WaComM: A Parallel Water Quality Community Model for Pollutant Transport and Dispersion Operational Predictions

Abstract

Accurate prediction of trends in marine pollution is strategic, given the negative effects of low water quality on human marine activities. We describe here the computational and functional performance evaluation of a decision making tool that we developed in the context of an operational workflow for food quality forecast and assessment. Our Water Community Model (WaComM) uses a particle-based Lagrangian approach relying on tridimensional marine dynamics field produced by coupled Eulerian atmosphere and ocean models. WaComM has been developed matching the hierarchical parallelization design requirements and tested in Intel X86-64 and IBM P8 multi core environments and integrated in FACE-IT Galaxy workflow. The predicted pollutant concentration and the amount of pollutants accumulated in the sampled mussels are compared in search of coherent trends to prove the correct model behaviour. In the case study shown in this paper, the predicted Lagrangian tracers, acting as pollutant concentration surrogates, tend to spread rapidly and undergo rapid dilution as expected depending on dominant water column integrated currents

Shoreline Rotation Analysis of Embayed Beaches by Means of In Situ and Remote Surveys

“…Hence, an integrated approach between different remote sensing tools investigated in this study to monitor shoreline rotation of embayed beaches is suggested that combines continuous and updated long-term remote sensing observations, together with in situ UAV, video-camera system and GPS measurement campaigns. Using the findings of this work, we are confident that we can implement our shoreline measures with modeling scenarios (i.e., nearshore wave model simulations or sediment transport) using GPGPU [55,56]-based high-performance cloud computing [57] as already done with sea pollutant transport and diffusion [58] and wave run-up forecast [8].…”

Vessel to shore data movement through the Internet of Floating Things: A microservice platform at the edge

“…Moreover, leveraging on more sophisticated parallelization techniques 60 and accelerated infrastructures and devices, 61 the final goal is to build an incremental data set of coastal marine environmental data, 62 characterized by a consistent data georeferencing, 63 with a 2‐fold utilization: (i) training the next generation of deep learning models to carry out useful information for strategical resources management 64 and (ii) providing assimilation data for predicting and simulating models 65,66 and workflows 67‐69 …”

“…training the next generation of deep learning models to carry out useful information for strategical resources management 64 and (ii) providing assimilation data for predicting and simulating models 65,66 and workflows. [67][68][69]…”

A scalable Kalman filter algorithm: Trustworthy analysis on constrained least square model

“…VAR‐KF will be decomposed by using the proposed DD framework. This means that, any interest reader, who wants to apply the DD framework in a real‐world application, for instance, in References 9,10, that is, with a PDE‐based model state and an observation mapping, once the dynamic PDE‐based model state has been discretized, he should rewrite the state estimation problem under consideration as a CLS model problem (cfr Section 2.2) and apply the KF‐CLS algorithm (cfr Section 2.3). In other words, she/he should follow the discretize‐then‐optimize approach, common to most data assimilation problems and state estimation problems, before employing the DD‐KF framework.…”