Determination of CERES TOA Fluxes Using Machine Learning Algorithms. Part I: Classification and Retrieval of CERES Cloudy and Clear Scenes Article Swipe
Bijoy V. Thampi
,
Takmeng Wong
,
Constantin Lukashin
,
Norman G. Loeb
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1175/jtech-d-16-0183.1
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1175/jtech-d-16-0183.1
Continuous monitoring of the earth radiation budget (ERB) is critical to the understanding of Earth’s climate and its variability with time. The Clouds and the Earth’s Radiant Energy System (CERES) instrument is able to provide a long record of ERB for such scientific studies. This manuscript, which is the first of a two-part paper, describes the new CERES algorithm for improving the clear/cloudy scene classification without the use of coincident cloud imager data. This new CERES algorithm is based on a subset of the modern artificial intelligence (AI) paradigm called machine learning (ML) algorithms. This paper describes the development and application of the ML algorithm known as random forests (RF), which is used to classify CERES broadband footprint measurements into clear and cloudy scenes. Results from the RF analysis carried using the CERES Single Scanner Footprint (SSF) data for January and July are presented in the manuscript. The daytime RF misclassification rate (MCR) shows relatively large values (>30%) for snow, sea ice, and bright desert surface types, while lower values (<10%) for the forest surface type. MCR values observed for the nighttime data in general show relatively larger values for most of the surface types compared to the daytime MCR values. The modified MCR values show lower values (<4%) for most surface types after thin cloud data are excluded from the analysis. Sensitivity analysis shows that the number of input variables and decision trees used in the RF analysis has a substantial influence on determining the classification error.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1175/jtech-d-16-0183.1
- OA Status
- green
- Cited By
- 14
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2741847760
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2741847760Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1175/jtech-d-16-0183.1Digital Object Identifier
- Title
-
Determination of CERES TOA Fluxes Using Machine Learning Algorithms. Part I: Classification and Retrieval of CERES Cloudy and Clear ScenesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-08-02Full publication date if available
- Authors
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Bijoy V. Thampi, Takmeng Wong, Constantin Lukashin, Norman G. LoebList of authors in order
- Landing page
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https://doi.org/10.1175/jtech-d-16-0183.1Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.ncbi.nlm.nih.gov/pmc/articles/7837512Direct OA link when available
- Concepts
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Algorithm, Daytime, Random forest, Footprint, Snow, Environmental science, Remote sensing, Scanner, Meteorology, Computer science, Artificial intelligence, Geology, Atmospheric sciences, Geography, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 4, 2022: 1, 2021: 4, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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