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NASA NTRSDate not supplied

Global-to-local air quality forecasts using the NASA GEOS Composition Forecast System

Since 2019, the NASA Global Earth Observing System (GEOS) model has been used to generate global, near-real-time estimates and daily five-day forecasts of atmospheric composition at a horizontal resolution of 0.25 degrees (~25 km) from the surface up to the lower mesosphere. This composition forecast system (“GEOS-CF”) combines the GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to deliver detailed analysis of a wide range of air pollutants, including the policy-relevant species such as ozone, carbon monoxide, nitrogen oxides, sulfur dioxide and fine particulate matter (PM2.5). Because GEOS-CF includes atmospheric levels up through the stratosphere, this system has been leveraged to support the Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite mission and provide stratospheric intrusion alerts to ground-based monitoring stations. We will present recent advances to GEOS-CF which target increased computational efficiency and accuracy. These include the incorporation of simplified chemistry mechanisms to accelerate model forecasts, use of model-observation data fusion techniques to provide highly localized forecasts, and assimilation of satellite observations to produce more accurate model analyses. We further discuss our attempts to make these tools publicly available on platforms outside the NASA domain, such as Google Earth Engine and Amazon Web Services with the goal to facilitate the integration of state-of-the-science air quality information onto platforms used by stakeholders, air quality managers, and the public.

Emma Knowland↗
NASA NTRSDate not supplied

Assessing the Influence of Decompression Sickness on Medical Risk for Artemis

As NASA begins to shift its focus from LEO missions to the Moon and beyond, our understanding of the risk associated with human spaceflight is challenged by mission profiles and objectives far different from preceding missions. For Artemis, one of these aspects is surface Extra Vehicular Activity (EVA) tempo, in which the crew will perform 3 to 4 surface EVA’s in quick succession. The crew health and performance risk associated with EVA Decompression Sickness (DCS) and its associated countermeasures, is an important consideration for Artemis missions given this new and unprecedented EVA frequency. Successfully completing Artemis mission objectives relies heavily on the crew being able to perform and complete these EVAs. This analysis quantifies how medical risk changes when accounting for the expected increase in DCS risk due to the increase in EVA frequency. If there is an occurrence of DCS during a mission, appropriate downtime needs to be accounted for. Treatment for DCS requires crewmember to stop the current EVA and return to the habitat. This impacts the current EVA the crew is performing, could potentially delay the next EVA, and increases the chance of the crewmember being unable to perform future EVAs. Capturing the downtime and impact of that downtime based on the number of DCS occurrences allows us to better understand how the risk associated with DCS affects the overall medical risk and completion of missions that have more frequent EVA tempo. There are preventative strategies and mitigation countermeasures to decrease the chance of DCS occurring such as different prebreathe protocols, variable suit pressure, and vehicle pressure settings. Modeling the medical risk with and without different DCS countermeasures enables quantitative comparisons of risk reduction associated with each DCS countermeasure. We present results on how these countermeasures affect medical risks and quantify changes in the medical kit contents with respect to the countermeasures implemented within Artemis-class missions.

Clara Gasiewski↗
NASA NTRS2018

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.

Castellanos, Patricia↗
NASA NTRSDate not supplied

Latest Development in Radiative Transfer Models and Retrieval Algorithms Using Principal Components

The radiative transfer model (RTMs) has a wide range of applications in satellite remote sensing and atmospheric radiation applications. However, millions of line-by-line (LBL) radiative transfer calculations at fine monochromatic frequencies are needed in order to properly calculate spectral contributions of water vapor and trace gases in the atmosphere in infrared and solar spectral regions. Therefore, fast and accurate RTMs are needed to efficiently process large amount of satellite data. A Principal Component-based Radiative Transfer Model (PCRTM) was first developed in 2004 at NASA Langley Research Centre to fulfil this need. By using PC-compression, one can reduce the data dimension significantly while maintaining original information content. The PCRTM can directly compute PC-scores and their derivatives with respect to retrieved parameters. The PCRTM can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra from 0.250 µm (400000 cm-1) to 2000 µm (50 cm-1) with several orders of magnitude faster speed as compared to a LBL RTM. It is also extremely accurate compared to LBL RTM benchmarks (0.03 K RMS error in IR and 0.05% in solar). The PCRTM model has been developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, FIRST, and CLARREO-IR in thermal IR spectral region and CLARREO-Solar, CPF, TEMPO, EMIT, OMI, and SCIAMACHY in solar spectral region. The PCRTM accuracy has been demonstrated via RTM intercomparisons and with real satellite observations from AIRS, CrIS, IASI, SCHIAMACHY, and EMIT etc. In this presentation, we will describe two PCRTM-based inversion algorithms to retrieve atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties from hyperspectral sounders such as AIRS, CrIS, IASI, and NAST-I. The first one is called Single Fieldof-view Sounder Atmospheric Product (SiFSAP) algorithm. It provides L2 products with 9-times higher area spatial resolution as compared to current cloud-clearing sounder algorithms. The SiFSAP L2 and L3 products are available at NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The second inversion algorithm is called Climate Fingerprinting Atmospheric Product (ClimFiSP) algorithm. It is designed to produce high quality climate products (trends, anomalies, daily, and monthly profiles of temperature, water vapor, traces, clouds, and surface properties) from multiple satellite sensors such AIRS on Aqua and CrIS on multiple satellites. This product will be available at NASA GES DISC later this year. The PCRTMbased high fidelity simulators for CLARREO and CPF have been used for sensor performance trade studies, algorithm development, and inter-satellite calibrations. We have also used PCRTM generated TOA radiance spectra to train an AI-based algorithm and successfully retrieved cloud properties from EMIT solar hyperspectral imagers.

