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The scientific discovery process has been deeply influenced by the data deluge started at the beginning of this century. This has caused a profound transformation in several scientific domains which are now moving towards much more collaborative processes. In the climate sciences domain, the ENES Data Space aims to provide an open, scalable, cloud-enabled data science environment for climate data analysis. It represents a collaborative research environment, deployed on top of the EGI federated cloud infrastructure, specifically designed to address the needs of the ENES community. The service, developed in the context of the EGI-ACE project, provides ready-to-use compute resources and datasets, as well as a rich ecosystem of open source Python modules and community-based tools (e.g., CDO, Ophidia, Xarray, Cartopy, etc.), all made available through the user-friendly Jupyter interface. In particular, the ENES Data Space provides access to a multi-terabyte set of specific variable-centric collections from large community experiments to support researchers in climate model data analysis experiments. The data pool of the ENES Data Space consists of a mirrored subset of CMIP datasets from the ESGF federated data archive collected by using the Synda community tool in order to provide the most up to date datasets into a single location. Results and output products as well as experiment definitions (in the form of Jupyter Notebooks) can be easily shared among users through data sharing services, which are also being integrated in the infrastructure, such as EGI DataHub.The service was opened in the second part of 2021 and is now accessible in the European Open Science Cloud (EOSC) through the EOSC Portal Marketplace (https://marketplace.eosc-portal.eu/services/enes-data-space). This contribution will present an overview of the ENES Data Space service and its main features.

In the context of XF-ACTORS project, a IoT based monitoring system has been established and tested in the field - for an olive grove in Puglia (Italy) affected by Xf - to measure in near real-time some parameters proxies of trees’ conditions and vulnerability under both abiotic (climate) and biotic factors. The system is based on the TreeTalker (TT) technology, comprising multiparametric sensors to monitor water transport in trees, trunk humidity and diametrical oscillations, spectral characteristics of the leaves and microclimatic parameters (temperature, relative humidity). In particular, the sap flow density can be retrieved according to the Heat Balance Method (Granier 1985) after measuring the temperature of two 20 mm long probes inserted into the stem wood at 10 cm distance along the trunk vertical axis; the probe in the higher position is heated while the lower one provides the reference temperature. The TT system collects and transmits data at hourly time frequency, thanks to a LoRa based wireless connection, to a node managed by another microcontroller (TT-Cloud) serving a few tens of devices in a cluster. The TT-Cloud is in turn connected to the internet via the GPRS network and sends data to a computer server. Here, raw data are subject to ETL procedure that allows data Extraction from the TT-Cloud source, data Transformation by cleaning and converting them into variables with eco-physiological meaning, and finally data Loading to insert them into the target spatio-temporal database, adopting proper storage format/structure for querying and analysis purpose. From here, data can be further elaborated and visualized, e.g. into useful statistics, through a tailored Data Science environment. The preliminary results on sap flow density are here presented as they can give important information about the impact of Xf that is known obstructing xylem vessels, reducing hydraulic conductivity and thus affecting evapotranspiration.

Tired of downloading tons of model results? Is your internet connection flakey? Are you about to overload your computer’s memory with the constant increase of data volume and you need more computing resources? You can request free of charge computing time at one of the supercomputers of the Infrastructure of the European Network of Earth System modelling (IS-ENES)1, the European part of Earth System Grid Federation (ESGF)2, which also hosts and maintains more than 6 Petabytes of CMIP6 and CORDEX data.Thanks to this new EU Comission funded service, you can run your own scripts in your favorite programming language and straightforward pre- and post-process model data. There is no need for heavy data transfer, just load with one line of code the data slice you need because your script will directly access the data pool. Therefore, days-lasting calculations will be done in seconds. You can test the service, we very easily provide pre-access activities.In this session we will run Jupyter notebooks directly on the German Climate Computing Center (DKRZ)3, one of the ENES high performance computers and a ESGF data center, showing how to load, filter, concatenate, take means, and plot several CMIP6 models to compare their results, use some CMIP6 models to calculate some climate indexes for any location and period, and evaluate model skills with observational data. We will use Climate Data Operators (cdo)4 and Python packages for Big Data manipulation, as Intake5, to easily extract the data from the huge catalog, and Xarray6, to easily read NetDCF files and scale to parallel computing. We are continuously creating more use cases for multi-model evaluation, mechanisms of variability, and impact analysis, visit the demos, find more information, and apply here: https://portal.enes.org/data/data-metadata-service/analysis-platforms.[1] https://is.enes.org/[2] https://esgf.llnl.gov/[3] https://www.dkrz.de/[4] https://code.mpimet.mpg.de/projects/cdo/[5] https://intake.readthedocs.io/en/latest/[6] http://xarray.pydata.org/en/stable/

