Long-range temperature forecasting correction techniques Using machine learning
Name
1192545182-MIT.pdf
Size
1.98 MB
Format
Adobe PDF
Checksum (MD5)
3771cf70f4795d009c6d801f09128918
Author(s)
Grossman, Alexander G.
Advisor(s)
Srinivas Ravela.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
We present solutions to four problems emerging in data-driven long-range weather prediction that were explored as part of an M.Eng Thesis. These problems are related to long-range prediction using a network of observing stations and climate indicators. The first problem relates to the correction of phase error in long-term temperature forecasts. The second problem involves the task of using correlated observed and proxy signals to update each other to improve forecasting accuracy. The third problem relates to the use of deep learning in the problem of predicting the future value of near oscillators. The fourth problem relates to the discovery of new, finer scale oscillation signals using Representation Learning based Dimensionality Reduction techniques. Together, our proposed solutions enable the use of inference and learning for data-driven long-range weather forecasting using context from the global climate system.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 53-56).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
Persistent DSpace Link
