the outside world. Our primary use case is a web-server with local/persistent data storage that runs within and only serves the stakeholder network (e.g. an incident command post). We are tailoring the RACE/ODIN system to support wildland fire management for the upcoming NASA Wildland Fire Safety Demonstration Series. RACE-ODIN is under consideration for application in the Scalable Traffic Management for Emergency Response Operations project, or STEReO, which aims to create a system that can be deployed during emergencies, to coordinate multiple elements of disaster response. Such data sources predominantly come from existing services on the internet (e.g. weather and satellite data, imported from so called "edge servers") but can also include dynamic (real-time) data from computer simulations and within the stakeholder network (such as aircraft and personnel tracking information). We will present the architecture and ODIN system demonstration incorporating local data from instrumented power-line towers, interpolated weather data and geospatial data from space-based platforms.

Guillaume P Brat↗