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NASA NTRS2016

Monitoring Orbital Precession of EO-1 Hyperion With Three Atmospheric Correction Models in the Libya-4 PICS

Spaceborne spectrometers require spectral-temporal stability characterization to aid in validation of derived data products. Earth Observation 1 (EO-1) began orbital precession in 2011 after exhausting onboard fuel resources. In the Libya-4 pseudo-invariant calibration site (PICS), this resulted in a progressive shift from a mean local equatorial crossing time of approximately10:00 A.M. in 2011 to approximately 8:30 A.M. in late 2015. Here, we studied precession impacts to Hyperion surface reflectance products using three atmospheric correction approaches from 2004 to 2015. Combined difference estimates of surface reflectance were less than 5 percent in the visible near infrared (VNIR) and less than 10 percent for most of the shortwave infrared (SWIR). Combined coefficient of variation estimates in the VNIR ranged from 0.025 to 0.095, and in the SWIR it ranged from 0.025 to 0.06, excluding bands near atmospheric absorption features. Reflectances produced with different atmospheric models were correlated (R squared) in VNIR from 0.25 to 0.94 and in SWIR from 0.12 to 0.88 (p value (calculated probability) less than 0.01). The uncertainties in all the models increased with a terrain slope up to 15 degrees and selecting dune flats could reduce errors. We conclude that these data remain a valuable resource over this period for sensor intercalibration despite orbital decay.

Neigh, Christopher S. R.↗
NASA NTRS2015

High-Contrast Coronagraph Performance in the Presence of DM Actuator Defects

Deformable Mirrors (DMs) are critical elements in high contrast coronagraphs, requiring precision and stability measured in picometers to enable detection of Earth-like exoplanets. Occasionally DM actuators or their associated cables or electronics fail, requiring a wavefront control algorithm to compensate for actuators that may be displaced from their neighbors by hundreds of nanometers. We have carried out experiments on our High-Contrast Imaging Testbed (HCIT) to study the impact of failed actuators in partial fulfillment of the Terrestrial Planet Finder Coronagraph optical model validation milestone. We show that the wavefront control algorithm adapts to several broken actuators and maintains dark-hole contrast in broadband light.

adaptive optics↗
NASA NTRS2016

Coupled Aerosol-Cloud Systems over Northern Vietnam during 7-SEAS BASELInE: A Radar and Modeling Perspective

The 2013 7-SEASBASELInE campaign over northern Southeast Asia (SEA) provided, for the first time ever, comprehensive ground-based W-band radar measurements of the low-level stratocumulus (Sc) systems that often exist during the spring over northern Vietnam in the presence of biomass-burning aerosols. Although spatially limited, ground-based remote sensing observations are generally free of the surface contamination and signal attenuation effects that often hinder space-borne measurements of these low-level cloud systems. Such observations permit detailed measurements of structures and lifecycles of these clouds as part of a broader effort to study potential impacts of these coupled aerosol-cloud systems on local and regional weather and air quality. Introductory analyses of the W-band radar data show these Sc systems generally follow a diurnal cycle, with peak occurrences during the nighttime and early morning hours, often accompanied by light precipitation. Preliminary results from idealized simulations of Sc development over land based on the observations reveal the familiar response of increased numbers and smaller sizes of cloud droplets, along with suppressed drizzle formation, as aerosol concentrations increase. Slight reductions in simulated W-band reflectivity values also are seen with increasing aerosol concentrations and result primarily from decreased droplet sizes. As precipitation can play a large role in removing aerosol from the atmosphere, and thereby improving air quality locally, quantifying feedbacks between aerosols and cloud systems over this region are essential, particularly given the negative impacts of biomass burning on human health in SEA. Such an endeavor should involve improved modeling capabilities along with comprehensive measurements of time-dependent aerosol and cloud profiles.

aerosol cloud interactions↗
NASA NTRS2016

Production of Peroxy Nitrates in Boreal Biomass Burning Plumes over Canada During the BORTAS Campaign

