Correction of Back Trajectories Utilizing Machine Learning

Abstract

The goal of this work was to analyze 24-hour back trajectory performance from a global, low-resolution weather model compared to a high-resolution limited area weather model in particular meteorological regimes, or flow patterns using K-means clustering, an unsupervised machine learning technique. The duration of this study was from 2015-2019 for the contiguous United States (CONUS). Three different machine learning algorithms were tested to study the utility of these methods improving the performance of the CFS relative to the performance of the RAP. The aforementioned machine learning techniques are linear regression, Bayesian ridge regression, and random forest regression. These results mean reducing computational time for the user. Additionally, the greatest improvement of CFS values occurred in July, August, and September.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2021
Accession Number
AD1146028

Entities

People

  • Britta F. Gjermo Morrison

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Atmospheric Chemistry
  • Case Studies
  • Data Sets
  • Department Of Defense
  • Geography
  • High Resolution
  • Information Science
  • Low Resolution
  • Machine Learning
  • Materials
  • Measurement
  • Neural Networks
  • North America
  • Pattern Recognition
  • Public Health
  • Recurrent Neural Networks
  • United States
  • Unsupervised Machine Learning
  • Urban Areas

Fields of Study

  • Computer science

Readers

  • Climatology
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks