Application of a Nighttime Fog Detection Method Using SEVIRI Over an Arid Environment

Abstract

Fog degrades horizontal visibility causing significant adverse impacts on transport systems. The detection of fog from satellite data remains challenging especially in the presence of higher clouds, dust, mist, or unknown underlying soil conditions. Observations from Meteosat second generation Spinning-Enhanced Visible and Infrared Imager (MSG SEVIRI) over the United Arab Emirates (UAE), an arid area on the Arabian Peninsula, from 2016 to 2018 (two fog seasons) are used in this study. We implement an adaptive threshold-based technique using pseudo-emissivity values to detect nocturnal fog from SEVIRI. The method allows the threshold to vary spatially and temporally. Low clouds are detected with the analysis of the vertical temperature gradient. Fog classification was verified against four stations in the UAE, namely Abu Dhabi, Dubai, Al Ain, and Al Maktoum, where visibility and meteorological observations are available. The probability of detection (POD) (false alarm ratio (FAR)) was 0.81 (0.40), 0.83 (0.50), 0.83 (0.33), and 0.77 (0.44) at Abu Dhabi, Dubai, Al Ain, and Al Maktoum, respectively. In addition, the spatial frequency of fog is presented, which provides new insights into the fog dynamics in the region.

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

“…A majority of research involving the application of satellite data and imagery are used in detection (Weston and Temimi 2020 ; Ahmed et al 2015 ; Chaurasia et al 2011 ; Lee et al 2011 ; Yoo et al 2010 ), monitoring (Jiskoot et al 2019 ; Wang et al 2010 ), for developing fog climatology (Cermak et al 2009 ; Bendix 2002 ) and as predictors in the machine learning algorithms for fog forecasting (Kim et al 2020a , b ). In locations where there is an absence of in-situ observations, or it is difficult to set up observation sites, the significance of satellite data plays a crucial role in forecasting.…”

“…The general consensus is, it is a clear sky if BTD = 0 °C, it is a high cloud if BTD < 0 °C, and it is fog if BTD > 0 °C (Eyre et al 1984 ). Detection of fog during daytime using infrared bands poses a bit of difficulty as the brightness temperature of fog is annulled by its sunlight reflectance (Weston and Temimi 2020 ). So for fog detection during day the very high clouds (very low brightness temperature) showing pixels are eliminated using the thresholding method and the fog is discriminated from the low stratus clouds based on the calculation of geometrical thickness using the Visible and Near-Infrared bands.…”

“…After the geometrical thickness is determined the elevation of the stratus/ fog from the ground is estimated using the DEM data to evaluate the height of the stratus/ fog from the ground, if the cloud base height is less than surface elevation the pixel is identified as fog (Cermak and Bendix 2011 ; Bendix et al 2005 ). Geostationary satellites such as AVHRR (Advanced Very-High-Resolution Radiometer) (Baldocchi and Waller 2014 ; Wang et al 2010 ; Choudhury et al 2007 ), MODIS (Moderate Resolution Imaging Spectroradiometer) (Terra & Aqua, Wu and Li 2014 ; Chaurasia et al 2011 ), CALIOPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) (Xiao et al 2019 ; Wu et al 2015 ) and polar-orbiting satellites such as GOES (Geostationary Operational Environmental satellite), SEVIRI (Spinning Enhanced Visible and Infrared Imager System) (Weston and Temimi 2020 ; Egli et al 2018 ; Cermak et al 2009 ), MTSAT (Multifunctional Transport Satellites) (Jun et al 2012 ; Gao et al 2009 ) and INSAT-3D (Indian National SATellite) (Banerjee and Padmakumari, 2020; Arun et al 2018 ; Chaurasia and Jenamani 2017 ; Chaurasia and Gohil 2015 ). Comparatively, a more significant chunk of research literature in the past had utilized AVHRR data where BTD is calculated using channel 4 (thermal) and 3 (absorbing) (Bendix 2002 ); and MODIS data where TIR (Thermal Infrared) and MIR (Mid Infrared) is used for BTD retrieval (Chaurasia et al 2011 ; Bendix et al 2006 ).…”

Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

“…Even if it is a difficult task, fog detection from satellite data is approached by [ 116 ]. Information from a Meteostat second-generation Spinning-Enhanced Visible and Infrared Imager over the United Arab Emirates was used for the study.…”

A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery

“…Fog and low stratus have differences in some applications such as air and sea navigation and thus, there are several studies which have proposed approaches for distinguishing these two meteorological features, such as [20]. However, fog and low stratus have similar radiation signatures in satellite images [21].…”