The observations collected during the BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) campaign in summer 2011 over Canada are analysed to study the impact of forest fire emissions on the formation of ozone (O3 and total peroxy nitrates (sigma)PNs, (sigma)ROONO2. The suite of measurements on board the BAe-146 aircraft, deployed in this campaign, allows us to calculate the production of O3 and of (sigma)PNs, a long-lived NOx reservoir whose concentration is supposed to be impacted by biomass burning emissions.In fire plumes, profiles of carbon monoxide (CO), which is a well-established tracer of pyrogenic emission, show concentration enhancements that are in strong correspondence with a significant increase of concentrations of (sigma)PNs, where as minimal increase of the concentrations of O3 and NO2 is observed. The (sigma)PN and O3 productions have been calculated using the rate constants of the first- and second-order react Pions of volatile organic compound (VOC) oxidation. The (sigma)PN and O3 productions have also been quantified by 0-D model simulation based on the Master Chemical Mechanism. Both methods show that in fire plumes the average production of (sigma)PNs and O3 are greater than in the background plumes, but the increase of (sigma)PN production is more pronounced than the O3 production. The average (sigma)PN production in fire plumes is from 7 to 12 times greater than in the background, whereas the average O3 production in fire plumes is from 2 to 5 times greater than in the background. These results suggest that, at least for boreal forest fires and for the measurements recorded during the BORTAS campaign,fire emissions impact both the oxidized NOy and O3;but (1)(sigma)PN production is amplified significantly more thanO3 production and (2) in the forest fire plumes the ratio between the O3 production and the (sigma)PN production is lower than the ratio evaluated in the background air masses, thus confirming that the role played by the (sigma)PNs produced during biomass burning is significant in the O3 budget. The implication of these observations is that fire emissions in some cases, for example boreal forest fires and in the conditions reported here, may influence more long-lived precursors of O3than short-lived pollutants, which in turn can be transported and eventually diluted in a wide area.

biomass burning↗
NASA NTRS2017

Materials Science Corrosion Internship Overview

The overarching purpose of this semester-long internship at the National Aeronautics and Space Administration's (NASA) John F. Kennedy Space Center (KSC) was to study the impacts of corrosion regarding the development of ground support equipment (GSE), launch support structures, and flight hardware in aid of the Space Launch System (SLS) and Orion program. Environmental corrosion is known as one of the largest material degradation issues for any structure comprised of metal on the Space Coast and requires the initiation of special protective measures for proper corrosion mitigation. The four (4) projects to be described were conducted with NASA Civil Servants and NASA Contractors who provided necessary resources to ensure the highest quality of data acquisition and knowledge transfer. The main objective shared between all four (4) of the following projects was to assess the modes by which corrosion takes place in multiple environments as well as the methods by which these modes of corrosion can be reduced for the enhancement of the mechanical, structural, and chemical compatibility of those respective environments with different candidate metals. A multitude of test methods were performed over the duration of this internship to assess the corrosive properties of these different candidate metals.

Ground Systems Development and Operations↗
NASA NTRS2018

Closed Loop Guidance Trade Study for Space Launch System Block-1B Vehicle

NASA is currently building the Space Launch System (SLS) Block-1 launch vehicle for the Exploration Mission 1 (EM-1) test flight. The design of the next evolution of SLS, Block-1B, is well underway. The Block-1B vehicle is more capable overall than Block-1; however, the relatively low thrust-to-weight ratio of the Exploration Upper Stage (EUS) presents a challenge to the Powered Explicit Guidance (PEG) algorithm used by Block-1. To handle the long burn durations (on the order of 1000 seconds) of EUS missions, two algorithms were examined. An alternative algorithm, OPGUID, was introduced, while modifications were made to PEG. A trade study was conducted to select the guidance algorithm for future SLS vehicles. The chosen algorithm needs to support a wide variety of mission operations: ascent burns to LEO, apogee raise burns, trans-lunar injection burns, hyperbolic Earth departure burns, and contingency disposal burns using the Reaction Control System (RCS). Additionally, the algorithm must be able to respond to a single engine failure scenario. Each algorithm was scored based on pre-selected criteria, including insertion accuracy, algorithmic complexity and robustness, extensibility for potential future missions, and flight heritage. Monte Carlo analysis was used to select the final algorithm. This paper covers the design criteria, approach, and results of this trade study, showing impacts and considerations when adapting launch vehicle guidance algorithms to a broader breadth of in-space operations.

Von der Porten, Paul↗
NASA NTRS2018

Using an Automated Air Traffic Simulation Capability for a Parametric Study in Traffic Flow Management

Flight delays occur when demand for capacity-constrained airspace or airports exceeds predicted capacity. Demand for capacity-constrained airspace or airports can be controlled by a series of Traffic Management Initiatives (TMIs), which use departure and airborne delays, as well as pre-departure and airborne reroutes, to manage access to the constrained resources. Two systems exist in current and planned future operations to address imbalances between demand and capacity. The Collaborative Trajectory Options Program (CTOP) reduces demand to constrained resources by assigning strategic departure delay and pre-departure reroutes. Reroutes are selected from Trajectory Options Sets (TOSs) submitted by airlines. As flights approach the constrained resource, the Time-Based Flow Management System (TBFM) is used to assign tactical delay to satisfy constraints. This paper describes experiments performed to study the impact of varying levels of airline participation in CTOP via submission of TOSs on ground delay and flight time, and the impact of departure uncertainty on TBFM delays. Results suggest that as CTOP participation increases, average ground delays decrease for all airlines, but to the greatest extent for airlines participating in CTOP. A threshold in CTOP participation, which varies with the constraint capacity, is identified beyond which there is relatively little further reduction in average ground delays. Similarly, given the likely level of CTOP participation, the capacity reduction for which CTOP would be an appropriate TMI is also identified. Results also suggest that high average departure errors and high variability in departure error can make the prioritization of TBFM internal departures in TBFM metering and scheduling infeasible. Departure errors at current levels are, however, acceptable.

