Surviving Hurricane Celia
On the final days of July in 1970, a devastating storm swept over the Coastal Bend. The category 3 storm dubbed Hurricane Celia touched down in Aransas Pass at wind speeds estimated around 125 mph. Hurricane Celia left great devastation behind in its path, costing the lives of 15 Texas citizens and earning the title of “most costliest” tropical cyclone in Texas History until Hurricane Alice in 1983. Though much was lost in the wake of Celia, several stories of extraordinary human triumph rose from the rubble. This gallery of photos originates from a recounting of one man’s experience surviving Hurricane Celia. The veteran and boat captain, pictured in this gallery alongside meteorologist and Texas politician Maclovio Perez and other research scientists in Corpus Christi, retold the story of his survival amidst the storm; stranded in the water and unable to access safe shelter, the boat captain shielded himself in his boat until he reached a safe point of escape. His presence in the Aransas Pass marina was essential to further tropical cyclone research, as it allowed for institutes like the Coastal Dynamics Lab to find resources such as photos of the marina completely emptied by the force of Hurricane Celia. Without these tales of human perseverance, there would be little to say about Celia besides the devastation this stormed wrecked on the Coastal Bend. At CDL, documentation of severe weather events allows our researchers to develop models and procedures for risk evaluation in the future, preventing future loss at the magnitude caused by Celia.
New Article!
A new article, FogNet: A multiscale 3D CNN with double-branch dense block and attention mechanism for fog prediction, will be published by Elsevier in the September 15, 2021 volume of the Bulletin of the Machine Learning with Applications journal.
Abstract: The reduction of visibility adversely affects land, marine, and air transportation. Thus, the ability to skillfully predict fog would provide utility. We predict fog visibility categories below 1600 m, 3200 m and 6400 m by post-processing numerical weather prediction model output and satellite-based sea surface temperature (SST) using a 3D-Convolutional Neural Network (3D-CNN). The target is an airport located on a barrier island adjacent to a major US port; measured visibility from this airport serves as a proxy for fog that develops over the port. The features chosen to calibrate and test the model originate from the North American Mesoscale Forecast System, with values of each feature organized on a 32 × 32 horizontal grid; the SSTs were obtained from the NASA Multiscale Ultra Resolution dataset. The input to the model is organized as a high dimensional cube containing 288 to 384 layers of 2D horizontal fields of meteorological variables (predictor maps). In this 3D-CNN (hereafter, FogNet), two parallel branches of feature extraction have been designed, one for spatially auto-correlated features (spatial-wise dense block and attention module), and the other for correlation between input variables (variable-wise dense block and attention mechanism.) To extract features representing processes occurring at different scales, a 3D multiscale dilated convolution is used. Data from 2009 to 2017 (2018 to 2020) are used to calibrate (test) the model. FogNet performance results for 6, 12, and 24 hour lead times are compared to results from the High-Resolution Ensemble Forecast (HREF) system. FogNet outperformed HREF using 8 standard evaluation metrics.