Date of Award
12-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Ocean Engineering and Marine Sciences
First Advisor
Steven M. Lazarus, Ph.D.
Second Advisor
Pallav Ray, Ph.D.
Third Advisor
Charles R. Bostater, Ph.D.
Fourth Advisor
Jean-Paul Pinelli, Ph.D., P.E.
Abstract
This project was developed to support the Florida Tech Wireless Sensor Network System (WSNS) hurricane research team in determining wind loads during high-impact wind events. The WSNS team objectives included estimating the critical pressure (𝐶p) from observed 3-s gusts during high impact weather events (tropical cyclones). The field work was conducted at a single-story residential home in Satellite Beach, FL approximately 400 m from the coast. During data collection periods, more than 1-million 20-second LiDAR wind profiles were observed.
The bulk roughness at the test residence is characterized utilizing an existing morphometric technique with geometry that is derived from a new, and untested, 3- dimensional (3D) building dataset and augmented by canopy vegetation information. A unique approach is taken where building height is estimated from property appraiser data. Roughness was calculated for varying upstream footprints with and without vegetation data. For small upstream footprints with relatively shallow LiDAR observed internal boundary layers (IBLs, on the order of 40 m), the morphometric effective roughness was found to be largest for onshore flow due to large structures (i.e., condominiums) along the coast. This underscores the importance of the contribution of the upstream elements for the short (land- based) fetch associated with onshore flow.
Statistical and machine learning approaches (generalized additive model (GAM) and random forest (RF)) were applied to the roughness values along with LiDAR, anemometer, and nearby ASOS winds to investigate whether the peak 3-s gust can be predicted from observed and/or estimated quantities. Two metrics were used, namely the mean standard error and the number of observations which fell within one standard error of the prediction. The best performing GAM was trained using 2 continuous and 3 categorical variables. Important variables included 50 m LiDAR winds, ASOS winds adjusted to the local morphometric roughness, IBL height and morphometric roughness. The best performing GAM without LiDAR data was trained using 3 continuous and 4 categorical variables. These variables included the ASOS gust factor, wind direction, and wind speed adjusted by local morphometric roughness as well as stability, IBL height, and effective morphometric roughness.
The variable combinations that produced the best GAM results were then used to train and evaluate RF models from the same datasets. LiDAR winds tend to degrade the overall RF performance when compared to the ASOS based RF model – this is due, in part, to LiDAR data loss from power outages during peak storm intensity.
Recommended Citation
Besing, Hadley Rose, "Morphometric Based Roughness Estimates for Wind Loading Applications in the Coastal Transition Zone" (2024). Theses and Dissertations. 1511.
https://repository.fit.edu/etd/1511