Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

Krasnopolsky, Vladimir; Nadiga, Sudhir; Mehra, Avichal; Bayler, Eric; Behringer, David

doi:10.1155/2016/6156513

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The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners. As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.

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Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

2016
By Krasnopolsky, Vladimir ; Nadiga, Sudhir ; Mehra, Avichal ; ...
Source: Computational Intelligence and Neuroscience, 9.

[PDF-2.43 MB]

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Journal Title:

Computational Intelligence and Neuroscience
Personal Author:

Krasnopolsky, Vladimir ; Nadiga, Sudhir ; Mehra, Avichal ; Bayler, Eric ; Behringer, David

Krasnopolsky, Vladimir ; Nadiga, Sudhir ; Mehra, Avichal ; Bayler, Eric ; Behringer, David Less -
NOAA Program & Office:

NWS (National Weather Service) ; NESDIS (National Environmental Satellite, Data, and Information Service)

NWS (National Weather Service) ; NESDIS (National Environmental Satellite, Data, and Information Service) Less -
Description:

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived "ocean color" (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAA's operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed-signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NN's generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.
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Computational Intelligence and Neuroscience, 9.
DOI:

https://doi.org/10.1155/2016/6156513
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Journal Article
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CC BY
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urn:sha256:03383642e775362c07d061838105b822279f9c3e9500f5ccb3f271364de7e293
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Neural Networks Technique for Filling Gaps in Satellite Measurements: Application to Ocean Color Observations

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