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NASA SPoRT Dust Event Labels

Authors: Elmer, Nicholas; Berndt, Emily; Harkema, Sebastian; Burke, Angela; Fuell, Kevin; Feemster, Caley; Junod, Robert;

NASA SPoRT Dust Event Labels

Abstract

GENERAL INFORMATION 1. Title of Dataset: SPoRT Dust Event Labels 2. Author Information: A. Nicholas Elmer NASA Postdoctoral Program NASA Marshall Space Flight Center Huntsville, Alabama, USA nicholas.j.elmer@nasa.gov B. Emily Berndt Earth Science Office NASA Marshall Space Flight Center Huntsville, Alabama, USA emily.b.berndt@nasa.gov 3. Date of data collection: 2018-01-14 to 2020-06-09 4. Geographic location of data collection: Southwest United States West longitude: 126.0 W East longitude: 90.0 W South latitude: 24.0 N North latitude: 45.0 N 5. Funding source: Data collection was supported by the NASA Short-term Prediction Research and Transition (SPoRT) project at NASA Marshall Space Flight Center. DATA & FILE OVERVIEW 1. File List: testing_dataset.txt training_dataset.txt validation_dataset.txt Georeferenced polygon shapefiles, comprising .shp, .shx, .dbf, and .prj files with timestamp {YYYY}{MM}{DD}T{HH}{MM}{SS}. 2. Relationship between files: This dataset contains: 1) Georeferenced (WGS 1984) polygon shapefiles containing image classification for airborne dust. 2) Text files listing the timestamp of shapefiles used in the training, testing, and validation datasets used by the Berndt et al. (2021) random forest dust detection model. METHODOLOGICAL INFORMATION. 1. Description of methods for collection: The dust labels were manually assigned by atmospheric scientists based largely on the GOES-16 ABI Dust RGB imagery but supplemented by GOES-16 true color imagery, Area Forecast Discussions issued by NOAA National Weather Service Weather Forecast Offices, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements. 2. Methods for processing the data: GOES-16 ABI Dust RGB imagery was downloaded from Amazon Web Services and regridded to a 2-km rectangular grid. Feature labels were manually drawn on the imagery and classified by experts with the aid of a Python Graphical User Interface (GUI) based on the Tkinter python package. DATA-SPECIFIC INFORMATION FOR SHAPEFILES: 1. Shapefile coordinate system: WGS 1984 2. Number of Shapefiles: 83 3. Number of Polygons per Shapefile: Varies 4. Number of Attributes per polygon: 1 5. Attribute list: A. Class: 0 --> No Dust 1 --> Dust 2 --> Reserved for future use 3 --> No Data Value

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Keywords

Dust, GOES-16, Random forest

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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