A Shoreline Alert Model for coastal early warning system in the Gulf of Naples (Italy)

The development and implementation of Early Warning Systems (EWSs) are decisive as they allow for timely measures before the arrival of the flooding waters. EWS enhances the prevention and preparedness activities that mitigate the effects of disasters on lives, property, and the environment. Forecasting outcomes supply decision-makers at local, regional, or national levels with relevant information and comprise an essential part of monitoring and warning procedures. These decisions must be taken quickly to allow mitigation measures, so computer time acceleration is decisive. 
The proposed innovation for implementing an Early Warning System (EWS) is the parallelization model. The proposed parallelization model is based on different parallel sub-schemes. Each parallelization sub-schema can be combinable with each other, providing a hierarchical parallelization scheme. The problem size (the number of transects along the Campania coast) is divided into lots and distributed to several executors. Each executor is an instance of a computer program (process) in charge of computing the partition of the problem in its duty. Due to CPUs being composed of more computing cores, each process can decompose its part of the problem to each computing core running concurrently (threads). While the threads of the same process share the same memory, processes communicate by exchanging data messages. As demonstrated later in the paper, the problem decomposition makes the overall computing performance remarkable as the problem size increase.
The EWS has been designed and developed, leveraging a high-performance computing system. This approach is motivated by the goal of managing and running scientific workflows. Therefore, the performance evaluation has been performed considering a production workflow executing diverse and different numerical and A.I. models): the community Weather Research and Forecasting (WRF),  the Wavewatch III (WW3) numerical models, and, finally, 3) the novel Shoreline Alert Model (SAM).
SAM implements the empirical approach to evaluate the alert level as a function of the shoreline characteristics. The workflow starts with the WRF numerical model to forecast the atmospheric forcing needed to fuel the WW3 model for estimating the offshore waves, which drives the initial and boundary conditions for modeling waves in shallow water. Then, according to the wave decay submodel, these conditions assess the run-up height and overtopping discharge. The results are associated with an alert system triggered by the duration and intensity of storm events forecasted by the models. Finally, it is obtained considering the geomorphology of the area of interest and the presence/absence of protection structures.
The case study under examination covers a coastal stretch located in the municipality of Torre del Greco, in the Gulf of Naples, consisting of a beach varying in width from 10 to 20 meters, which is protected by an artificial reef up of natural blocks. In recent years, the succession of extreme weather events has created coastal flooding, like during the extreme storm of October 2018, which caused considerable damage in this area.