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Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm.
- Source :
-
Atmospheric Research . Oct2021, Vol. 261, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. This study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain. • Proposed a geostationary satellite cloud top phase classification algorithm (MIA). • Cloud types classified by MIA include ice, supercooled water, and warm liquid water. • Performance decreases in multi-layer cloud scenario by using CALIOP as benchmark. • Overall hit rate of MIA outperforms current GOES-16 cloud top phase product. [ABSTRACT FROM AUTHOR]
- Subjects :
- *GEOSTATIONARY satellites
*REMOTE-sensing images
*ALGORITHMS
*FOREST mapping
Subjects
Details
- Language :
- English
- ISSN :
- 01698095
- Volume :
- 261
- Database :
- Academic Search Index
- Journal :
- Atmospheric Research
- Publication Type :
- Academic Journal
- Accession number :
- 151589265
- Full Text :
- https://doi.org/10.1016/j.atmosres.2021.105767