Anthropogenic Specular Interference in the Operational GOES-R Fire Product

<strong>Dataset for the research paper "Anthropogenic Specular Interference in the Operational GOES-R Fire Product".</strong> Large reflective structures like solar power plants and commercial greenhouses sometimes reflect sunlight directly into GOES-R sensors. These anthropogenic specular reflections, or "sparkles", cause commission errors in operational GOES-R ABI products like the Fire Detection and Characterization Algorithm (FDCA). Using the <em>abi-sparkle</em> library for Python (Dove-Robinson, 2023), we generated a dataset containing both detected anthropogenic specular reflection pixels and the coincident FDCA commission errors caused by them for the GOES-16 CONUS domain during the 2020 calendar year. The dataset consists of two exported PostgreSQL tables: <em>sparkle_pixels_g16_abi_conus_2020</em>, which contains the detected anthropogenic specular reflection pixels at 500 m resolution, and <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which contains clustered FDCA false alarm fire pixels caused by anthropogenic specular reflection at 2 km resolution. The FDCA pixels were only processed for fire mask codes 10-15 and 30-35; see Table 3.11 in Schmidt et al., 2013 for fire code definitions. Each row in <em>sparkle_pixels_g16_abi_conus_2020 </em>is a detected specular reflection pixel in a GOES-16 CONUS image from the 2020 calendar year with a unique numeric ID <em>sparkle_id </em>and associated metadata from the detection algorithm <em>abi-sparkle</em>. The column <em>sparkle_geom </em>is a PostGIS geometry ST_Point object that can be used to plot the pixels on a map. FDCA fire pixels at 2 km resolution were clustered based on their connectivity in a 3x3 pixel kernel and assigned a UUID <em>fire_cluster_id </em>in the table <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>. Only the fire clusters that overlapped with sparkle pixels in time and space were retained in the table. In this way, each row of <em>fdca_commission_error_clusters_g16_abi_conus_2020 </em>is a unique cluster of errant FDCA fire pixels caused by anthropogenic specular reflection in every available scan start time for the GOES-16 CONUS domain in 2020. Every fire cluster centroid has a PostGIS geometry object <em>fire_cluster_centroid_geom </em>that can be used to plot the errant fire pixel clusters on a map. The two tables relate with the column <em>sparkle_ids </em>in <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which is an array of overlapping sparkle IDs from the <em>sparkle_pixels_g16_abi_conus_2020 </em>table. The combined dataset may therefore be generated with a simple SQL INNER JOIN: <em>SELECT * FROM fdca_commission_error_clusters_g16_abi_conus_2020 fcecgac<br> INNER JOIN sparkle_pixels_g16_abi_conus_2020 spgac ON spgac.sparkle_id = ANY(fcecgac.sparkle_ids);</em> The tables can be imported into a PostgreSQL database version 12 or newer with PostGIS extensions installed. For example, to import the tables into a database in a Linux environment, run the following commands: <em>gunzip -c sparkle_pixels_g16_abi_conus_2020.sql.gz | psql -d your_database_name<br> gunzip -c fdca_commission_error_clusters_g16_abi_conus_2020.sql.gz | psql -d your_database_name</em>