Meteorology Commons^™

Modeling Hourly Storm Surges Using Deep Learning Techniques, Md Abu Zafor

Master's Theses

Storm surges cause coastal flooding, one of the most devastating coastal hazards. Accurate modeling of storm surges is essential for predicting and mitigating these impacts. Traditional approaches model surges at individual tide gauges and often focus on daily time scales, leading to data redundancy and limiting their ability to capture sub-daily variability. This study addresses these limitations using deep learning (DL) algorithms to model hourly surges simultaneously at multiple tide gauges along the U.S. East Coast and Gulf of Mexico. Three algorithms, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid ConvLSTM are employed to model surges using …

A Road Map To Indoos-2: Better Observations Of The Rapidly Warming Indian Ocean, L. M. Beal, J. Vialard, M. K. Roxy, J. Li, M. Andres, H. Annamalai, M. Feng, W. Han, R. Hood, T. Lee, M. Lengaigne, R. Lumpkin, Y. Masumoto, M. J. Mcphaden, M. Ravichandran, T. Shinoda, B. M. Sloyan, P. G. Strutton, A. C. Subramanian, T. Tozuka, C. C. Ummenhofer, A. S. Unnikrishnan, J. Wiggert, L. Yu, L. Cheng, D. G. Desbruyères, V. Parvathi

Faculty Publications

The Indian Ocean Observing System (IndOOS), established in 2006, is a multinational network of sustained oceanic measurements that underpin understanding and forecasting of weather and climate for the Indian Ocean region and beyond. Almost one-third of humanity lives around the Indian Ocean, many in countries dependent on fisheries and rain-fed agriculture that are vulnerable to climate variability and extremes. The Indian Ocean alone has absorbed a quarter of the global oceanic heat uptake over the last two decades and the fate of this heat and its impact on future change is unknown. Climate models project accelerating sea level rise, more …

On The Approximation Of The Inverse Error Covariances Of High-Resolution Satellite Altimetry Data, Max Yaremchuk, Joseph M. D'Addezio, Gleb Panteleev, Gregg Jacobs

Faculty Publications

© 2018 Royal Meteorological Society High-resolution (swath) altimeter missions scheduled to monitor the ocean surface in the near future have observation-error covariances (OECs) with slowly decaying off-diagonal elements. This property presents a challenge for the majority of the data assimilation algorithms which were designed under the assumption of the diagonal OECs being easily inverted. In this note, we present a method of approximating the inverse of a dense OEC by a sparse matrix represented by the polynomial of spatially inhomogeneous differential operators, whose coefficients are optimized to fit the target OEC by minimizing a quadratic cost function. Explicit expressions for …

Concorde Meteorological Analysis (Cma) - Data Guide, Patrick Fitzpatrick, Yee H. Lau

Faculty Publications

CONCORDE is the CONsortium for oil spill exposure pathways in COastal River-Dominated Ecosystems (CONCORDE), and is an interdisciplinary research program funded by the Gulf of Mexico Research Initiative (GoMRI) to conduct scientific studies of the impacts of oil, dispersed oil and dispersant on the Gulf’s ecosystem (Greer et al. 2018). A CONCORDE goal is to implement a synthesis model containing circulation and biogeochemistry components of the Northern Gulf of Mexico shelf system which can ultimately aid in prediction of oil spill transport and impacts.

The CONCORDE Meteorological Analysis (CMA) is an hourly gridded NetCDF dataset which provides atmospheric forcing for …

Climate Process Team On Internal Wave-Driven Ocean Mixing, Jennifer A. Mackinnon, Zhongxiang Zhao, Caitlin B. Whalen, Amy F. Waterhouse, David S. Trossman, Oliver M. Sun, Louis C. St. Laurent, Harper L. Simmons, Kurt Polzin, Robert Pinkel, Andrew Pickering, Nancy J. Norton, Jonathan D. Nash, Ruth Musgrave, Lynne M. Merchant, Angelique V. Melet, Benjamin Mater, Sonya Legg, Willima G. Large, Eric Kunze, Jody M. Klymak, Markus Jochum, Steven R. Jayne, Robert W. Hallberg, Stephen M. Griffies, Steve Diggs, Gokhan Danabasoglu, Eric P. Chassignet, Maarten C. Buijsman, Frank O. Bryan, Bruce P. Briegleb, Andrew Barna, Brian K. Arbic, Joseph K. Ansong, Matthew H. Alford

Faculty Publications

The study summarizes recent advances in our understanding of internal wave–driven turbulent mixing in the ocean interior and introduces new parameterizations for global climate ocean models and their climate impacts.

Using Proxy Records To Document Gulf Of Mexico Tropical Cyclones From 1820-1915, Jordan V. Pino, Robert V. Rohli, Kristine L. Delong, Grant L. Harley, Jill C. Trepanier

Faculty Publications

Observations of pre-1950 tropical cyclones are sparse due to observational limitations; therefore, the hurricane database HURDAT2 (1851–present) maintained by the National Oceanic and Atmospheric Administration may be incomplete. Here we provide additional documentation for HURDAT2 from historical United States Army fort records (1820–1915) and other archived documents for 28 landfalling tropical cyclones, 20 of which are included in HURDAT2, along the northern Gulf of Mexico coast. One event that occurred in May 1863 is not currently documented in the HURDAT2 database but has been noted in other studies. We identify seven tropical cyclones that occurred before 1851, three of which …

Coastal Hurricane Damage Assessment Via Wavelet Transform Of Remotely Sensed Imagery, Ricky Carl Crowsey

Dissertations

This dissertation uses post storm imagery processed using wavelet transforms to investigate the capability of wavelet transform-based methods to classify post storm damage of residential areas. Five level Haar, Meyer, Symlets, and Coiflets wavelet transform decompositions of the post storm imagery are inputs to damage classification models of post hurricane and tornado damage. Hurricanes Ike, Rita, Katrina, and Ivan are examined as are the 2011 Joplin and Tuscaloosa tornadoes.

Wavelet transform-based classification methods yielded varying classification accuracies for the four hurricanes examined, ranging from 67 percent to 89 percent classification accuracy for classification models informed by samples from the storms …