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DOE OSTI2025

PAVC: The foundation for a Pan-Arctic Vegetation Cover database

Field-measured Arctic vegetation cover data is essential for creating accurate, high-quality vegetation structure and composition maps. Extrapolating field data into high-resolution cover maps provides detailed, function-specific information for use in Earth System Models, vegetation classifications, and monitoring vegetation change over time and space. However, field campaigns that collect plant cover vary substantially in scope, method, and purpose, which makes them difficult to unify across data stores, and they are often not designed to meet remote sensing needs. In this work, we synthesized and harmonized field-based fractional cover data from various data stores to create a high-quality, consistent repository schema for remote sensing-based vegetation cover mapping applications. We developed a reproducible workflow for synthesizing visual estimate and point-intercept fractional cover data. The resultant Pan-Arctic Vegetation Cover (PAVC) database contains synthesized fractional cover at both the species and plant functional type levels. The latter includes absolute foliar cover for deciduous shrubs and trees, evergreen shrubs and trees, forbs, graminoids, lichen, bryophytes, and “other” vegetation, as well as absolute cover for litter and top cover for water and bare ground.

Steckler, Morgan R. [Oak Ridge National Laboratory↗

NASA NTRS2016

Station Program Note Pull Automation

Upon commencement of my internship, I was in charge of maintaining the CoFR (Certificate of Flight Readiness) Tool. The tool acquires data from existing Excel workbooks on NASA's and Boeing's databases to create a new spreadsheet listing out all the potential safety concerns for upcoming flights and software transitions. Since the application was written in Visual Basic, I had to learn a new programming language and prepare to handle any malfunctions within the program. Shortly afterwards, I was given the assignment to automate the Station Program Note (SPN) Pull process. I developed an application, in Python, that generated a GUI (Graphical User Interface) that will be used by the International Space Station Safety & Mission Assurance team here at Johnson Space Center. The application will allow its users to download online files with the click of a button, import SPN's based on three different pulls, instantly manipulate and filter spreadsheets, and compare the three sources to determine which active SPN's (Station Program Notes) must be reviewed for any upcoming flights, missions, and/or software transitions. Initially, to perform the NASA SPN pull (one of three), I had created the program to allow the user to login to a secure webpage that stores data, input specific parameters, and retrieve the desired SPN's based on their inputs. However, to avoid any conflicts with sustainment, I altered it so that the user may login and download the NASA file independently. After the user has downloaded the file with the click of a button, I defined the program to check for any outdated or pre-existing files, for successful downloads, to acquire the spreadsheet, convert it from a text file to a comma separated file and finally into an Excel spreadsheet to be filtered and later scrutinized for specific SPN numbers. Once this file has been automatically manipulated to provide only the SPN numbers that are desired, they are stored in a global variable, shown on the GUI, and transferred over to a new Excel worksheet for comparison. I managed to get my application to acquire the CSWG (Computer Safety Working Group) and the SPNWG (Space Station Working Group) SPN's with just two mouse clicks for each pull, as opposed to several from the original process. When all three pulls are performed, an Excel sheet containing all three different results will be generated for the user to compare and determine which SPN's will be presented or reviewed the following month. The experience from this internship has been spectacular. As a high school senior who will begin attending college in the fall, this internship has been both educationally and occupationally beneficial. The internship has allowed me the opportunities to learn new programming languages, effectively network with NASA personnel from a variety of departments at JSC, and allowed me to learn new professional skills and etiquette. My internship at NASA's Johnson Space Center has further motivated me to pursue a Master's degree in Software Engineering and strive for a prosperous career with NASA as a civil servant.

Delgado, Ivan↗

NASA NTRSDate not supplied

Hail Storm Risk Assessment Using Space-Borne Remote Sensing Observations and Reanalyses

Much of the world is impacted by severe thunderstorms, but whether they become disasters depends upon resilience--our capacity to prepare, mitigate, respond, and recover. Hail is the costliest severe weather hazard for the insurance industry, generating ~70% of severe convective storm losses due to damage to assets such as homes, businesses, agriculture, and infrastructure. Most insurance companies do not reserve enough capital to cover catastrophes, so they acquire reinsurance. The reinsurance industry uses catastrophe models (CatModels) to statistically estimate risk to an insurer’s portfolio. Hail CatModels are developed with climatologies that define hailstorm frequency and severity. Hail-prone areas can be defined using hail reports from trained spotters, the media, and the general public. Extremely severe hail (2+ inch diameter) occurs nearly every day across the world. Weather radars can detect hail because hailstones strongly reflect microwave signals that they emit. However, hail climatologies are difficult to derive because hail covers small areas and there are neither hail reporting mechanisms (e.g. website or mobile app) nor radar networks in most places outside the US and Europe. This lack of ground truth on severe hail puts society and economies at risk. Hail is generated within storms by strong updrafts. These updrafts exhibit unique signatures in NASA and other agency satellite observations, offering new opportunities for hailstorm analysis. Geostationary (GEO) visible and infrared imagery has been collected for ~15-25 years across the world (region dependent) and methods have been developed at NASA Langley Research Center (LaRC) to detect hailstorm updrafts using GEO imagery. Climatological GEO updraft data has been used by Willis Towers Watson (WTW), a leader in catastrophe risk assessment for the insurance industry, and Karlsruhe Institute of Technology to develop CatModels over Europe and Australia. Hail can also be inferred with passive microwave imagery collected by low-Earth-orbiting sensors such as the GPM GMI, TRMM TMI, AMSR-E, AMSR-2, SSM/I, and SSMIS over the last 20+ years using methods developed at the Marshall Space Flight Center (MSFC). Hailstorms generate enhanced lightning flash rates that can be tracked using new GOES-R series GEO Lightning Mapping (GLM) imagery. Atmospheric reanalyses can be used to define favorable hailstorm environments for combination with the satellite-based storm detections. This presentation will describe a framework for developing continental to global hail climatologies and CatModels based on NASA satellite data and capabilities. This is a collaboration between LaRC and MSFC, WTW, and partners in Brazil, Argentina, and South Africa. This project seeks to mitigate hail disasters by aiding development of new satellite-based severe storm nowcasting tools by regional partners and developing climatologies to improve societal understanding of hail frequency. GEOO visible and infrared metrics of storm intensity, environmental conditions based on reanalyses, spotter hail reports and radar MESH observations are intercompared to quantify the detectability of hailstorms, and our ability to discriminate hailstorms from other severe storms. We are also maturing methods using land surface imaging satellite data (e.g. MODIS, Landsat, Sentinel 1 and 2) to identify hail damage to agriculture. Work with WTW will improve socioeconomic resilience through development of new CatModels. Southern Brazil, Uruguay, Paraguay, and Argentina feature some of the most intense thunderstorms on Earth. South America and South Africa are developing insurance markets of interest to WTW clients, and is similar to other regions routinely impacted by hail that do not have comprehensive hail reporting or radars to assess hailstorm frequency. Project datasets will be made available via online GIS-enabled tools developed at the LaRC Atmospheric Science Data Center (ASDC) which will visualize data and provide it in multiple formats for use in a wide range of open source and commercial tools.

Kristopher Michael Bedka↗