Hybrid cloud and error masking to improve the quality of deterministic satellite sea surface temperature retrieval and data coverage

Koner, Prabhat K.; Harris, Andrew; Maturi, Eileen

doi:10.1016/j.rse.2015.12.015

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Hybrid cloud and error masking to improve the quality of deterministic satellite sea surface temperature retrieval and data coverage

2016
By Koner, Prabhat K. ; Harris, Andrew ; Maturi, Eileen
Source: Remote Sensing of Environment 174, 266–278

[PDF-2.87 MB]

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Journal Title:

Remote Sensing of Environment
Personal Author:

Koner, Prabhat K. ; Harris, Andrew ; Maturi, Eileen

Koner, Prabhat K. ; Harris, Andrew ; Maturi, Eileen Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CICSM (Cooperative Institute for Climate and Satellites Maryland)

NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) ; CICSM (Cooperative Institute for Climate and Satellites Maryland) Less -
Description:

In the infrared region, the quality of sea surface temperature (SST) retrievals critically depends on the cloud detection scheme. More than 5 million matchups, where the surface and top of atmosphere measurements are available, have been carefully analyzed to understand clouds related errors and to develop the advanced cloud detection scheme for improvement of satellite SST quality. The effectiveness of a Bayesian cloud detection (BCD) scheme, operationally implemented at the NOAA Office of Satellite Product Operations (OSPO) for the GOES-Imager, has been examined using an experimental filter and it is found that this scheme is not optimal. Thus, a new algorithm for cloud and error masking (CEM) scheme is proposed for physical SST retrievals.
Keywords:

[+]

Bayesian Cloud Detection Cloud Detection GOES-13 Imager Modified Total Least Squares Radiative Transfer Model Sea Surface Temperature Spectral Differences Method
Source:

Remote Sensing of Environment 174, 266–278
DOI:

https://doi.org/10.1016/j.rse.2015.12.015
Document Type:

Journal Article
Funding:

Grant no. NA09ES4400006
Rights Information:

CC BY-NC-ND
Compliance:

Submitted
Main Document Checksum:

[+]

urn:sha256:217d264cb384bf1855c2eb01e4a8fc44c82ca3444d962280fbb3bbb13c009786
Download URL:

https://repository.library.noaa.gov/view/noaa/41023/noaa_41023_DS1.pdf
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Hybrid cloud and error masking to improve the quality of deterministic satellite sea surface temperature retrieval and data coverage

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