Tropical cyclones (TCs) transport energy and moisture along their pathways interacting with the climate system and TCs activities are expected to extend further poleward during the 21st century.For this reason, it is important to assess the ability of state-of-the-art climate models in reproducing an accurate meridional distribution of TCs as well as a reasonable meridional portrait of moisture transport associated with TCs.Since high resolutions are required to reconstruct observed TCs activity, the present work is based on the simulations performed as part of HighResMIP in the framework of the community CMIP6 effort. To inspect this feature, two horizontal resolutions for each climate model are considered. Besides, the impact of boundary conditions, i.e. observed ocean surface state, is examined by considering both coupled and atmosphere-only configurations.In the present work, the north Atlantic region is analyzed as a sample region, while the same approach is applied on a multi-basin basis. In the sample area, climate models present a good ability in reproducing the TCs distribution, with a general underestimation at lower latitudes and a slight overestimation at high-latitudes compared to observed TCs tracks (e.g. IBTRACK).The meridional distribution of moisture transport associated with TCs is evaluated by considering the radial average of the integrated water vapor transport along the TC tracks. When compared to observation (IBTRACS and JRA-55 reanalysis), the simulated moisture transport associated with TCs displays reasonably good performance in atmosphere-only high-resolution models configuration. The interannual variability of water vapor associated with TCs, instead, is poorly represented in climate models.Climate models in high-resolution configuration can then be used in estimating future TCs meridional distribution and changes in meridional moisture transport associated with TCs.This effort is part of HighResMIP and it is developed in the framework of the EU-funded PRIMAVERA project.

Earth System observational and model data volumes are constantly increasing and it can be challenging to discover, download, and analyze data if scientists do not have the required computing and storage resources at hand. This is especially the case for detection and attribution studies in the field of climate change research since we need to perform multi-source and cross-disciplinary comparisons for datasets of high-spatial and large temporal coverage. Researchers and end-users are therefore looking for access to cloud solutions and high performance compute facilities. The Earth System Grid Federation (ESGF, https://esgf.llnl.gov/) maintains a global system of federated data centers that allow access to the largest archive of model climate data world-wide. ESGF portals provide free access to the output of the data contributing to the next assessment report of the Intergovernmental Panel on Climate Change through the Coupled Model Intercomparison Project. In order to support users to directly access to high performance computing facilities to perform analyses such as detection and attribution of climate change and its impacts, the EU Commission funded a new service within the infrastructure of the European Network for Earth System Modelling (ENES, https://portal.enes.org/data/data-metadata-service/analysis-platforms). This new service is designed to reduce data transfer issues, speed up the computational analysis, provide storage, and ensure the resources access and maintenance. Furthermore, the service is free of charge, only requires a lightweight application. We will present a demo on how flexible it is to calculate climate indices from different ESGF datasets covering a wide range of temporal and spatial scales using cdo (Climate Data Operators, https://code.mpimet.mpg.de/projects/cdo/) and Jupyter notebooks running directly on the ENES partners: the DKRZ (Germany), JASMIN (UK), CMCC(Italy), and IPSL (France) high performance computing centers.

This contribution highlights the Python xarray technique in context of a climate specific application (typical formats are NetCDF, GRIB and HDF).We will see how to use in-file metadata and why they are so powerful for data analysis, in particular by looking at community specific problems, e.g. one can select purely on coordinate variable names. ECAS, the ENES Climate Analytics Service available at Deutsches Klimarechenzentrum (DKRZ), will help by enabling faster access to the high-volume simulation data output from climate modeling experiments. In this respect, we can also make use of “dask” which was developed for parallel computing and can smoothly work with xarray. This is extremely useful when we want to exploit fully the advantages of our supercomputer.Our fully integrated service offers an interface via Jupyter notebooks (ecaslab.dkrz.de). We provide an analysis environment without the need of costly transfers, accessing CF standardized data files and all accessible via the ESGF portal on our nodes (esgf-data.dkrz.de). We can analyse the data of e.g. CMIP5, CMIP6, Grand Ensemble and observation data. ECAS was developed in the frame of European Open Source Cloud (EOSC) hub.

