Probabilistic Storm Surge Estimation for Landfalling Hurricanes: Advancements in Computational Efficiency Using Quasi-Monte Carlo Techniques

Abstract

During landfalling tropical storms, predictions of the expected storm surge are critical for guiding evacuation and emergency response/preparedness decisions, both at regional and national levels. Forecast errors related to storm track, intensity, and size impact these predictions and, thus, should be explicitly accounted for. The Probabilistic tropical storm Surge (P-Surge) model is the established approach from the National Weather Service (NWS) to achieve this objective. Historical forecast errors are utilized to specify probability distribution functions for different storm features, quantifying, ultimately, the uncertainty in the National Hurricane Center advisories. Surge statistics are estimated by using the predictions across a storm ensemble generated by sampling features from the aforementioned probability distribution functions. P-Surge relies, currently, on a full factorial sampling scheme to create this storm ensemble, combining representative values for each of the storm features. This work investigates an alternative formulation that can be viewed as a seamless extension to the current NHC framework, adopting a quasi-Monte Carlo (QMC) sampling implementation with ultimate goal to reduce the computational burden and provide surge predictions with the same degree of statistical reliability, while using a smaller number of sample storms. The definition of forecast errors adopted here directly follows published NWS practices, while different uncertainty levels are considered in the examined case studies, in order to offer a comprehensive validation. This validation, considering different historical storms, clearly demonstrates the advantages QMC can offer.

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

“…Each possible permutation is used where each TC event is assigned a weight based on the combined probability and the probabilistic result is determined through summation of the weighted model results. This leads to 63 TC events per CT perturbation, or ∼400-900 TC events based on 7 to 15 CT perturbations (Kyprioti et al 2021a), which would be prohibitive for HSOFS in a resource and time-limited environment.…”

“…To this end, Davis et al (2010) divided the range of the TC track errors into equal-area bins depending on a user-defined priority level, and estimated that 27 ensemble members resolved 90% of inundation. Additionally, Kyprioti et al (2021a) showed that quasi-Monte Carlo methodologies can be used to improve sampling efficiency of TC parameter errors over the full factorial approach used by P-Surge. However, there may still be limitations in the information available from smaller model ensembles [𝑂 (10)] that we aim for here.…”

An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions

“…Interested readers can found additional information for hurricane physics and modeling in [21]. These numerical tools can be ultimately used to accommodate deterministic and probabilistic approaches for establishing storm surge predictions [22,23,24,25,6,26].…”

“…Unfortunately, the computational burden of such numerical models is large, requiring thousands of CPU hours for each simulation, something that limits their applicability for real-time surge forecasting (during landfalling events) or regional probabilistic flood studies. Due to this computational complexity, such models can be utilized to provide only a small number of high-fidelity, deterministic predictions, but cannot easily accommodate thousand-run storm ensembles, for example for examining the impact of forecast errors [6] in the predicted track during landfalling events. This dramatically limits their utility for decision makers either in emergency response management (during landfalling events) or regional planning (long-term projection of storm impact) settings.…”

Identification of Landslide types in cliffs between Ancon and Anconcito, Santa Elena, Ecuador

“…Un tema de impacto para poblaciones y autoridades de todo el mundo es la erosión que se presenta en los acantilados costeros (Conforti et al, 2014;De la Peña & Sánchez, 2018;López & Vidal, 2012;Ordoqui & Hernández, 2009;Del Río & Gracia 2014) y la pérdida de terrenos montañosos (Milheiro, 2007), varios son los factores que influyen en la vulnerabilidad de las zonas costeras (Azuz et al, 2020;Merlotto et al, 2017;Ricaurte et al, 2021), considerándose como detonante a la intensidad y frecuencia de las precipitaciones (Bezerra et al, 2011;Kyprioti et al, 2021;Lemke & Miller, 2021;Useros, 2013). , y aumento acelerado del nivel del mar (Evelpidou et al, 2021;Louisor et al, 2021;Silveira et al, 2021;Wu et al, 2002).…”