PCRTM↗
NASA NTRS2015

Spatial Variability of Trace Gases During DISCOVER-AQ: Planning for Geostationary Observations of Atmospheric Composition

Results from an in-depth analysis of trace gas variability in MD indicated that the variability in this region was large enough to be observable by a TEMPO-like instrument. The variability observed in MD is relatively similar to the other three campaigns with a few exceptions: CO variability in CA was much higher than in the other regions; HCHO variability in CA and CO was much lower; MD showed the lowest variability in NO2All model simulations do a reasonable job simulating O3 variability. For CO, the CACO simulations largely under over estimate the variability in the observations. The variability in HCHO is underestimated for every campaign. NO2 variability is slightly overestimated in MD, more so in CO. The TX simulation underestimates the variability in each trace gas. This is most likely due to missing emissions sources (C. Loughner, manuscript in preparation).Future Work: Where reasonable, we will use these model outputs to further explore the resolvability from space of these key trace gases using analyses of tropospheric column amounts relative to satellite precision requirements, similar to Follette-Cook et al. (2015).

trace gas↗
NASA NTRSDate not supplied

A Safety-Driven Approach to Exploring and Comparing Air Traffic Management Concepts for Enabling Urban Air Mobility

There is broad recognition that the high tempo and density of Urban Air Mobility (UAM) operations will require identifying new Air Traffic Management (ATM) concepts to safely integrate UAM air traffic into the airspace alongside existing air traffic. However, the simulation models used to compare ATM concepts today are difficult to apply during the early stages of concept development and do not offer enough support in identifying potential new concepts. In addition, they have limited ability to evaluate ATM concepts in terms of safety, security, and other key emergent properties. Instead of using simulation to evaluate ATM concepts, this paper demonstrates how a safety-driven systems engineering approach based on Systems-Theoretic Accident Model and Processes (STAMP) can be used to design properties such as safety into an ATM system from the earliest stages of development. Using a hazard analysis technique called Systems-Theoretic Process Analysis (STPA), system requirements and the desired ATM behavior are derived. As an example, two possible ATM concepts to implement that behavior are compared to identify their safety-related benefits and tradeoffs. This new approach enables (1) systematic exploration of alternative ATM concepts and (2) identification of the safety-related tradeoffs between concepts as early as possible in the development process.

system safety↗
NASA NTRSDate not supplied

Use of TEMPO as a Proxy for Hyperspectral Geostationary Ocean Color Measurements from the GeoXO OCX Instrument: Harnessing Machine Learning and Principal Component Techniques for Atmospheric and Glint Correction

Retrievals of ocean color from space are important for better understanding the ocean ecosystem. The launch of atmospheric geostationary hyperspectral sensors such as TEMPO, provides a unique opportunity to examine the diurnal variability in ocean ecology. While TEMPO does not have as high spatial resolution or full spectral coverage as planned coastal ocean sensors such as the Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) or GeoXO Ocean Color instrument (OCX), its hourly measurements provide coverage of regions such as Lake Erie and the Gulf of Mexico at spatial scales of approximately 5 km. These data can be useful for testing new algorithms. We will apply our newly developed machine learning based atmospheric correction approach for ocean color retrievals to TEMPO data. Our approach begins by decomposing measured radiances from hyperspectral sensors into spectral features that describe the scattering and absorption of the atmosphere as well as the underlying surface reflectance. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from collocated MODIS/VIIRS physically-based retrievals. This machine learning approach does not rely on radiative transfer modeling, and the use of MODIS/VIIRS data for training accounts for possible calibration b in hyperspectral data. Previously, we applied our approach using blue and UV wavelengths with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) to show that it can estimate ocean color properties in less-than-ideal conditions such as lightly to moderately clouded conditions as well as sun glint and thus improve the spatial coverage of ocean color measurements. TEMPO provides an opportunity to improve on this approach since it will provide collocated measurements at green and red wavelengths that were not available from OMI and TROPOMI and are important particularly for coastal waters. Additionally, our technique can be applied early in the mission and has potential to demonstrate the value of near real time ocean color products that are important for monitoring of harmful algae blooms and other oceanic phenomena.

