Abstract
Many previous studies have attempted to distinguish fog from clouds using low-orbit and geostationary satellite observations from visible (VIS) to longwave infrared (LWIR) bands. However, clouds and fog have often been misidentified because of their similar spectral features. Recently, advanced meteorological geostationary satellites with improved spectral, spatial, and temporal resolutions, including Himawari-8/9, GOES-16/17, and GeoKompsat-2A, have become operational. Accordingly, this study presents an improved algorithm for detecting daytime sea fog using one VIS and one near-infrared (NIR) band of the Advanced Himawari Imager (AHI) of the Himawari-8 satellite. We propose a regression-based relationship for sea fog detection using a combination of the Normalized Difference Snow Index (NDSI) and reflectance at the green band of the AHI. Several case studies, including various foggy and cloudy weather conditions in the Yellow Sea for three years (2017–2019), have been performed. The results of our algorithm showed a successful detection of sea fog without any cloud mask information. The pixel-level comparison results with the sea fog detection based on the shortwave infrared (SWIR) band (3.9 μm) and the brightness temperature difference between SWIR and LWIR bands of the AHI showed high statistical scores for probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS). Consequently, the proposed algorithms for daytime sea fog detection can be effective in daytime, particularly twilight, conditions, for many satellites equipped with VIS and NIR bands.
Highlights
Sea fog often causes automobile, aviation, and marine transportation accidents because of its low visibility, with subsequent losses to life and socioeconomic impacts occurring throughout the ocean and in coastal regions [1]
The pixel-by-pixel statistical comparison between the proposed sea fog detection algorithm with Normalized Difference Snow Index (NDSI) and the 0.51 μm band and the sea fog data obtained from brightness temperature differences (BTD) using the 3.9 μm and 11.2 μm bands of Advanced Himawari Imager (AHI) was performed using the probability of detection (POD), post agreement (PAG), critical success index (CSI), and Heidke skill score (HSS) metrics as follows: RemoRteemSeontes.S2en02s.02,01220,11512, 1x FOR PEER REVIEW
Cloud mask information is an important parameter retrieved from satellites for generating useful geophysical and meteorological parameters for surface information such as fog, surface temperature, vegetation, and so on
Summary
Sea fog often causes automobile, aviation, and marine transportation accidents because of its low visibility, with subsequent losses to life and socioeconomic impacts occurring throughout the ocean and in coastal regions [1]. Cloud mask information has been provided by several satellite sensors, including the Advanced Very-High-Resolution Radiometer (AVHRR), and Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) of polar orbiting satellites, Advanced Baseline Imager (ABI), Advanced Meteorological Imager (AMI), and Advanced Himawari Imager (AHI) [8] of geostationary satellites These sensors tend to misidentify fog, sea ice, and snow as clouds because of their similar spectral features in the VIS bands of satellite sensors [9,10]. We present a novel daytime sea fog detection algorithm with the advantage of the VIS band and compensating for the disadvantages of the IR-based algorithm in twilight conditions around the Yellow Sea region using observations from the AHI [8]. The summary and conclusion of this study are described
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