Scientific data analysis experiments and applications require software capable of handling domain-specific and data-intensive workflows. The increasing volume of scientific data is further exacerbating these data management and analytics challenges, pushing the community towards the definition of novel programming environments for dealing efficiently with complex experiments, while abstracting from the underlying computing infrastructure. ECASLab provides a user-friendly data analytics environment to support scientists in their daily research activities, in particular in the climate change domain, by integrating analysis tools with scientific datasets (e.g., from the ESGF data archive) and computing resources (i.e., Cloud and HPC-based). It combines the features of the ENES Climate Analytics Service (ECAS) and the JupyterHub service, with a wide set of scientific libraries from the Python landscape for data manipulation, analysis and visualization. ECASLab is being set up in the frame of the European Open Science Cloud (EOSC) platform - in the EU H2020 EOSC-Hub project - by CMCC (https://ecaslab.cmcc.it/) and DKRZ (https://ecaslab.dkrz.de/), which host two major instances of the environment. ECAS, which lies at the heart of ECASLab, enables scientists to perform data analysis experiments on large volumes of multi-dimensional data by providing a workflow-oriented, PID-supported, server-side and distributed computing approach. ECAS consists of multiple components, centered around the Ophidia High Performance Data Analytics framework, which has been integrated with data access and sharing services (e.g., EUDAT B2DROP/B2SHARE, Onedata), along with the EGI federated cloud infrastructure. The integration with JupyterHub provides a convenient interface for scientists to access the ECAS features for the development and execution of experiments, as well as for sharing results (and the experiment/workflow definition itself). ECAS parallel data analytics capabilities can be easily exploited in Jupyter Notebooks (by means of PyOphidia, the Ophidia Python bindings) together with well-known Python modules for processing and for plotting the results on charts and maps (e.g., Dask, Xarray, NumPy, Matplotlib, etc.). ECAS is also one of the compute services made available to climate scientists by the EU H2020 IS-ENES3 project. Hence, this integrated environment represents a complete software stack for the design and run of interactive experiments as well as complex and data-intensive workflows. One class of such large-scale workflows, efficiently implemented through the environment resources, refers to multi-model data analysis in the context of both CMIP5 and CMIP6 (i.e., precipitation trend analysis orchestrated in parallel over multiple CMIP-based datasets).

In recent decades, the complexity of scientific and engineering problems has increased considerably. New applications and domains that use high performance computing systems have been introduced. These trends are projected to continue for the foreseen future (Reed and Dongarra, 2015) [ 1 ]. In many areas of engineering and science, High-Performance Computing (HPC) and Simulations have become determinants of industrial competitiveness and advanced research. In fact, advances in HPC architectures, storages, networking, and software capabilities are leading to a new era in HPC and simulations, along with new challenges both in computing and systems modeling (Geist and Lucas, 2009) [ 2 ]. These developments are especially critical considering that HPC systems continue to scale up in terms of nodes, cores, and accelerators, as well as software, infrastructure and tools, which in turn are expediting the move on the path toward Exascale (Reed and Dongarra, 2015; Geist and Lucas, 2009; Dongarra and Beckman, 2011; Dosanjh et al., 2014; Engelmann, 2014) [ [1] , [2] , [3] , [4] , [5] ]. Scalability and availability represent two of the main requirements that need to be considered before conceiving of these large-scale systems (ASCAC Subcommittee on Exascale Computing, 2010). The scalability feature allows the system to proportionately grow when service demand increases, whereas availability means the system continues to provide their services despite hardware and software failures (Theodoropoulos et al., 2014; Tang et al., 2014) [ [7] , [8] ]. The goal in large-scale HPC is to accommodate both availability and scalability while staying under strict constraints on performance (e.g., processing time) and cost metrics (e.g., power consumption). This special issue is envisioned to provide examples of research work on topics related to recent advances in High Performance Computing and Simulations. It briefly addresses and explores challenges toward Exascale computing, current state-of-the-art in HPC and simulation, and the path forward in the domains of large-scale HPC systems.

