Aurora Detection From Nighttime Lights for Earth and Space Science Applications. Issue 1 (19th January 2023)

• Record Type: Journal Article
• Authors: Kalb, Virginia; Kosar, Burcu; Collado‐Vega, Yaireska; Davidson, Carol
• Is Part Of: Earth and space science. Volume 10:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-19
• Subjects: aurora -- DNB -- VIIRS -- Ovation Prime -- machine‐learning -- nighttime lights; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2022EA002513
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: This research leverages data from the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer (VIIRS) instrument onboard the Suomi National Polar‐orbiting Partnership (S‐NPP) satellite. We demonstrate the value of mining the VIIRS DNB for aurora and describe our use of unsupervised machine learning to create a binary mask for aurora occurrence. This mask can be used to flag aurora‐contaminated observations for NASA's nighttime lights products for Earth science applications. The identification of auroral regions can also be used for Space Weather applications, for example, for comparison with aurora forecast model and with other satellite‐ or ground‐based aurora observations. The DNB is a broadband channel that is sensitive to wavelengths from 500 to 900 nm, which covers most of the visible light spectrum, and as the name implies, captures light even at night with a sensitivity at the nanowatt level. This band is suitable for aurora observations since the light emitted by the aurora tends to be dominated by emissions from atomic oxygen, resulting in a greenish glow at a wavelength of 557.7 nm, especially at an altitude of 110 km. This study compares the global nighttime derived aurora regions for 17 and 18 March with the NOAA Space Weather Prediction Center's (SWPC) probability product for the St. Patrick's Day geomagnetic storm in 2015. VIIRS sensors are slated to be added to the next generation of polar‐orbiting operational satellites. Our novel automated approach to aurora identification opens up an efficient way to leverage this unique data source. Key Points: Aurora can be detected in satellite‐based Earth nighttime lights using unsupervised machine‐learning methods A binary aurora mask can be derived and used to eliminate aurora‐contaminated nighttime lights observations from analysis for Earth science applications These aurora observations can be used for comparison with aurora forecast model predictions and to augment other sources of aurora observations