Probing Putative Biomarker Assignments in SEM Imagery from BRAILLE Investigations at Lava Beds National Monument (N. CA, USA) Using Machine Learning Tools
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The Far-Infrared Spectroscopy of the Troposphere (FIRST) instrument is a fourier transform spectrometer developed to measure the far-infrared portion of the Earth's longwave spectrum. Specifically, the purpose is measurement of the important, yet under-investigated 100 to 1000 cm-1 band. Presented here are measurements made by FIRST at two successful deployments in a ground-based configuration to measure downwelling longwave radiation. The initial deployment was to Cerro Toco, Chile. FIRST operated from August to October, 2009 as part of the Radiative Heating in Underexplored Bands Campaign (RHUBC-II) campaign. After recalibration, FIRST was deployed to the Table Mountain Facility from August to October, 2012. Dry days for both campaigns were chosen for analysis ? 09/24/2009 and 10/19/2012. Also available from both deployments are coincident radiosonde temperature and water vapor vertical profiles used as inputs to radiance calculations performed by two platforms, the AER Line-by-Line Radiative Transform Model (using the AER line database), and a research platform, MRTA utilizing HITRAN 2012. Thus, we have three streams of data for the closure studies, and results of the comparisons are presented showing good agreement in the optically thick regions of 200-400 cm-1 and 600-800 cm-1. Differences between measured and coincident calculated spectra between 400 and 600 cm-1 are less than 10 mW/m^2/sr/cm-1. Uncertainties accounted for in this study include uncertainty from multiple detectors, water vapor, temperature, line strength and width as presented in HITRAN 2012, and due to internal heating caused by black bodies. The residuals fall within the root-sum-square envelope of the uncertainties.
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An overview of phase 2 of the ATM-X project
The Arctic Carbon Atmospheric Profiles (Arctic-CAP) project conducted six airborne surveys of Alaska and northwestern Canada between April and November 2017 to capture the spatial and temporal gradients of northern high-latitude carbon dioxide (CO2), methane (CH4) and carbon monoxide (CO) as part of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4 and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. We observed vertical gradients of CO2, CH4 and CO that vary by eco-region and duration of the sampling period, which spanned the majority of the seasonal cycle. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons with GEOS simulations of atmospheric CO2, CH4 and CO highlight the potential of these multi-species observations to inform improvements in surface flux estimates and the representation of atmospheric transport. GEOS simulations provide estimates of the near surface average CO2 and CH4 enhancements that are well correlated with aircraft observations (R=0.74 and R=0.60 respectively), suggesting that GEOS has reasonable fidelity over this complex and heterogeneous region. This model-data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broadscale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are observed. The study also provides a framework for benchmarking a global model at regional scales, which is needed to use climate models as tools to investigate high-latitude carbon-climate feedbacks.
When a satellite is placed into its desired orbit, to achieve its designed mission objectives, it is imperative to maintain orbit requirements as well as the overall health and safety of the vehicle. One integral part of this process is ensuring that the satellite does not collide with any other vehicle or object in space, which can cause a detrimental consequence to the mission as well as to other satellites within the same orbit regime. To prevent such an occurrence, a thorough collision avoidance process must be followed. This process includes constantly monitoring potential conjunctions and performing collision avoidance maneuvers when necessary, while remaining within the limitations of the mission’s orbit maintenance requirements. This presentation will describe the Earth Observing System (EOS) Aqua, Aura and Terra satellite’s operational collision a! voidance process. It will highlight the routine conjunction assessment screening and data evaluation process performed by the NASA/GSFC EOS Collision Avoidance Engineers. The presentation will include background information, a summary of operational activities and duties, internal tools used for support, EOS High Interest Event statistics, and potential challenges expected in the future.
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Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wild fire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry, a Marin-based fire service work force development program, to develop new models to assist with fire management. Using data from Sentinel-2A, Planet Scope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), the team developed several input data layers to three models evaluating wild fire severity. One model performed a suitability analysis with weights based on scientific literature, another utilized machine learning based on past fires in Marin and neighboring Sonoma County to predict the difference normalized burn ratio, and the third inputted data layers into the Flam Map tool, which outputs risk categories. The team compared model outputs and, using the best-fit model, performed fuzzy logic analysis to identify specific locations where a fire break could be constructed to interrupt the progress of an active fire. These tools were proven useful and will assist partners in preparing for and managing an active wildfire event.
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