Deep Learning for Weather Clustering and Forecasting
Abstract
Clustering weather data is a valuable endeavor in multiple respects. The results can be used within a larger weather prediction framework or could simply serve as an analytical tool for characterizing climatic differences of a particular region. This research proposes a methodology for clustering geographic locations based on the similarity in shape of their temperature time series. To this end an emerging and powerful class of clustering techniques that leverages deep learning, called deep representation clustering (DRC), are utilized. Moreover, a time series specific DRC algorithm is proposed that addresses a current gap in the field. Finally, deep learning based weather prediction is an increasingly common research topic as a means of obtaining more rapid predictions when compared to traditional numerical weather prediction (NWP). Since their are known physical equations that govern atmospheric behavior, namely the Navier-Stokes equations, the concept of reformulating these laws into a physics based loss function is explored with particular interest in whether a model trained with such a loss function can outperform its baseline counterpart.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2021
- Accession Number
- AD1149667
Entities
People
- Nathanael R. Beveridge
Organizations
- Air Force Institute of Technology