| Autor: |
Kahn, Brian, Bertossa, Cameron, Chen, Xiuhong, Drouin, Brian, Hokanson, Erin, Huang, Xianglei, L'Ecuyer, Tristan, Mattingly, Kyle, Merrelli, Aronne, Michaels, Tim, Miller, Nate, Donat, Federico, Maestri, Tiziano, Martinazzo, Michele |
| Předmět: |
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| Zdroj: |
EGUsphere; 11/8/2023, p1-31, 31p |
| Abstrakt: |
We describe a cloud mask simulation experiment developed for the PREFIRE mission. The basis of the cloud mask is a principal component (PC) methodology (PC-MSK) adapted from the algorithm heritage of the upcoming Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission. Simulated clear-sky and cloudy-sky PREFIRE radiances are calculated from the Goddard Earth Observing System (GEOS) meteorological fields and include a variety of complex cloud configurations. The simulation experiment is based on local training that is adjusted along segments of simulated orbits that mimic actual PREFIRE orbits. A numerically stable method of separating clear sky from cloudy sky is achieved using Otsu's binary classification method and requires no a priori thresholding estimate for multimodal histograms. Comparisons are made against a machine-learning cloud mask (ML-MSK) developed for the PREFIRE mission. The global hit rate of PC-MSK (92.6 %) compares favorably to the hit rate of ML-MSK (95.3 %). The Arctic hit rate of PC-MSK (86.7 %) compares favorably to the hit rate of ML-MSK (89.4 %) and both cloud masks are shown to meet mission requirements for PREFIRE cloud detection. The simulation experiment demonstrates the potential for accurate cloud masking with PREFIRE despite a low number of information-containing PCs compared to those obtained from hyperspectral infrared sounders. We conclude with a discussion about clear-sky and cloudy-sky training sets that are suitable for an operational version of PC-MSK and their development during the post-launch checkout time period. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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