Zachary Fasnacht↗
NASA NTRSDate not supplied

Synergistic TEMPO Air Quality Science (STAQS)

In June-August 2023, STAQS seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science and increase societal benefit. Study philosophy: - Build an integrated observing system consisting of ground-, airborne-, and satellite-based platforms and air quality models. - Prioritize repeated systematic sampling in a predefined domain during morning, midday, and afternoon times over at least 4 days in each primary target areas. - Assemble a collaborative research team through leveraging multiple activities occurring in summer 2023 with federal and academic partners.

Laura Margaret Judd↗
NASA NTRS2019

CNS Simulation Tool Development for Increasingly Complex Airspace Operation Evaluation

As unmanned aircraft operations continue to grow and new vehicles such as air taxi, supersonic aircraft and high-altitude long endurance aircraft seek access to the airspace, it is anticipated that National Airspace System (NAS) operations will increase in density and complexity. To manage the influx, demand and safe integration of these new aircraft and missions into the NAS, a careful evaluation of existing and new operational concepts, vehicle characteristics and technology performance is required. Communications, Navigation and Surveillance (CNS) technologies operating in the NAS have evolved to meet changing needs of the Air Traffic Control System and the vehicles operating in the airspace. Today's CNS systems have been architected to deliver critical services to a carefully designed airspace configuration and to serve airborne vehicles equipped with standardized systems that enable global air navigation. The introduction of new vehicles is projected to expand NAS operations beyond today's configuration to include a new breed of aircraft operating from non-conventional aerodromes, resulting in a mix of vehicles operating in the same airspace. To enable this expansion, it is anticipated that modeling and simulation will play an important role in the evaluation of new management concept of operations.In this regard, NASA is developing simulation capabilities that will enable the evaluation of new concepts of operation and CNS technology performance in an increasingly dense, high-tempo operational environment. The NASA Glenn Research Center is working on the development of CNS simulation capabilities intended to support the integrated evaluation of operational concepts and emerging technologies. This presentation describes simulation capability development efforts that, together with other simulation tools, will enable evaluation of new concepts and technologies for the integration of new vehicles and services into the NAS. [Also discussed: NASA Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART NAS)]

Surveillance↗
NASA NTRS2021

Concurrent Runtime Verification of Data Rich Events

This paper presents the open source runtime verification tool MESA (MEssage-based System Analysis), implemented in Scala, which supports concurrent monitors using the Actor model. Furthermore, the tool supports indexing (slicing) on the data values occurring in data-carrying events, for each individual monitor. The tool is generic in the sense that any monitoring system can be used for creating monitors. In this paper, we use the internal Scala DSL Daut for programming such in data parameterized state machines and temporal logic. To illustrate MESA/Daut, we present a case study that monitors flights from live U.S. airspace data streams, verifying that they conform to planned routes. With base in the case study, we then perform an extensive empirical study of the potential benefits from monitoring slices of a single property in concurrently executing actors. Due to the overhead of scheduling “small” actors (one for each slice or a small number of slices), it is not obvious that concurrent execution of such is beneficial. However, as a main result, we demonstrate that concurrent monitoring of slices to handle data-carrying events can provide considerable speed gains.

finite state machines↗
NASA NTRS2020

An Integrated Model of Models for Global Flood Alerting

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant damage to infrastructure and economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures. Situational awareness in near real-time is essential to ensure community resilience and enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions at regional to global scale. Given the global coverage of two operational flood models - GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the first component of this project first focuses on integrating the outputs from these two models to classify flood severity and send alerts based on potential for impacts similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes). The second component of the project focuses on using flood outputs derived from earth observation data to validate, update and add additional exposure and impact products to flood alerts. The flood impacts and severity information will be disseminated as alerts and maps through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. In this presentation, the following objectives will be covered: (i) the effective integration of the flood models, (ii) accuracy of the flood model outputs (flood extent and depth) for specific flood events both in the United States and globally in comparison with each other and earth observation data.

Glasscoe, Margaret↗
NASA NTRS1985

The Accuracy of Radio Interferometric Measurements of Earth Rotation

The accuracy of very long base interferometry earth rotation (UT1) measurements is examined by intercomparing TEMPO and POLARIS data for 1982 and the first half of 1983. None of these data are simultaneous, and so a proper intercomparison requires accounting for the scatter introduced by the rapid, unpredictable, UT1 variations driven by exchanges of angular momentum with the atmosphere. A statistical model of these variations, based on meteorological estimates of the Atmospheric Angular Momentum is derived, and the optimal linear (Kalman) smoother for this model is constructed. The scatter between smoothed and independent raw data is consistent with the residual formal errors, which do not depend upon the actual scatter of the UT1 data. This represents the first time that an accurate prediction of the scatter between UT1 data sets were possible.

