Publication:
APPLICATIONS OF BAYESIAN NEURAL NETWORKS TO GLOBAL PRECIPITATION MEASUREMENT MISSION DATA

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Authors
Heslin, Sean C.
Subjects
artificial intelligence
remote sensing
tropical meteorology
convolutional neural networks
CNN
passive microwave
geostationary
synthetic radar
global precipitation measurement mission
GPM
Bayesian convolutional neural networks
BCNN
rain-type classification
GOES-16
Advisors
Powell, Scott
Orescanin, Marko
Date of Issue
2021-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Meteorological remote sensing efforts have advanced operational decision making and scientific research over the last half-century by providing high-quality global observations of the land, atmosphere, and ocean. The continued development of convolutional neural networks (CNNs) and Bayesian neural networks shows potential for allowing some of these datasets to be synthetically produced where they cannot be directly observed. In this thesis, global precipitation measurement mission (GPM) data is used to train a rain-type classification Bayesian CNN (BCNN) using passive microwave data. Additionally, regression CNNs and BCNNs are trained to predict precipitation using GOES-16 multispectral infrared data over a tropical maritime region. The rain-type classification BCNN shows a 17% improvement in accuracy over existing literature, and the regression models demonstrate a proof of concept in using GPM radar data and geostationary radiances to train skillful CNNs and BCNNs to predict radar reflectivity and rain rate. The experiments demonstrate both the promise of using these data sources to train accurate models and the possible advantages of using BCNNs to quantify and better understand prediction uncertainty for these applications.
Type
Thesis
Description
Series/Report No
Department
Meteorology (MR)
Other Units
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NPS Report Number
Sponsors
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Citation
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.
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