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- W2258904898 abstract "Algorithms for retrieval of geophysical parametersfrom radiances measured by instruments onboard satellites play alarge role in helping scientists monitor the state of the planet.Current retrieval algorithms based on neural networks are superiorin accuracy and speed compared to physics-based algorithms likeiterated minimum variance (IMV). However, they do not have any formof error estimation, unlike IMV. This thesis examines thesuitability of several different approaches to adding in confidenceintervals and other methods of error estimation to the retrievalalgorithm, as well as alternative machine learning methods that canboth retrieve the parameters desired and assign error bars. Testdatasets included both current generation operational instrumentslike AIRS/AMSU, as well as a hypothetical future hyper- spectralmicrowave sounder. Mixture density networks (MDN) and Sparse PseudoInput Gaussian processes (SPGP) were found to be the most accurateat variance prediction. Both of these are novel methods in thefield of remote sensing. MDNs also had similar training and testingtime to neural networks, while SPGPs often took three times as longto train in typical cases. As a baseline, neural networks trainedto estimate variance were also tested, but found to be lacking inaccuracy and reliability compared to the othermethods." @default.
- W2258904898 created "2016-06-24" @default.
- W2258904898 creator A5080906883 @default.
- W2258904898 date "2010-01-01" @default.
- W2258904898 modified "2023-10-16" @default.
- W2258904898 title "Improved uncertainty estimates for geophysical parameter retrieval" @default.
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