Geostationary Operational Environmental Satellite-R series advanced baseline imagery artifacts

Gunshor, Mathew M.; Schmit, Timothy J.; Pogorzala, David; Lindstrom, Scott B.; Nelson, Jonathan D.

doi:10.1117/1.jrs.14.032411

Cited by 4 publications

(3 citation statements)

References 11 publications

(15 reference statements)

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“…As mentioned previously, this temporal resolution is insufficient to allow estimates of wave phase velocities for the scales of interest. An important issue we have to consider with ABI data is the satellite instrument measurement failures that result in longitudinal striping noise (Gunshor et al., 2020). These artifacts cause strong local shifts in the brightness temperature field that can be strongly misinterpreted depending on the analysis.…”

Section: Datamentioning

confidence: 99%

Analysis of Trapped Small‐Scale Internal Gravity Waves Automatically Detected in Satellite Imagery

Vicari,

Stephan,

Lane

et al. 2024

JGR Atmospheres

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In water vapor‐sensitive satellite imagery, small‐scale wave‐like perturbations of brightness temperature can be attributed to the presence of trapped internal waves in the troposphere. We present a method for detecting these local perturbations with wavelengths of about 10 km and apply it to imagery from the Advanced Baseline Imager on board the geostationary satellite GOES‐16. The algorithm allows us to analyze 4 years of sub‐hourly data in the southern part of the tropical eastern Pacific, where only a relatively low amount of medium and high clouds obscures the scene. By combining a measure of wave activity/trapping with ERA5 reanalysis data, we connect the occurrence of trapping with the presence of an increased upper‐tropospheric wind shear. This connection is more evident during December, January and February, when upper‐tropospheric jets are more likely. Our work supports existing case and model studies and is a step forward in the statistical and automated analysis of trapped small‐scale internal waves in the atmosphere.

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Section: Datamentioning

confidence: 99%

Analysis of Trapped Small‐Scale Internal Gravity Waves Automatically Detected in Satellite Imagery

Vicari,

Stephan,

Lane

et al. 2024

JGR Atmospheres

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“…This, combined with many of the channels being derived from those aboard the Visible Infrared Imaging Radiometer Suite (VIIRS), make the data from ABI more suitable for research purposes than those from older instruments (Heidinger et al, 2020). A number of artefacts are known to occur in ABI imagery (Gunshor et al, 2020). Although the majority of these artefacts are removed using the data quality flag associated with the ABI data, we have found a number of cases in which bad detector stripes (described in Sect.…”

Section: Advanced Baseline Imagermentioning

confidence: 99%

“…Although the majority of these artefacts are removed using the data quality flag associated with the ABI data, we have found a number of cases in which bad detector stripes (described in Sect. 3.2 of Gunshor et al, 2020) are not flagged in the data, and so detection and tracking of DCCs has not been performed during the time periods when these artefacts occurred.…”

Section: Advanced Baseline Imagermentioning

confidence: 99%

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

Jones¹,

Christensen²,

Stier³

2023

Atmos. Meas. Tech.

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Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.

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The Great Dimming of Betelgeuse seen by the Himawari-8 meteorological satellite

2022

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Betelgeuse, one of the most studied red supergiant stars1,2, dimmed in the optical range by ~1.2 mag between late 2019 and early 2020, reaching a historical minimum3–5 called ‘the Great Dimming’. Thanks to enormous observational effort to date, two hypotheses remain that can explain the Dimming1: a decrease in the effective temperature6,7 and an enhancement of the extinction caused by newly produced circumstellar dust8,9. However, the lack of multiwavelength monitoring observations, especially in the mid-infrared, where emission from circumstellar dust can be detected, has prevented us from closely examining these hypotheses. Here we present 4.5 yr, 16-band photometry of Betelgeuse between 2017 and 2021 in the 0.45–13.5 μm wavelength range making use of images taken by the Himawari-810 geostationary meteorological satellite. By examining the optical and near-infrared light curves, we show that both a decreased effective temperature and increased dust extinction may have contributed by almost equal amounts to the Great Dimming. Moreover, using the mid-infrared light curves, we find that the enhanced circumstellar extinction actually contributed to the Dimming. Thus, the Dimming event of Betelgeuse provides us with an opportunity to examine the mechanism responsible for the mass loss of red supergiants, which affects the fate of massive stars as supernovae11.

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Geostationary Operational Environmental Satellite-R series advanced baseline imagery artifacts

Cited by 4 publications

References 11 publications

Analysis of Trapped Small‐Scale Internal Gravity Waves Automatically Detected in Satellite Imagery

Analysis of Trapped Small‐Scale Internal Gravity Waves Automatically Detected in Satellite Imagery

A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations

The Great Dimming of Betelgeuse seen by the Himawari-8 meteorological satellite

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