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NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Fe(exp3) - A Monte Carlo Simulation Capability for Evaluation of New Air Traffic Management Concepts

The concepts of unmanned aircraft system traffic management (UTM) and urban air mobility (UAM) introduce high-density operations in low-altitude airspace and will change the paradigm of the traditional air traffic system. The Flexible engine for Fast-time evaluation of Flight environments (Fe3) provides the capability of statistically analyzing high-density, high-fidelity, and low-altitude traffic system without conducting infeasible and cost-prohibitive flight tests that involve a large volume of aerial vehicles. With this simulation capability, stakeholders can study the impacts of critical factors, define requirements, policies, and protocols needed to support a safe yet efficient traffic system, assess operational risks, and optimize flight schedules. This work provides an introduction to this simulation tool including its architecture and various models involved. Its performance and applications in high density air traffic operations are also presented.

Paralellization↗
NASA NTRS2020

A Demonstration that Correcting for Completeness and Reliability Is Critical for Robust Occurrence Rates

A measurement of planetary occurrence rates based on a planet catalog should be robust against details of how initial detections were classified as planets or false positives. This is accomplished by supplying the catalog’s rate of missed planets (completeness) and rate of non-planets incorrectly called planets (reliability). The final Kepler data release (DR25) includes products that can be used with the DR25 planet candidate catalog to correct for completeness and reliability in occurrence rate estimates. This is made possible by the Kepler Robovetter, which algorithmically and uniformly selects planets based on a variety of metrics and thresholds. Completeness, reliability, and occurrence rates potentially depend on these Robovetter thresholds. We study the impact of varying these vetting thresholds using the techniques of Bryson et al. 2019 (arXiv:1906.03575). We explore sets of thresholds that result in more or fewer planets (trading off completeness for reliability), as well as thresholds tuned to pass DR25 false positives identified as possible planets by the Kepler False Positive Working Group. We find that when correcting only for completeness, and not reliability, the resulting occurrence rates have a strong dependence on these threshold sets. For example, the value of SAG13 eta-Earth varies by over a factor of 4 when not corrected for reliability. However, when correcting for both completeness and reliability, occurrence rates using our threshold sets are statistically indistinguishable, with differences being well inside 1-sigma error bars. We present occurrence rates integrated over several period-radius ranges. For example, SAG13 eta-Earth is consistent with 0.127 (+0.094)(-0.054) (from Bryson et al. 2019) for all the Robovetter threshold sets. This result emphasizes the importance of correcting occurrence rates for both completeness and reliability. This suggests that inconsistent completeness and reliability correction may be a significant contributor to the large variation of occurrence rates in recent literature. We plan to make the Robovetter results for our threshold sets available, and encourage the community to use them to examine whether other occurrence rate methods yield similarly robust results.

Bryson, S.↗
NASA NTRS2019

Observation of Absorbing Aerosols Above Clouds over the South-East Atlantic Ocean from the Geostationary Satellite SEVIRI – Part 1: Method Description and Sensitivity

High temporal resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the South East Atlantic Ocean (SEAO). This paper presents a method developed to retrieve simultaneously aerosol and cloud properties in aerosol above cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud Aerosol Optical Thickness (AOT), the Cloud Optical Thickness (COT) and the Cloud droplet Effective Radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10% error on the humidity profile leads to an 18.5% bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00-15:00 UTC, an uncertainty of 40% is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17% between 09:00-15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6% on the COT and 3% on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time-series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above cloud events over the SEAO.

Peers, Fanny↗
NASA NTRS2019

Observation of absorbing aerosols above clouds over the south-east Atlantic Ocean from the geostationary satellite SEVIRI – Part 1: Method description and sensitivity

