Automatic nighttime sea fog detection using GOES-16 imagery

Amani, Meisam; Mahdavi, Sahel; Bullock, Terry; Beale, Steven

doi:10.1016/j.atmosres.2019.104712

Cited by 23 publications

(23 citation statements)

References 15 publications

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“…To date, accurate prediction of fog in NWP still poses a challenge because of the lack of a clear depiction of many interacting processes (Smith et al 2021 ; Westerhuis et al 2020 ). The accuracy of NWP over the ocean and in the case of ice fog is usually low compared to land due to limited observational data (Amani et al 2020 ; Gultepe et al 2015 ). Many uncertainties in the fog microphysical aspect of the model arise because the characteristics, distribution, and evolution of CCN are not clearly available and thus cannot be adequately incorporated in the prediction models.…”

Section: Fog Forecasting and Detectionmentioning

confidence: 99%

“…The brightness temperature corresponds to fog droplet size, its optical thickness, and its height (Bendix et al 2005 ). For nighttime fog detection, the temperature retrieved from ~ 3.8 μm is slightly smaller than ~ 11 μm wavelength for fog droplets whereas the temperature at both the wavelengths is the same for larger-sized cloud droplets (Amani et al 2020 ). The brightness temperature difference calculated between ~ 3.8 and ~ 11 μm is then compared to the threshold value and categorized into fog or cloud.…”

Section: Fog Forecasting and Detectionmentioning

confidence: 99%

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A review on factors influencing fog formation, classification, forecasting, detection and impacts

Lakra

Avishek

2022

Rend. Fis. Acc. Lincei

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With the changing climate and environment, the nature of fog has also changed and because of its impact on humans and other systems, study of fog becomes essential. Hence, the study of its controlling factors such as the characteristics of condensation nuclei, microphysics, air–surface interaction, moisture, heat fluxes and synoptic conditions also become crucial, along with research in the field of prediction and detection. The current review expands for the period between 1976 to 2021, however, especially focused on the research articles published in the last two decades. It considers 250 research papers/research letters, 24 review papers, four book chapters/manuals, five news articles, 15 reports, six conference papers and five other online readings. This review is a compilation of the pros and cons of the techniques used to determine the factors influencing fog formation, its classification, tools and techniques available for its detection and forecast. Some recent advanced are also discussed in this review: role of soil properties on fogs, application of microwave communication links in the detection of fog, new class of smog, and how the cognitive abilities of humans are affected by fog. Recently India and China are facing an emergence and repetitions of fog haze/smog and thus their policies initiatives are also briefly discussed. It is concluded that the complexity in fog forecasting is high due to multiple factors playing a role at multiple levels. Most of the researchers have worked upon the role of humidity, temperature, wind, and boundary layer to predict fogs. However, the role of global wind circulations, soil properties, and anthropogenic heat requires further investigations. Literature shows that fog is being harnessed to address water insecurity in various countries, however, coastal areas of Angola, Namibia and South Africa, Kenya, Eastern Yemen, Oman, China, India, Sri Lanka, Mexico, along with the mountainous regions of Peru, Chile, and Ecuador, are some of the potential sites that can benefit from the installation of fog water harvesting systems.

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Section: Fog Forecasting and Detectionmentioning

confidence: 99%

Section: Fog Forecasting and Detectionmentioning

confidence: 99%

A review on factors influencing fog formation, classification, forecasting, detection and impacts

Lakra

Avishek

2022

Rend. Fis. Acc. Lincei

View full text Add to dashboard Cite

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“…The central wavelengths for all 16 ABI channels are listed in Table 1 along with common nicknames and brief descriptions of each channel's application(s). 7,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Since this work is primarily motivated by inferring and visualizing cloud properties in SW data, we do not attempt to extrapolate SW reflectance in clear sky conditions. The extrapolation method presented could be applied to clear sky conditions but would likely have different optimal parameters and error characteristics than those presented herein.…”

Section: Goes-16 Observationsmentioning

confidence: 99%

“…Table 1 List of ABI channels, their central wavelengths, common nicknames, applications, and use in the final extrapolation algorithm. 7,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Channel (band) number 7 Central wavelength (μm) 7 Common nickname 19 Common application(s) 19,[32][33][34][35] The precise times of observations are all within 10 min of the top of the hour. The subsolar points at 1500, 1700, 1800, and 2100 UTC on the 2018 autumnal equinox are shown in Fig.…”

Section: Goes-16 Observationsmentioning

confidence: 99%

“…Channel 7 is useful in detecting fog and low stratus clouds, but interpretation of the data is complex since separating the terrestrial and solar components is not trivial. 19,22,23 At night, only terrestrial emissions are included in channel 7, so these terrestrial emissions could be extrapolated from nighttime into daytime using the extrapolation algorithm presented herein. The daytime solar component could then be computed by subtracting the extrapolated emitted radiance from the observed daytime radiance.…”

Section: Applications To Goes-16 Channelmentioning

confidence: 99%

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Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations

Rugg

Haggerty

Adriaansen

et al. 2021

J. Appl. Rem. Sens.

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The lack of shortwave (SW, visible, and near-infrared) geostationary satellite data at night results in degradation of many weather forecasts and real-time diagnostic products. We present a method to extrapolate SW GOES-16 advanced baseline imager data through night using nighttime longwave (LW, infrared) observations and the relationships between LW and SW data observed during the previous day. The method is not a forecast since it requires LW nighttime observations but can provide continuity through day, night, and satellite terminator hours. To provide performance statistics, the algorithm is applied during the day so the SW extrapolations can be compared to observations. Typical mean absolute errors (MAEs) range from 1.0% to 12.7% reflectance depending on the SW channel. These MAEs can be predicted using a diagnostic metric called 0-h MAE which quantifies the quality of the algorithm's input data. In addition to quantitative error statistics, three case studies are presented, including an animation of extrapolated imagery from dusk through dawn. Considerations for future improvements include use of convolutional neural networks and/or object-based extrapolations where mesoscale features are extrapolated individually. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Study of fog dissipation in an internal boundary layer on Sable Island

Bardoel,

Hoch,

Ruiz‐Plancarte

et al. 2024

Quart J Royal Meteoro Soc

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An interesting fog dissipation event was observed during the Fog and Turbulence Interactions in the Marine Atmosphere (FATIMA) Grand Banks field campaign, where a fog‐free region appeared immediately downstream of Sable Island as fog advected past it. This fog‐free region was predicted a priori by a high‐resolution numerical model that guided intensive operational periods of the FATIMA campaign, and its presence was adumbrated by GOES satellite observations. A comprehensive set of field observations shows that this fog‐free layer was due to the development of a (daytime) thermal internal boundary layer (IBL) that grew with distance from the leading shore line. The net incoming radiation following sunrise led to an increased air temperature and decreased relative humidity close to the ground, thus dissipating fog over the island. The height of the thermal IBL, as identified by the thickness of the superadiabatic layer, was found to be consistent with several available theoretical IBL formulae.

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Automatic nighttime sea fog detection using GOES-16 imagery

Cited by 23 publications

References 15 publications

A review on factors influencing fog formation, classification, forecasting, detection and impacts

A review on factors influencing fog formation, classification, forecasting, detection and impacts

Extrapolating shortwave geostationary satellite imagery of clouds into nighttime using longwave observations

Study of fog dissipation in an internal boundary layer on Sable Island

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