Abstract
The Advanced Scatterometer (ASCAT) is a satellite-based remote sensing instrument designed for measuring wind speed and direction over the Earth's oceans. This thesis aims to expand and improve the capabilities of ASCAT by adding rain detection and advancing wind retrieval. Additionally, this expansion to ASCAT serves as evidence of Artificial Intelligence (AI) techniques learning both novel and traditional methods in remote sensing. I apply semantic segmentation to ASCAT measurements to detect rain over the oceans, enhancing capabilities to monitor global precipitation. I use two common neural network architectures and train them on measurements from the Tropical Rainfall Measuring Mission (TRMM) collocated with ASCAT measurements. I apply the same semantic segmentation techniques on wind retrieval in order to create a machine learning model that acts as an inverse Geophysical Model Function (GMF). I use three common neural network architectures and train the models on ASCAT data collocated with European Centre for Medium-Range Weather Forecasts (ECMWF) wind vector data. I successfully increase the capabilities of the ASCAT satellite to detect rainfall in Earth's oceans, with the ability to retrieve wind vectors without a GMF or Maximum Likelihood Estimation (MLE).
Degree
MS
College and Department
Ira A. Fulton College of Engineering; Electrical and Computer Engineering
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
McKinney, Matthew Yoshinori Otani, "Ocean Rain Detection and Wind Retrieval Through Deep Learning Architectures on Advanced Scatterometer Data" (2024). Theses and Dissertations. 10455.
https://scholarsarchive.byu.edu/etd/10455
Date Submitted
2024-06-18
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd13293
Keywords
GMF, wind retrieval, rain detection, deep learning, ASCAT
Language
english