New Snow & Sea Ice Detection Algorithm Using the New Geostationary Meteorological Satellites Himawari-8 and 9/AHI
Abstract
Since extents of snow and sea ice affect thermal boundary conditions and radiation balance of our climate system, we need to know their distribution accurately. Using observations by geostationary satellite sensors, which have wide observation area and high observation frequency, is one of the best ways to satisfy a such purpose. Himawari-8 and 9 are the new generation geostationary meteorological satellites operated by the Japan Meteorological Agency (JMA). Each of them has the new visible and infrared imager called the Advanced Himawari Imager (AHI), which has huge improvements from previous ones in many ways, i.e., 16 spectral bands (6 in visible/near-infrared and 10 in thermal-infrared regions), full-disk observation in every 10 minutes, Japan and arbitrary target areas observation in every 2.5 minutes and up to 0.5 km horizontal resolution. By making full use of the AHI's capabilities, we have succeeded to improve a snow and sea ice detection algorithm. First, snow cover on dense forest is generally difficult to detect as snow-covered area because solar reflective properties of vegetation disturbs that of snow. Normalized Difference Water/Vegetation Indices (NDWI/NDVI), which became available thanks to the newly added spectral bands, have been employed to consider snow and vegetation areas briefly and make the new threshold. Second, the new multiple-scene merging algorithm that merges every 10 minutes full-disk observations for a day into a more precise daily product has been developed. Finally we validated our algorithm by comparing with in-situ snow depth data of Automatic Weather Stations (AWS) and similar products derived from other satellite imagers. The result shows a drastic improvement in wide area, especially northern forest area in Japan and Russia. This study presents details, benefits and validation results of our algorithm (for further information, please see https://www.data.jma.go.jp/mscweb/en/product/library/note/index.html ).
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.H21N1951Y
- Keywords:
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- 0798 Modeling;
- CRYOSPHERE;
- 1655 Water cycles;
- GLOBAL CHANGE;
- 1855 Remote sensing;
- HYDROLOGY;
- 1863 Snow and ice;
- HYDROLOGY