some remarks about a community open source lagrangian pollutant transport and dispersion model

Nowadays fishes and mussels farming is very important, from an economical point of view, for the local social background of the Bay of Naples. Hence, the accurate forecast of marine pollution becomes crucial to have reliable evaluation of its adverse effects on coastal inhabitants' health. The use of connected smart devices for monitoring the sea water pollution is getting harder because of the saline environment, the network availability and the maintain and calibration costs2. To this purpose, we designed and implemented WaComM (Water Community Model), a community open source model for sea pollutants transport and dispersion. WaComM is a model component of a scientific workflow which allows to perform, on a dedicated computational infrastructure, numerical simulations providing spatial and temporal high-resolution predictions of weather and marine conditions of the Bay of Naples leveraging on the cloud based31FACE-IT workflow engine27. In this paper we present some remarks about the development of WaComM, using hierarchical parallelism which implies distributed memory, shared memory and GPGPUs. Some numerical details are also discussed. Peer-review under responsibility of the Conference Program Chairs

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

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

Processing of crowd-sourced data from an internet of floating things

Sensors incorporated into mobile devices provide unique opportunities to capture detailed environmental information that cannot be readily collected in other ways. We show here how data from networked navigational sensors on leisure vessels can be used to construct unique new datasets, using the example of underwater topography (bathymetry) to demonstrate the approach. Specifically, we describe an end-to-end workflow that involves the collection of large numbers of timestamped (position, depth) measurements from "internet of floating things" devices on leisure vessels; the communication of data to cloud resources, via a specialized protocol capable of dealing with delayed, intermittent, or even disconnected networks; the integration of measurement data into cloud storage; the efficient correction and interpolation of measurements on a cloud computing platform; and the creation of a continuously updated bathymetric database. Our prototype implementation of this workflow leverages the FACE-IT Galaxy workflow engine to integrate network communication and database components with a CUDA-enabled algorithm running in a virtualized cloud environment

Applications of the FACE-IT portal and workflow engine for operational food quality prediction and assessment: Mussel farm monitoring in the Bay of Napoli, Italy

Mussel farm product quality remains a challenging problem for operational marine science. In an operational scenario, the model chain, orchestrated in a workflow fashion, produces a huge amount of predicted spatially-referenced (big) data. These workflow components have been integrated into the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT), a workflow engine and data science portal based on Galaxy and Globus technologies. We describe how FACE-IT workflows can be used to couple many simulation/prediction models, leveraging high-performance and cloud computing resources to enable fast full system modeling in order to produce operational predictions about the impact of pollutants spilled out from both natural and anthropic sources in mussels farming high density areas

Using the FACE-IT portal and workflow engine for operational food quality prediction and assessment: An application to mussel farms monitoring in the Bay of Napoli, Italy

The Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) is a workflow engine and data science portal based on Galaxy and Globus technologies that enables computational scientists to integrate data, pre/post processing and simulation into a framework that supports offline environmental model coupling. We describe how the FACE-IT workflows engine can be used to couple many simulation/prediction models, leveraging high-performance cloud computing resources to enable fast full system modeling and produce operational predictions about the impact of pollutants spilled out from both natural and anthropic sources in mussels farming high density areas. Mussel farms product quality remains a challenging problem for operational marine science: in this scenario, the model chain presented in this work, orchestrated in a workflow fashion, produces a huge amount of predicted spatially-referenced (big) data. The software infrastructure we built using FACE-IT Galaxy Globus provides tools enabled to evaluate the impact of hazardous substances (chemical or biological) continuously or spottily spilled in the marine environment

FACE-IT: A science gateway for food security research

Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post-processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large-scale climate impact simulations with agricultural and other models, leveraging high-performance and cloud computing; and post-processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connect biophysical models to global and regional economic models. FACE-IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well-defined, reusable, and comparable forms. We describe FACE-IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision-making on Climate and Energy Policy