Repository logo
 

Data-driven models for subseasonal cyclogenesis forecasts in the east Pacific and north Atlantic

Simple item page
dc.contributor.authorCarlo Frontera, Zaibeth, author
dc.contributor.authorBarnes, Elizabeth A., advisor
dc.contributor.authorMaloney, Eric, advisor
dc.contributor.authorAnderson, G. Brooke, committee member
dc.date.accessioned2024-09-09T20:51:04Z
dc.date.available2024-09-09T20:51:04Z
dc.date.issued2024
dc.description.abstractTropical cyclones (TCs) are hazardous and financially burdensome meteorological events. Previous studies have revealed that longer timescale phenomena, including the El NiƱo Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and African Easterly Waves, influence TC development by modifying large-scale environmental conditions such as vertical wind shear, mid-level moisture, and sea surface temperatures. Statistical models have been developed to forecast TCs in the Atlantic and Pacific basins by incorporating information about ENSO and the MJO. Expanding on this work, we employ logistic regression (LR) and neural network (NN) models with an extended set of variables to predict cyclogenesis on subseasonal timescales for the east Pacific and Atlantic regions. These models utilize ENSO and MJO indices, along with other local environmental information, and demonstrate enhanced forecasting skill relative to models that only use TC climatology. Overall, the NN model shows superior performance compared to the LR model, retaining skill out to three weeks leadtime for the east Pacific, and out to four weeks for the Atlantic basin. The predictive capabilities of the model are demonstrated for the years 1983 and 2021. To gain insights into the decision-making process of the NN models, an AI explainability technique is employed to understand which features are considered important in making the predictions. For both basins, the addition of ENSO and MJO information prove to be essential for the superior forecast skill of the NN model.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierCarloFrontera_colostate_0053N_18386.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239109
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectmachine learning
dc.subjecttropical cyclones
dc.subjectneural network
dc.subjectlogistic regression
dc.titleData-driven models for subseasonal cyclogenesis forecasts in the east Pacific and north Atlantic
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineAtmospheric Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
CarloFrontera_colostate_0053N_18386.pdf
Size:
23.91 MB
Format:
Adobe Portable Document Format
Download

Collections

2020-
Theses and Dissertations