A method of detecting sea fogs using CALIOP data and its application to improve MODIS-based sea fog detection

Wu, Deliang; Lü, Bo; Zhang, Tianche; Yan, Fengqi

doi:10.1016/j.jqsrt.2014.09.021

Cited by 37 publications

(26 citation statements)

References 28 publications

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“…Egli et al (2018) used the cloud-base information from ground observations to reduce the errors in satellite fog retrievals. Lidar measurements, such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument aboard CALIPSO, may provide direct estimates of cloud-base heights, but Wu et al (2015) showed that the differentiation between surface and ground-touching fog in the CALIOP measurements may be challenging. Ellrod (2003) suggested that the cloud-top temperature of a cloud layer near the surface might be closer to the surface temperature because of a thermal inversion in the cloud than outside the cloud.…”

Section: Introductionmentioning

confidence: 99%

Arctic Fog Detection Using Infrared Spectral Measurements

Chen

et al. 2019

Journal of Atmospheric and Oceanic Technology

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The rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂ T in this work. A fog event is said to be detected if ∂ T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂ T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.

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Section: Introductionmentioning

confidence: 99%

Arctic Fog Detection Using Infrared Spectral Measurements

Chen

et al. 2019

Journal of Atmospheric and Oceanic Technology

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“…It is difficult to distinguish between sea fog and low clouds and stratus clouds through visual interpretations [32]. Therefore, a sea fog sample database is established using CALIPSO-VFM data to assist the selection of sea fog samples from AHI images [5,33]. Figure 4 shows the selection method, where Figure 4a is an AHI image on 15 June 2018 at 05:10 UTC, and Figure 4b is the classification result when using CALIPSO-VFM data from 15 June 2018 at 05:17 UTC.…”

Section: Sample Selection Of Sea Fogmentioning

confidence: 99%

“…There is little research in the field of navigation risk assessment that applies to remote sensing technology for sea fog detection, although many methods of detecting sea fog by remote sensing have been developed. For instance, the multiband threshold algorithm [5] and U-Net deep learning method [6] were used for sea fog detection based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. An unsupervised learning algorithm was used for sea fog detection based on Communication, Ocean, and Meteorological Satellite (COMS) data [7], while a dual-satellite method was proposed by [8], which combined Himawari-8 and FY-4A satellite data to detect sea fog at dawn.…”

Section: Introductionmentioning

confidence: 99%

Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China

Zeng

Zhang

et al. 2021

Remote Sensing

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Sea fog is a disastrous marine phenomenon for ship navigation. Sea fog reduces visibility at sea and has a great impact on the safety of ship navigation, which may lead to catastrophic accidents. Geostationary orbit satellites such as Himawari-8 make it possible to monitor sea fog over large areas of the sea. In this paper, a framework for marine navigation risk evaluation in fog seasons is developed based on Himawari-8 satellite data, which includes: (1) a sea fog identification method for Himawari-8 satellite data based on multilayer perceptron; (2) a navigation risk evaluation model based on the CRITIC objective weighting method, which, along with the sea fog identification method, allows us to obtain historical sea fog data and marine environmental data, such as properties related to wind, waves, ocean currents, and water depth to evaluate navigation risks; and (3) a way to determine shipping routes based on the Delaunay triangulation method to carry out risk analyses of specific navigation areas. This paper uses global information system mapping technology to get navigation risk maps in different seasons in Bohai Sea and its surrounding waters. The proposed sea fog identification method is verified by CALIPSO vertical feature mask data, and the navigation risk evaluation model is verified by historical accident data. The probability of detection is 81.48% for sea fog identification, and the accident matching rate of the navigation risk evaluation model is 80% in fog seasons.

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“…Totally attenuated refers to a set of pixels that have a profile that is totally attenuated by thick clouds above the sea surface. The classification method for fog is that used by Wu et al ( 2015 ). It examines the CALIOP VFM products, and classifies any cloud layer which has a base attached to the sea surface (allowing 2 bins of the CALIOP vertical resolution above the sea surface, 30 m per bin for low altitudes between −0.5 and 8.2 km) as sea fog.…”

Section: Datamentioning

confidence: 99%

A New Application of Unsupervised Learning to Nighttime Sea Fog Detection

Shin

Kim

2018

Asia-Pacific J Atmos Sci

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This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

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A method of detecting sea fogs using CALIOP data and its application to improve MODIS-based sea fog detection

Cited by 37 publications

References 28 publications

Arctic Fog Detection Using Infrared Spectral Measurements

Arctic Fog Detection Using Infrared Spectral Measurements

Fog Season Risk Assessment for Maritime Transportation Systems Exploiting Himawari-8 Data: A Case Study in Bohai Sea, China

A New Application of Unsupervised Learning to Nighttime Sea Fog Detection

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