High-temporal-resolution observations from satellites have a great potential for studying the impact of biomass burning aerosols and clouds over the south-east Atlantic Ocean (SEAO). This paper presents a method developed to simultaneously retrieve aerosol and cloud properties in aerosol above-cloud conditions from the geostationary instrument Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). The above-cloud aerosol optical thickness (AOT), the cloud optical thickness (COT) and the cloud droplet effective radius (CER) are derived from the spectral contrast and the magnitude of the signal measured in three channels in the visible to shortwave infrared region. The impact of the absorption from atmospheric gases on the satellite signal is corrected by applying transmittances calculated using the water vapour profiles from a Met Office forecast model. The sensitivity analysis shows that a 10 % error on the humidity profile leads to an 18.5 % bias on the above-cloud AOT, which highlights the importance of an accurate atmospheric correction scheme. In situ measurements from the CLARIFY-2017 airborne field campaign are used to constrain the aerosol size distribution and refractive index that is assumed for the aforementioned retrieval algorithm. The sensitivities in the retrieved AOT, COT and CER to the aerosol model assumptions are assessed. Between 09:00 and 15:00 UTC, an uncertainty of 40 % is estimated on the above-cloud AOT, which is dominated by the sensitivity of the retrieval to the single-scattering albedo. The absorption AOT is less sensitive to the aerosol assumptions with an uncertainty generally lower than 17 % between 09:00 and 15:00 UTC. Outside of that time range, as the scattering angle decreases, the sensitivity of the AOT and the absorption AOT to the aerosol model increases. The retrieved cloud properties are only weakly sensitive to the aerosol model assumptions throughout the day, with biases lower than 6 % on the COT and 3 % on the CER. The stability of the retrieval over time is analysed. For observations outside of the backscattering glory region, the time series of the aerosol and cloud properties are physically consistent, which confirms the ability of the retrieval to monitor the temporal evolution of aerosol above-cloud events over the SEAO.

cloud properties↗
NASA NTRS2022

Mexico Disasters: Comparing Feasibility of Flood Detection Methods Using Google Earth Engine and Open Data Cube for Flood Mitigation in Mexico

In coordination with out partner's, we identified three case studies for the application of the flood detection algorithms. The first case study analyzes the impacts of Hurricane Sally on Villahermosa, Tabasco resulting in urban flooding and impacting adjacent cropland. The second case study investigates floods in Salto de Agua, Chiapas caused by Hurricane Eta, where the Xanil River level exceeded 14 meters, pouring into the surrounding villages. Finally, we look further north to Tula de Allende, Hidalgo during Hurricane Ida, where floods collapsed the power supply citywide and caused numerous deaths.

John Willis↗
NASA NTRSDate not supplied

IMPACT - Information for the Development of Human Health and Performance Systems

IMPACT is a suite of computational and systems engineering tools. Currently being developed for NASA Exploration Medical Capabilities (ExMC) and could be extended to other Human Research Program (HRP) elements. The purpose is to provide a data-driven means to inform human health and performance risk mitigation during exploration missions considering resource constraints in the medical system. IMPACT enables systematic trade studies to evaluate options. IMPACT uses Probabilistic Risk Assessment (PRA) as a systematic methodology to evaluate risks. IMPACT is currently under development to inform decision-makers the risks involved with trade-options that have likelihoods and consequences backed by scientific research in a medical evidence library. IMPACT supports multi-segment analysis to determine probabilities and risks in different mission segments. There are currently 122 medical conditions and 900 resources in the IMPACT Medical Database. IMPACT modeling is using mission segments and multiple vehicles for the NASA Artemis campaign and could support future complex missions in deep space.

IMPACT PRA↗
NASA NTRSDate not supplied

First Optical Constants from 0.4 to 1.6 µm of Titan Aerosol Analogs Produced in the NASA Ames COSmIC Facility and Their Use in a New Analysis of Cassini VIMS Observations

