EMULATING PASSIVE MICROWAVE OBSERVATIONS WITH PATCH-TO-PIXEL CONVOLUTIONAL NEURAL NETWORKS
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Authors
Hall, Micky S.
Subjects
neural network
GMI
ABI
CNN
satellite
GMI
ABI
CNN
satellite
Advisors
Orescanin, Marko
Date of Issue
2022-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Geostationary (GEO) satellites such as the GOES constellation are equipped with Advanced Baseline Imager (ABI) sensors that have a very high temporal resolution with a very low spatial resolution and provide visible through infrared data every 15 minutes. In contrast, Low Earth Orbit (LEO) satellites with Global Precipitation Measurement Microwave Imager (GMI) sensors have very high spatial resolution with a low temporal resolution that provide data as infrequently as every 15 hours. The purpose of this research is to study the viability of using the ABI data to regress to a synthetic GMI dataset. Specifically, the focus is on improving the ability to make predictions on the under-represented data points within our dataset and being able to generalize well to future distributions of data. This thesis has created a sampling technique that combines over and under sampling in conjunction with a purpose-built Residual Neural Network to perform regression from multi-spectral ABI data to a single GMI channel. In doing so, we prove that it is possible to predict under-represented values more accurately in datasets when using our sampling method and to generalize well to future data. Using our approach, we predict within 5 Kelvin for 34.5% of the tail of the test data compared to only 24.4% when we used an unsampled dataset. We also are able to prevent our mean absolute error from rising by 1 Kelvin when measured across three test datasets that span a timeframe of five months.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
Identifiers
NPS Report Number
Sponsors
Office of Naval Research, One Liberty Center, 875 N. Randolph Street, Suite 1425 Arlington, VA 22203-1995
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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.