Center Fixing Tropical Depressions and Tropical Storms Using Machine Learning-Nighttime Visible Imagery

Author

Nathan K. Stanford

Date of Award

3-2024

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Engineering Physics

First Advisor

Chandra M. Pasillas, PhD

Abstract

The first step in most TC-retrieval algorithms is determining the storm’s central position. In mature TCs, the center is highlighted by a distinct eye and curved band pattern; however, in intensifying and decaying storms, the center is often obscured by thick clouds or overlying cirrus. This study assesses the benefits of incorporating machine learning-derived nighttime visual imagery to improve analysis of center fix in intensifying and decaying TD- and TS-strength TCs during periods of darkness and when polar orbiting satellites are unavailable. The study is divided into two parts: the first, an objective analysis using the ARCHER-2 algorithm, and the second, a subjective imagery analysis with the JTWC. For the objective analysis, 955 NVI, SWIR, and LWIR image sets, comprised of TD- and TS-strength cyclones, were ingested into ARCHER-2. Results showed that NVI improved center fix forecasts by 21.6 and 49.3 kilometers over SWIR and LWIR, respectively.

AFIT Designator

AFIT-ENP-MS-24-M-056

Comments

A 12-month embargo was observed for posting this work on AFIT Scholar.

Distribution Statement A, Approved for Public Release. PA case number on file.

Recommended Citation

Stanford, Nathan K., "Center Fixing Tropical Depressions and Tropical Storms Using Machine Learning-Nighttime Visible Imagery" (2024). Theses and Dissertations. 7787.
https://scholar.afit.edu/etd/7787