We have determined the real and imaginary refractive indices (n and k, respectively), from the visible to the near infrared (0.4 to 1.6 μm), of five laboratory-generated organic refractory materials produced from gas-phase chemistry with the NASA Ames COSmIC facility. The solid samples were produced using a plasma discharge in the stream of a 200-K supersonic jet-cooled expansion of different gas mixtures to study the impact of the molecular precursors on the solid sample optical properties. Three samples were produced from N2:CH4 (95:5) gas mixtures using three different high voltages (700V, 800V and 1000V) to vary the energy in the plasma discharge. One sample was produced from a N2:CH4:C2H2 (94:5:5:0.5) gas mixture, with a high voltage of 1000 V. The fifth sample was produced in an Ar:CH4 (95:5) gas mixture with a high voltage of 1000 V to produce a nitrogen-free hydrocarbon sample. The optical constants, n and k, of these five samples were determined using spectral reflectance measurements. They appear to be positively correlated with the nitrogen content in the solid sample, i.e., a sample with larger nitrogen content exhibits higher n and k values. We have used these refractive indices as input parameters in a radiative transfer model to analyze Cassini Visible Infrared Mapping Spectrometer (VIMS) observations of Titan’s atmosphere. The results show that using the tholin samples with higher n and k values (higher nitrogen content) provides a better fit to the observational data than using the samples with lower n and k values (lower nitrogen content). The Titan tholins with higher nitrogen content therefore appear to be more representative of the Titan aerosols observed by VIMS.

Titan Aerosol Analogs↗
NASA NTRSDate not supplied

TC3 Overview

This presentation gives an overview of the technology, methods and processes developed during the TC3 Technical Challenge of the System-Wide Safety (SWS) project. The presentation goes over the motivation for doing this research, gives an overview of the tool, methods and process developed under this program, and describes case studies and impact in industry.

Software assurance↗
NASA NTRSDate not supplied

Toward Enhancing the Use of IASI and CrIS Surface-Sensitive Radiances Over Land in the NASA GMAO GEOS Data Assimilation Framework

Assimilating surface-sensitive radiances over land is still challenging for both infrared (IR) and microwave (WV) radiances essentially because of the large uncertainties of the land physical surface emissivity model used in the Community Radiative Transfer Model (CRTM) and the uncertainties of land surface state properties. Currently very few IR radiances are assimilated over land. Large number of IR radiances are rejected by the surface sensitivity checks as well as the cloud detection check. In this study, we identified the appropriate Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) surface-sensitive channels to retrieve Land Surface Temperature (LST). Then, we studied the impacts of these retrieved LST and retuned cloud detection on the simulation and assimilation of IASI and CrIS in the NASA GEOS in clear sky conditions. The preliminary results are shown to enhance the rate of IASI and CrIS assimilated channels over land. The impacts on the quality of the resulting analysis and subsequent forecast will be presented at the meeting.

Niama Boukachaba↗
NASA NTRSDate not supplied

Fe3 Traffic Simulator Overview

NASA Ames developed a first of its kind air traffic simulation tool known as Flexible engine for Fast time evaluation of Flight environments (Fe3). The patent-pending technology is a robust simulation tool that provides the capability of statistically analyzing the high-density and low-altitude traffic system. With this capability, stakeholders can study the impacts of critical components (such as wind, surveillance, communication, collision, avoidance, traffic rules, energy consumption, etc.) in the low-altitude high-density traffic system, gain insights and help define requirements, policies, and protocols for a safe and efficient traffic system, and assess operational risks and optimize fight schedules.

Flexible engine for Fast time evaluation of Flight↗
NASA NTRSDate not supplied

Deriving representative size distributions from UW OPC data for SAGE II extinction measurements

For the purposes of studying the impacts of aerosols on the chemistry and climate of the atmosphere, size distribution information has been inferred from extinction measurements for decades. Using the measurements from the University of Wyoming balloon-borne optical particle counter experiment we have derived representative size distributions at SAGE II channel wavelengths. These are bimodal lognormal size distributions grouped into bins of 525 to 1020 nm extinction ratio. Future work will include expanding this analysis to SAGE III’s channels and considering the effect of a absorbing channel in the IR would have.

