GAUSSIAN PROCESSES FOR SATELLITE DATA
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Authors
Martin, David W.
Subjects
machine learning
artificial intelligence
atmospheric science
Gaussian processes
GP
convolutional neural network
CNN
Bayesian neural network
BNN
artificial intelligence
atmospheric science
Gaussian processes
GP
convolutional neural network
CNN
Bayesian neural network
BNN
Advisors
Orescanin, Marko
Date of Issue
2023-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Using convolutional neural networks (CNNs) on satellite data sets outperforms previous meteorological methods for classifying weather events from space-based sensor data. However, CNNs alone lack the ability to quantify the uncertainty about their prediction given the input to the model. Gaussian processes (GPs) are a mathematical technique for making predictions while providing an estimate of the functional uncertainty about the underlying model; however, they are more computationally expensive to train than traditional CNNs. We train a ResNet50 CNN augmented with GPs against a large satellite dataset but test the model across not only held out data from the training dataset but also two other large datasets of a tropical cyclone and mesoscale convective system to test model performance across a variety of weather events. We compare the accuracy of a ResNet50 CNN augmented with GPs for the output layer of the neural network to the Goddard Profiling Algorithm, a differential equation-based approach that is not based on neural networks, a fully-deterministic neural network, as well as several Bayesian neural networks (BNNs) and find that Gaussian Processes outperform the GPROF and deterministic approaches but fall short of the top-end BNN results. Additionally, more work is needed to compare if the uncertainty estimates from the GPs are better calibrated than the uncertainty estimates from the BNNs.
Type
Thesis
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Series/Report No
Department
Computer Science (CS)
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NPS Report Number
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Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.