The Dark Side of Hurricane Matthew: Unique Perspectives from the VIIRS Day/Night Band

Abstract

Hurricane Matthew (28 September–9 October 2016) was perhaps the most infamous storm of the 2016 Atlantic hurricane season, claiming over 600 lives and causing over $15 billion (U.S. dollars) in damages across the central Caribbean and southeastern U.S. seaboard. Research surrounding Matthew and its many noteworthy meteorological characteristics (e.g., rapid intensification into the southernmost category 5 hurricane in the Atlantic basin on record, strong lightning and sprite production, and unusual cloud morphology) is ongoing. Satellite remote sensing typically plays an important role in the forecasting and study of hurricanes, providing a top-down perspective on storms developing over the remote and inherently data-sparse tropical oceans. In this regard, a relative newcomer among the suite of satellite observations useful for tropical cyclone monitoring and research is the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB), a sensor flying on board the NOAA–NASA Suomi National Polar-Orbiting Partnership (SNPP) satellite. Unlike conventional instruments, the DNB’s sensitivity to extremely low levels of visible and near-infrared light offers new insight into storm properties and impacts. Here, we chronicle Matthew’s path of destruction and peer through the DNB’s looking glass of low-light visible observations, including lightning connected to sprite formation, modulation of the atmospheric nightglow by storm-generated gravity waves, and widespread power outages. Collected without moonlight, these examples showcase the wealth of unique information present in DNB nocturnal low-light observations without moonlight, and their potential to complement traditional satellite measurements of tropical storms worldwide.

Dynamical Coupling Between Hurricane Matthew and the Middle to Upper Atmosphere via Gravity Waves

“…As first mentioned by Miller et al (), similar 30–200 km CGW patterns were detected in the vicinity of Hurricane Matthew on almost every night during 2–7 October period in the DNB images. These data are shown in Figure .…”

“…Due to the extremely high resolution provided by the DNB, very small wave structures were also observed. Miller et al () analyze the GW ripple wave pattern seen in Figure a ( small GWs notation) on the eastern side of Matthew's eye and north of Venezuela, determining a wavelength of 24–27 and 30 km. These ripple‐pattern structures are distributed to the northeast of the hurricane eye.…”

“…Among the published studies of Hurricane Matthew,Miller et al (2018) conducted a preliminary analysis of the DNB's unique remote sensing capability. Owing to the high sensitivity and high spatial resolution of the low-light 10.1029/2018JA026453 Journal of Geophysical Research: Space Physics visible DNB observations, they noted fine structures in the visible-light-band airglow layer due to hurricaneinduced GWs.…”

Observations of Red Sprites Above Hurricane Matthew

“…Figure a shows the long‐exposure (over 1 s at ISO‐6400) color picture (photographed with a Canon REBEL T3 DSLR) of a bright sprite observed at 0656UTC on 1 October, with the causative stroke being 593 km from the observer. In the background we can see stars and also the mesopause gravity wave (banded structure) related to the convection of hurricane (Miller et al, ). The maximum range of sprite observation is estimated to be >800 km.…”

Assessing the stability of surface lights for use in retrievals of nocturnal atmospheric parameters

“…Unlike its predecessor, the Operational Linescan System (OLS) on the Defense Meteorological Satellite Program (DMSP) platforms, the DNB is calibrated and can be used for quantitative applications. DNB imagery has proven useful for many applications including cloud property retrievals (Walther et al, 2018), the detection of low-altitude circulation centers in tropical cyclones (Hawkins et al, 2017), forest fire detection and the estimation of fire radiative power (Elvidge et al, 2013;Polivka et al, 2016), lightning detection (Bankert et al, 2011), the identification of fishing boats and fishery monitoring (Elvidge et al, 2015(Elvidge et al, , 2018, mapping power outages in the wake of natural disasters (Miller et al, 2015(Miller et al, , 2018Molthan and Jedlovec, 2013), and monitoring socioeconomic activity (Ma et al, 2014).…”