Aerosol↗
NASA NTRSDate not supplied

Effect of Space Radiation on TES Detectors Performance

The Athena mission and its X-IFU instrument (X-ray Integral Field Unit) will be positioned at the Sun-Earth Lagrangian point L1, where it will be subject to solar wind (low flux plasma of 95% protons and 5% alpha particles) with energy below 0.1 MeV, and to galactic cosmic rays and solar flares (energetic protons, alpha particles, and electrons) with energies up to hundreds of MeV for protons and the GeV for heavier ions. Some of these particles will go through the satellite and hit the focal plane assembly and hence the detectors. These detectors will be TES (Transition-edge sensor) microcalorimeters, flown for the first time in such an environment. In order to ensure the performance of this type of detectors throughout the duration of such mission, it is critical to study the impact of the radiation on their behavior. Indeed, although a lot of reference material exist for semiconductor detectors such as CCDs, little is currently known about the impact of radiation on TES detectors. These energetic events could cause local heating or damage to the detectors and affect their performance. In this work, we describe how we designed a test campaign to assess the impact of L1 radiation on TES detectors for Athena/X-IFU-like missions and present the results of the tests. Analyses includes assessing changes in the pulse shapes and energy resolution of the detectors measured at 55 mK after several radiation dose steps performed at 4 K.

Sophie Beaumont↗
NASA NTRS2022

The 21-cm Signal From the Cosmic Dawn: Metallicity Dependence of High Mass X-Ray Binaries

X-rays from high-mass X-ray binaries (HMXBs) are likely the main source of heating of the intergalactic medium (IGM) during Cosmic Dawn (CD), before the completion of reionization. This Epoch of Heating (EoH; z ∼10–15) should soon be detected via the redshifted 21-cm line from neutral hydrogen, allowing us to indirectly study the properties of HMXBs in the unseen, first galaxies. Low-redshift observations, as well as theoretical models, imply that the integrated X-ray luminosity to star formation rate of HMXBs (L_X/SFR) should increase in metal-poor environments, typical of early galaxies. Here, we study the impact of the metallicity (Z) dependence of L_X/SFR during the EoH. For our fiducial models, galaxies with star formation rates of order 10^(−3)–10^(−1)M_⊙ yr^(−1) and metallicities of order 10^(−3) –10^(−2) Z_⊙ are the dominant contributors to the X-ray background (XRB) during this period. Different L_X/SFR–Z relations result in factors of ∼ 3 differences in these ranges, as well as in the mean IGM temperature and the large-scale 21-cm power, at a given redshift. We compute mock 21-cm observations adopting as a baseline a 1000 h integration with the upcoming Square Kilometer Array (SKA) for two different L_X/SFR–Z relations. We perform inference on these mock observations using the common simplification of a constant L_X/SFR, finding that constant L_X/SFR models can recover the IGM evolution of the more complicated L_X/SFR–Z simulations only during the EoH. At z < 10, where the typical galaxies are more polluted, constant L_X/SFR models overpredict the XRB and its relative contribution to the early stages of the reionization.

galaxies: high-redshift↗
NASA NTRS2022

NASA's Next Generation of Atmospheric Data Science

The Multi-Angle Imager for Aerosols (MAIA) and the Tropospheric Emission: Monitoring of Pollution(TEMPO) are NASA’s next-generation satellite missions for air quality monitoring. These missions will produce high-quality, high-resolution air quality data to support cross-displinary research. The MAIA mission is collaborating with health science researchers and epidemiologists to study the impacts of air quality on health outcomes. TEMPO aims to improve our understanding of tropospheric air pollution chemistry and our ability to make predictions about air quality and climate forcing. TEMPO will offer hourly measurements of tropospheric ozone, aerosols, and clouds focused on North America at high-spatial resolution, while MAIA will produce high-resolution measurements of speciated particulate matter targeting densely populated cities around the globe. Data from these missions will help improve our understanding of the sources, types, and interactions among the aerosols and trace gases that are polluting Earth’s atmosphere, as well as our understanding of the impact of air pollution on pollution on a wide range of important areas including human health, agriculture, weather, and climate change. The challenges of cross-disciplinary research, computationally expensive multi-variate analyses, and high-resolution data at both local and global scales are driving substantial changes across all of NASA’s Distributed Active Archive Centers (DAACs). High resolution data at scales such these requires a new approach to data ingest, archive, and publication. Like other NASA DAACs, the Atmospheric Science Data Center (ASDC), the DAAC that will be responsible for publishing MAIA and TEMPO data products has historically archived and distributed data on premise. DAACs of the future will archive and distribute data in the cloud, enabling them to remake themselves as research-focused data centers that will support on-demand, data-intensive computations for highly accurate retrospective analyses and predictions. Under the new paradigm, data formats and metadata must support on-demand spatial and temporal sub-setting, as well as other data transformation services such as re-gridding and re-sampling. This presentation will discuss work being done to address data formatting and metadata requirements in this dynamic new environment. In addition to the changes in data stewardship practices at the ASDC, the increased focus on supporting scientific research is driving changes in the relationship between DAACs and researchers. While the ASDC will continue to provide first rate data management and stewardship, it is increasingly focused on serving as a partner not only to the science teams that gather and produce the data it publishes, but to the researchers that use that data.