CTOP↗
NASA NTRS2018

Using an Automated Air Traffic Simulation Capability for a Parametric Study in Traffic Flow Management

Flight delays occur when demand for capacity-constrained airspace or airports exceeds predicted capacity. Demand for capacity-constrained airspace or airports can be controlled by a series of Traffic Management Initiatives (TMIs), which use departure and airborne delays, as well as pre-departure and airborne reroutes, to manage access to the constrained resources. Two systems exist in current and planned future operations to address imbalances between demand and capacity. The Collaborative Trajectory Options Program (CTOP) reduces demand to constrained resources by assigning strategic departure delay and pre-departure reroutes. Reroutes are selected from Trajectory Options Sets (TOSs) submitted by airlines. As flights approach the constrained resource, the Time-Based Flow Management System (TBFM) is used to assign tactical delay to satisfy constraints. This paper describes experiments performed to study the impact of varying levels of airline participation in CTOP via submission of TOSs on ground delay and flight time, and the impact of departure uncertainty on TBFM delays. Results suggest that as CTOP participation increases, average ground delays decrease for all airlines, but to the greatest extent for airlines participating in CTOP. A threshold in CTOP participation, which varies with the constraint capacity, is identified beyond which there is relatively little further reduction in average ground delays. Similarly, given the likely level of CTOP participation, the capacity reduction for which CTOP would be an appropriate TMI is also identified. Results also suggest that high average departure errors and high variability in departure error can make the prioritization of TBFM internal departures in TBFM metering and scheduling infeasible. Departure errors at current levels are, however, acceptable.

integrated demand management↗
NASA NTRS2019

Global Assimilation of Loon Stratospheric Balloon Observations

Accurate analyses of stratospheric winds are important for determining realistic constituent transport and providing improved diagnostic studies and forecasts of the stratosphere. This study examines impacts on global meteorological analyses resulting from using winds derived from Loon superpressure balloons in the lower stratosphere (hereafter Loon winds) as additional input observations to the Goddard Earth Observing System (GEOS) data assimilation system. To fully investigate the impacts of assimilating the Loon winds, two steps are taken: (1) comparison of the GEOS analysis winds with Loon winds (Control experiment) and (2) examination of the impacts of assimilating the Loon winds into the GEOS data assimilation system (Loon experiment). The time period selected is June–August 2014 when over 150 Loon balloons were launched, mainly in the Southern Hemisphere. In the middle latitudes, the Loon winds and Control winds agree well (Loon balloon zonal wind observation minus forecast, O − F, root‐mean‐square (RMS) values of ~2.75 m/s) and assimilating the Loon winds has a small impact (O − F RMS values unchanged). In the tropics, the Loon observations and Control analysis winds differ more than in middle latitudes (zonal wind O − F RMS ~3.75 m/s) and assimilating the Loon winds improves the zonal wind O − F RMS by ~1 m/s. In selected cases where the Loon observations and Control analysis differ greatly (O − F RMS values greater than 10 m/s), assimilating Loon winds significantly decreases the zonal wind O − F RMS by 5 m/s. These decreases in O − F RMS values show that the 6‐hr forecasts are improved at the Loon balloon observation locations. While satellites routinely measure Earth's global temperatures from space, in situ wind measurements are relatively scarce, consisting mainly of a network of ground‐based weather balloons. Since only a limited number of the weather balloons even reach stratospheric altitudes, there is a need for additional stratospheric observations. One option for addition wind information is the use of experimental superpressure balloons (SPBs). SPBs can remain in the stratosphere for many months traveling with the winds and hence, by reporting their changing positions, providing in situ wind observations. These SPBs experiments are generally limited in terms of time period and number of balloons. However, over the past few years Loon has been launching hundreds of SPBs (hereafter called Loon balloons) providing a more consistent set of observations of wind in the stratosphere. Here we assimilate some of the Loon balloon winds into the NASA GEOS global data assimilation system. The data assimilation system combines all observations with an atmospheric model to produce global analysis of winds and temperatures. Results show that including the Loon balloon wind information noticeably affects the global analysis as well as forecasts with the largest impact in the tropics. These results provide direction to future model and analysis improvements.

L. Coy↗
NASA NTRS2019

Atmospheric Chemistry Modeling 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

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↗