Abstract
Sea ice is an important meteorological factor affecting the global climate system, but it is difficult to observe in sea ice ground truth data because of its location mainly at high latitudes and in polar regions. Accordingly, sea-ice detection research has been actively conducted using satellites, since the 1970s. Polar-orbiting and geostationary satellites are used for this purpose; notably, geostationary satellites are capable of real-time monitoring of specific regions. In this paper, we introduce the Geo-KOMPSAT-2A (GK-2A)/Advanced Meteorological Imager (AMI) sea-ice detection algorithm using Japan Meteorological Agency (JMA) Himawari-8/Advanced Himawari Imager (AHI) data as proxy data. The GK-2A/AMI, which is Korea Meteorological Administration (KMA)’s next-generation geostationary satellite launched in December 2018 and Himawari-8/AHI have optically similar channel data, and the observation area includes East Asia and the Western Pacific. The GK-2A/AMI sea-ice detection algorithm produces sea-ice data with a 10-min temporal resolution, a 2-km spatial resolution and sets the Okhotsk Sea and Bohai Sea, where the sea ice is distributed during the winter in the northern hemisphere. It used National Meteorological Satellite Center (NMSC) cloud mask as the preceding data and a dynamic threshold method instead of the static threshold method that is commonly performed in existing sea-ice detection studies. The dynamic threshold methods for sea-ice detection are dynamic wavelength warping (DWW) and IST0 method. The DWW is a method for determining the similarity by comparing the pattern of reflectance change according to the wavelength of two satellite data. The IST0 method detects sea ice by using the correlation between 11.2-μm brightness temperature (BT11.2) and brightness temperature difference (BTD) [BT11.2–BT12.3] according to ice surface temperature (IST). In addition, the GK-2A/AMI sea-ice detection algorithm reclassified the cloud area into sea ice using a simple test. A comparison of the sea-ice data derived the GK-2A/AMI sea-ice detection algorithm with the S-NPP/visible infrared imaging radiometer suite (VIIRS) sea ice characterization product indicates consistency of 99.0% and inconsistency of 0.9%. The overall accuracy (OA) of GK-2A/AMI sea-ice data with the sea ice region of interest (ROI) data, which is constructed by photo-interpretation method from RGB images, is 97.2%.
Highlights
Areas covered by sea ice have a higher reflectance of incident solar energy, compared to ice-free sea water
We developed GK-2A/Advanced Meteorological Imager (AMI) sea-ice detection algorithm considering the reflectance variability of sea ice using Himawari-8/Advanced Himawari Imager (AHI) data with similar characteristics to GK-2A/AMI
The dynamic wavelength warping (DWW) is a method for determining similarity, which is determined by comparing the pattern of reflectance change that changes depending on the wavelength of data
Summary
Areas covered by sea ice have a higher reflectance of incident solar energy, compared to ice-free sea water. Satellite data are generally utilized to detect sea ice for polar or high-latitude regions. Satellite-based microwave measurements can detect sea ice below the clouds and night–time. Microwave-based sea-ice data such as AMSR-2 sea ice products have a spatial resolution of more than five kilometers, AMSR-2 sea ice products can cover all polar regions within a day. Sea-ice data derived from optical instruments onboard of polar-orbiting and geostationary satellites cannot detect surface properties under cloud conditions and night–time. The spatial resolution (500 m -2 km) of the optical instruments [4,5,6,7] is higher than that for microwave satellite data except for SAR-based sea-ice data [8]. In the case of optical satellites, there are restrictions on various environmental conditions, but a geostationary satellite among optical satellites can observe a region of interest with a high temporal resolution
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