Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation
Abstract
Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when comparedmore »
- Authors:
-
[1];
[4];
- a Environmental Science Division, Argonne National Laboratory, Lemont, Illinois
- b NOAA/Coast Survey Development Laboratory, Silver Spring, Maryland, c University Corporation for Atmospheric Research, Boulder, Colorado
- d Sandia National Laboratories, Livermore, California
- b NOAA/Coast Survey Development Laboratory, Silver Spring, Maryland
- Publication Date:
- Research Org.:
- Argonne National Laboratory (ANL), Argonne, IL (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Oceanic and Atmospheric Administration (NOAA)
- OSTI Identifier:
- 1971189
- Alternate Identifier(s):
- OSTI ID: 1991171
- Grant/Contract Number:
- AC02-06CH11357; NA0003525; NA19OAR0220123
- Resource Type:
- Published Article
- Journal Name:
- Artificial Intelligence for the Earth Systems
- Additional Journal Information:
- Journal Name: Artificial Intelligence for the Earth Systems Journal Volume: 2 Journal Issue: 2; Journal ID: ISSN 2769-7525
- Publisher:
- American Meteorological Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Ensembles; Machine learning; Probability forecasts/models/distribution; Storm surges; Tropical cyclones; Uncertainty
Citation Formats
Pringle, William J., Burnett, Zachary, Sargsyan, Khachik, Moghimi, Saeed, and Myers, Edward. Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation. United States: N. p., 2023.
Web. doi:10.1175/AIES-D-22-0040.1.
Pringle, William J., Burnett, Zachary, Sargsyan, Khachik, Moghimi, Saeed, & Myers, Edward. Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation. United States. https://doi.org/10.1175/AIES-D-22-0040.1
Pringle, William J., Burnett, Zachary, Sargsyan, Khachik, Moghimi, Saeed, and Myers, Edward. Sat .
"Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation". United States. https://doi.org/10.1175/AIES-D-22-0040.1.
@article{osti_1971189,
title = {Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation},
author = {Pringle, William J. and Burnett, Zachary and Sargsyan, Khachik and Moghimi, Saeed and Myers, Edward},
abstractNote = {Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.},
doi = {10.1175/AIES-D-22-0040.1},
journal = {Artificial Intelligence for the Earth Systems},
number = 2,
volume = 2,
place = {United States},
year = {Sat Apr 01 00:00:00 EDT 2023},
month = {Sat Apr 01 00:00:00 EDT 2023}
}
https://doi.org/10.1175/AIES-D-22-0040.1
Search WorldCat to find libraries that may hold this journal