Eubanks, T. M.↗
NASA NTRS2013

Prospecting for Polar Volatiles: Results from the Resolve Field

Both the Moon and Mercury evidently host ice and other volatile compounds in cold traps at the planets poles. Determining the form, spatial distribution, and abundance of these volatiles at the lunar poles can help us understand how and when they were delivered and emplaced. This bears directly on the delivery of water and prebiotic compounds to the inner planets over the solar system s history, and also informs plans for utilizing the volatiles as resources for sustained human exploration as well as the commercial development of space. Temperature models and orbital data suggest near-surface volatile concentrations may exist at polar locations not strictly in permanent shadow. Remote operation of a robotic lunar rover mission for the 7-10 days of available sunlight would permit key questions to be answered. But such a short, quick-tempo mission has unique challenges and requires a new concept of operations. Both science and rover operations decisionmaking must be done in real time, requiring immediate situational awareness, data analysis, and decision support tools.

Elphic, Richard C.↗
NASA NTRS1993

Earth Rotation Parameters from DSN VLBI: 1993

This year we have introduced several modeling improvements, including estimating a parametric model for the mearly-diurnal and nearly-semidiurnal tidal frequency variations of UTI and polar motion, and estimating site velocities.

earth rotation DSN VLBI IERS TEMPO↗
NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite NO2 Data Assimilation and Improvements in Background NOx Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

V. Shah↗
NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite Data Assimilation and Improvements in Background NO x Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NO x =NO+NO 2 ) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO 2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

Viral Shah↗
NASA NTRSDate not supplied

Principal Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Remote Sensors for UV, VVIS, NIR, IR, and FIR Spectral Regions

Fast and accurate radiative transfer models are needed to efficiently process satellite hyperspectral remote sensors. We have developed a Principal Component-based radiative transfer model (PCRTM) which can simulate TOA radiance or reflectance spectra in the cloudy atmosphere for far IR, IR, NIR, VIS, and UV spectral regions quickly and accurately. Multi-scattering of multiple layers of clouds/aerosols is included in the model. Polarization components can also be calculated. The computation speed is 3 to 4 orders of magnitude faster than MODTRAN5, LBLRTM, and VLIDORT. The PCRTM calculated radiance spectra agree with reference RTM calculated radiance spectra very well (0.03 K in IR and 0.05% in solar). Comparisons of the PCRTM model calculations with observed AIRS, CrIS, IASI, NAST-I, and SCIAMACHY data will be presented. The solar-PCRTM has been developed for CLARREO PathFinder (CPF) and has been extensively used for CPF science algorithms. The PCRTM can also be used for many current and future NASA future missions such as EMIT, SBG, TEMPO, and PACE.

pcrtm↗
NASA NTRSDate not supplied

Joint Assimilation of CO, O3, NO2, SO2, PM, AOD, NH4, PAN, and HNO3 in Support of the Tropospheric Regional Atmospheric Composition and Emissions Reanalysis (2005 - 2024) (TRACER-I)

NASA Ames Research Center is collaborating with the NOAA Chemical Systems Laboratory (NOAA/CSL), the NASA Jet Propulsion Laboratory (JPL), the NASA Goddard Modeling and Assimilation Office, and the National Center for Atmospheric Research to prepare a 20-year Tropospheric Regional Atmospheric Composition and Emissions Reanalysis (2005 – 2024) (TRACER-I) for the continental United States during the summer ozone (O3) and wildfire seasons (April to October). TRACER-I will be a regional complement to JPL’s global Tropospheric Chemistry Reanalyses II and III. We will use WRF-Chem/DART with NOAA/CSL’s WRF-Chem setup at 12 km x 12 km horizontal resolution, 51 vertical levels, and 30 ensemble members. We will assimilate: (i) conventional meteorological observations; (ii) EPA’s Air Quality System in situ measurements of carbon monoxide (CO), O3, nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM) with diameters less than 10 µm (PM10), and PM with diameters less than 2.5 µm (PM2.5); and (iii) MOPITT, MODIS, OMI, TROPOMI, GOME-2a, MLS, TES, SCIAMACHY, CrIS, and TEMPO total/partial column and/or profile retrievals of CO, O3, NO2, SO2, aerosol optical depth (AOD), formaldehyde (HCHO), ammonia (NH4), peroxyacetyl nitrate (PAN), and/or nitric acid (HNO3) with 3-hr cycling. We will present an overview of this project, its status, and available results (likely the analysis of sensitivity experiment results from the assimilation/emissions estimation system).

data assimilation↗