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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2007

Creating Interactive Graphical Overlays in the Advanced Weather Interactive Processing System Using Shapefiles and DGM Files

Graphical overlays can be created in real-time in the Advanced Weather Interactive Processing System (AWIPS) using shapefiles or Denver AWIPS Risk Reduction and Requirements Evaluation (DARE) Graphics Metafile (DGM) files. This presentation describes how to create graphical overlays on-the-fly for AWIPS, by using two examples of AWIPS applications that were created by the Applied Meteorology Unit (AMU) located at Cape Canaveral Air Force Station (CCAFS), Florida. The first example is the Anvil Threat Corridor Forecast Tool, which produces a shapefile that depicts a graphical threat corridor of the forecast movement of thunderstorm anvil clouds, based on the observed or forecast upper-level winds. This tool is used by the Spaceflight Meteorology Group (SMG) at Johnson Space Center, Texas and 45th Weather Squadron (45 WS) at CCAFS to analyze the threat of natural or space vehicle-triggered lightning over a location. The second example is a launch and landing trajectory tool that produces a DGM file that plots the ground track of space vehicles during launch or landing. The trajectory tool can be used by SMG and the 45 WS forecasters to analyze weather radar imagery along a launch or landing trajectory. The presentation will list the advantages and disadvantages of both file types for creating interactive graphical overlays in future AWIPS applications. Shapefiles are a popular format used extensively in Geographical Information Systems. They are usually used in AWIPS to depict static map backgrounds. A shapefile stores the geometry and attribute information of spatial features in a dataset (ESRI 1998). Shapefiles can contain point, line, and polygon features. Each shapefile contains a main file, index file, and a dBASE table. The main file contains a record for each spatial feature, which describes the feature with a list of its vertices. The index file contains the offset of each record from the beginning of the main file. The dBASE table contains records for each attribute. Attributes are commonly used to label spatial features. Shapefiles can be viewed, but not created in AWIPS. As a result, either third-party software can be installed on an AWIPS workstation, or new software must be written to create shapefiles in the correct format.

Barrett, Joe H., III↗

NASA NTRS2007

Creating Interactive Graphical Overlays in the Advanced Weather Interactive Processing System (AWIPS) Using Shapefiles and DGM Files

Graphical overlays can be created in real-time in the Advanced Weather Interactive Processing System (AWIPS) using shapefiles or DARE Graphics Metafile (DGM) files. This presentation describes how to create graphical overlays on-the-fly for AWIPS, by using two examples of AWIPS applications that were created by the Applied Meteorology Unit (AMU). The first example is the Anvil Threat Corridor Forecast Tool, which produces a shapefile that depicts a graphical threat corridor of the forecast movement of thunderstorm anvil clouds, based on the observed or forecast upper-level winds. This tool is used by the Spaceflight Meteorology Group (SMG) and 45th Weather Squadron (45 WS) to analyze the threat of natural or space vehicle-triggered lightning over a location. The second example is a launch and landing trajectory tool that produces a DGM file that plots the ground track of space vehicles during launch or landing. The trajectory tool can be used by SMG and the 45 WS forecasters to analyze weather radar imagery along a launch or landing trajectory. Advantages of both file types will be listed.

Barrett, Joe H., III↗

NASA NTRS2007

Improving Access to Precipitation Data for GIS Users: Designing for Ease of Use

The Global Precipitation Measurement Mission (GPM) is a NASA/JAXA led international mission to configure a constellation of space-based radiometers to monitor precipitation over the globe. The GPM goal of making global 3-hour precipitation products available in near real-time will make such global products more useful to a broader community of modelers and Geographic Information Systems (GIS) users than is currently the case with remote sensed precipitation products. Based on the existing interest to make Tropical Rainfall Measuring Mission (TRMM) data available to a growing community of GIS users as well as what will certainly be an expanded community during the GPM era, it is clear that data systems must make a greater effort to provide data in formats easily used by GIS. We describe precipitation GIS products being developed for TRMM data. These products will serve as prototypes for production efforts during the GPM era. We describe efforts to convert TRMM precipitation data to GeoTIFF, Shapefile, and ASCII grid. Clearly, our goal is to format GPM data so that it can be easily used within GIS applications. We desire feedback on these efforts and any additions or direction changes that should be undertaken by the data system.

Stocker, Erich F.↗

NASA NTRSDate not supplied

Influence of regional anthropogenic changes over Nile region on the climate system during the late Holocene (~2500 years before present)

The societal impacts of climate change during the late Holocene leads to regional anthropogenic changes over the Nile region floodplain and could have acted in tandem with natural factors like major volcanic eruptions on the regional climate system to magnify the local climatic impacts. This study aims to explore and investigate the sensitivity of climatic changes to the regional anthropogenic changes due to various factors over the Nile river floodplains during the late-Holocene (2.5K years before present). The GISS ModelE Earth system model will be used to simulate the various scenarios of regional increasing/decreasing river fraction, changes in vegetation type and cover, along with changes in land surface type against the no-changes scenario in absence of volcanic eruptions. The spatial coverage of the Nile river basin is estimated using the GIS shapefile based on elevation data from Shuttle Radar Topography Mission (SRTM) at 3 Arc-seconds (approx. 90-meter) horizontal resolution. The extent of flooding in the model grid (2.0°x2.5° in latitude and longitude) is estimated using the existing high-resolution (0.125°x0.125°) gridded topographic elevation information and mapped over the Nile river floodplains. This study also focuses on evaluating the NASA GISS ModelE for resolving the climate feedbacks and response on climate system due to anthropogenic changes and volcanic eruptions. It is also aimed to analyze and quantify the impact of various anthropogenic factors over the African monsoon system and rainfall over the region, which feeds the Nile River.

Anthropogenic changes↗

NASA NTRSDate not supplied

ImageLabler: Labeling and Managing Image Data for Machine Learning in the Earth Sciences

While machine learning techniques for image classification have been around for a long time, storing and managing the vast number of images required as training data is still a problem for scientists. This is especially true for the field of Earth science, where only recently have experts begun using machine learning techniques for image-based phenomena classification. Image Labeler, a fast and scalable cloud-based tagging platform for Earth science images, seeks to improve upon existing methods of managing images and associated metadata, such as maintaining categorized folders of images on a local machine, a process that can be cumbersome and difficult to scale. The platform facilitates rapid development of image-based Earth science phenomena training datasets by allowing scientists to upload their existing imagery as well as extract new samples from open satellite imagery services made available through NASA’s Global Imagery Browse Service (GIBS). Image Labeler also supports GeoTIFF data, with capabilities such as displaying GeoTIFFs on an interactive map, drawing shapefiles over them, and tagging them with additional metadata. This allows scientists to perform spatiotemporal subsetting with geographic information and develop training data more quickly. Built using modern web technologies, Image Labeler includes additional capabilities such as team collaboration for large-scale image tagging projects. Users can download their data in a machine-learning-ready format, allowing scientists to spend time on experimentation rather than on the collection of training data. In this presentation, we demonstrate how Image Labeler seeks to become a one-stop image data management solution for machine learning applications in Earth science.

Ashish Acharya↗

NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