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Articles 1 - 3 of 3
Full-Text Articles in Meteorology
Unsupervised Machine Learning Of Tornado-Producing Storms In The Southeastern United States, Morgan R. Steckler
Unsupervised Machine Learning Of Tornado-Producing Storms In The Southeastern United States, Morgan R. Steckler
Masters Theses
The east-southeastern US is uniquely affected by storm and tornado-related damages, costs, injuries, and deaths. Based on doppler radar, satellite, and modeled data, previous research sought to understand these different types of storms that produce strong tornadoes. Many approaches to storm classification are time intensive, complex, and vary significantly across the literature. The purpose of this work is to (1) explore the radar-derived data structure and spread of strong tornado-producing mesoscale storms in the east-southeastern US; (2) use K-Means unsupervised machine learning methods to elucidate clusters (storm types) and clustering attributes; and (3) assess the utility of K-Means as a …
Information Technology Implementation Decisions To Support The Kentucky Mesonet, D. Michael Grogan
Information Technology Implementation Decisions To Support The Kentucky Mesonet, D. Michael Grogan
Masters Theses & Specialist Projects
The Kentucky Mesonet is a high-density, mesoscale network of automated meteorological and climatological sensing platforms being developed across the commonwealth. Data communications, collection, processing, and delivery mechanisms play a critical role in such networks, and the World Meteorological Organization recognizes that “an observing system is not complete unless it is connected to other systems that deliver the data to the users.” This document reviews the implementation steps, decisions, and rationale surrounding communications and computing infrastructure development to support the Mesonet. A general overview of the network and technology-related research is provided followed by a review of pertinent literature related to …
Data Warehouse Techniques To Support Global On-Demand Weather Forecast Metrics, Meriellen C. Joga
Data Warehouse Techniques To Support Global On-Demand Weather Forecast Metrics, Meriellen C. Joga
Theses and Dissertations
Air Force pilots and other operators make crucial mission planning decisions based on weather forecasts; therefore, the ability to forecast the weather accurately is a critical issue to Air Force Weather (AFW) and its customers. The goal of this research is to provide Air Force Weather with a methodology to automate statistical data analysis for the purpose of providing on-demand metrics. A data warehousing methodology is developed and applied to the weather metrics problem in order to present an option that will facilitate on-demand metrics. On-line analytical processing (OLAP) and data mining solutions are also discussed.