Beth Huffer↗
NASA NTRS2022

Large Interferometer for Exoplanets (Life) I. Improved Exoplanet Detection Yield Estimates for A Large Mid-Infrared Space-Interferometer Mission

Context. One of the long-term goals of exoplanet science is the atmospheric characterization of dozens of small exoplanets in order to understand their diversity and search for habitable worlds and potential biosignatures. Achieving this goal requires a space mission of sufficient scale that can spatially separate the signals from exoplanets and their host stars and thus directly scrutinize the exoplanets and their atmospheres. Aims. We seek to quantify the exoplanet detection performance of a space-based mid-infrared (MIR) nulling interferometer that measures the thermal emission of exoplanets. We study the impact of various parameters and compare the performance with that of large single-aperture mission concepts detecting exoplanets in reflected light. Methods. We have developed an instrument simulator that considers all major astrophysical noise sources and coupled it with Monte Carlo of a synthetic exoplanet population around main-sequence stars within 20 pc. This allows us to quantify the number (and types) of exoplanets that our mission concept could detect over a certain time period. Two different scenarios to distribute the observing time among the stellar targets are discussed and different apertures sizes and wavelength ranges are considered. Results. An interferometer consisting of four 2 m apertures working in the 4–18.5 μ.m wavelength range with a total instrument throughput of 5% could detect up to ≈550 exoplanets with radii between 0.5 and 6 R ⊕ with an integrated S/N ≥ 7. At least ≈160 of the detected exoplanets have radii ≤1.5 R ⊕ . Depending on the observing scenario, ≈25–45 rocky exoplanets (objects with radii between 0.5 and 1.5 R ⊕ ) orbiting within the empirical habitable zone (eHZ) of their host stars are among the detections. With four 3.5 m apertures, the total number of detections can increase to up to ≈770, including ≈60–80 rocky eHZ planets. With four times 1 m apertures, the maximum detection yield is ≈315 exoplanets, including ≤20 rocky eHZ planets. The vast majority of small, temperate exoplanets are detected around M dwarfs. The impact of changing the wavelength range to 3–20 μm or 6–17 μm on the detection yield is negligible. Conclusions. A large space-based MIR nulling interferometer will be able to directly detect hundreds of small, nearby exoplanets, tens of which would be habitable world candidates. In terms of predicted detection yield, such a mission can compete with large single-aperture reflected light missions. Given that we assumed only 2.5 years for the search phase, a significant sub-set of the detected exoplanets can be followed-up in a second mission phase to obtain high SNR thermal emission spectra, leveraging the superior diagnostic power of the MIR wavelength regime compared to shorter wavelengths.

planets and satellites: terrestrial planets↗
NASA NTRS2022

SMAP Soil Moisture Assimilation To Enhance Streamflow Estimates Across South Asia

Streamflow estimation across areas facing water scarcity is important considering the evolving climatic conditions. In this study, the impact of assimilating Soil Moisture Active Passive (SMAP) soil moisture retrievals on the modeled streamflow across three main river basins in South Asia is explored. Model estimated runoff was hydraulically routed using the HYMAP routing model within the NASA Land Information System (LIS) framework. Streamflow estimates via soil moisture assimilation highlighted the potential improvements in streamflow across low-flow river tributaries in irrigated regions. Limited relative change was noted along high-flow river tributaries. Modeled streamflow tended to underestimate the flow magnitude at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operation. Improvement of the modeling systems representativeness of the ground conditions by the inclusion of water management information would potentially improve the current streamflow modeling capability across South Asia.

Jawairia Ahmad↗