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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.
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.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.