Reliable and timely information on the environmental conditions at sea is key to the safety of professional and recreational users as well as to the optimal execution of their activities. The possibility of users obtaining environmental information in due time and with adequate accuracy in the marine and coastal environment is defined as sea situational awareness (SSA). Without adequate information on the environmental meteorological and oceanographic conditions, users have a limited capacity to respond, which has led to loss of lives and to large environmental disasters with enormous consequent damage to the economy, society and ecosystems. Within the framework of the TESSA project, new SSA services for the Mediterranean Sea have been developed. In this paper we present SeaConditions, which is a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. Model forecasts and satellite products from operational services, such as ECMWF and CMEMS, can be visualized in SeaConditions. In addition, layers of information related to bathymetry, sea level and ocean-colour data (chl a and water transparency) are displayed. Ocean forecasts at high spatial resolutions are included in the version of SeaConditions presented here. SeaConditions provides a user-friendly experience with a fluid zoom capability, facilitating the appropriate display of data with different levels of detail. SeaConditions is a single point of access to interactive maps from different geophysical fields, providing high-quality information based on advanced oceanographic models. The SeaConditions services are available through both web and mobile applications. The web application is available at www.sea-conditions.com and is accessible and compatible with present-day browsers. Interoperability with GIS software is implemented. User feedback has been collected and taken into account in order to improve the service. The SeaConditions iOS and Android apps have been downloaded by more than 105000 users to date (May 2016), and more than 100000 users have visited the web version.

The European Network for Earth System Modelling (ENES) Climate Analytics Service (ECAS) is a new service from the EOSC-hub project. It offers a Virtual Research Environment (VRE) to scientific users, combining a Python (Jupyter) work environment with support services for data access, computing and data sharing. ECAS is motivated by providing users with remote access to extensive computing and storage resources beyond what they may have access to locally, reducing the need to conduct costly data transfer, and helping to realize the vision of FAIR data management. ECAS aims at providing a paradigm shift for the ENES community and beyond with a strong focus on data intensive analysis, provenance management, and server-side approaches as opposed to the current ones mostly client-based, sequential and with limited or missing end-to-end analytics workflow and provenance capabilities. Furthermore, the integrated data analytics service enables basic data provenance tracking by establishing a graph of persistent identifiers (PIDs) through the whole chain, and thereby improving reusability, traceability, and reproducibility. ECAS targets multiple user groups, including researchers in lack of local computing and storage resources, researchers with interest in the high-volume climate data pools, and use within education and training scenarios.

[EN] Climate and biodiversity systems are closely linked across a wide range of scales. To better understand the mutual interaction between climate change and biodiversity there is a strong need for multidisciplinary skills, scientific tools, and access to a large variety of heterogeneous, often distributed, data sources. Related to that, the EUBrazilCloudConnect project provides a user-oriented research environment built on top of a federated cloud infrastructure across Europe and Brazil, to serve key needs in different scientific domains, which is validated through a set of use cases. Among them, the most data-centric one is focused on climate change and biodiversity research. As part of this use case, the BioClimate Science Gateway has been implemented to provide end-users transparent access to (i) a highly integrated user-friendly environment, (ii) a large variety of data sources, and (iii) different analytics & visualization tools to serve a large spectrum of users needs and requirements. This paper presents a complete overview of BioClimate and the related scientific environment, in particular its Science Gateway, delivered to the end-user community at the end of the project., This work was supported by the EU FP7 EUBrazilCloudConnect Project (Grant Agreement 614048), and CNPq/Brazil (Grant Agreement no 490115/2013-6).

Analysis of public transportation data in large cities is a challenging problem. Managing data ingestion, data storage, data quality enhancement, modelling and analysis requires intensive computing and a non-trivial amount of resources. In EUBra-BIGSEA (Europe–Brazil Collaboration of Big Data Scientific Research Through Cloud-Centric Applications) we address such problems in a comprehensive and integrated way. EUBra-BIGSEA provides a platform for building up data analytic workflows on top of elastic cloud services without requiring skills related to either programming or cloud services. The approach combines cloud orchestration, Quality of Service and automatic parallelisation on a platform that includes a toolbox for implementing privacy guarantees and data quality enhancement as well as advanced services for sentiment analysis, traffic jam estimation and trip recommendation based on estimated crowdedness., The work shown in this article has been funded jointly by European Commission under the Cooperation Programme, Horizon2020 grant agreement No 690116 (EUBra-BIGSEA) and the Min-istériode Ciência,Tecnologiae Inovação(MCTI) from Brazil

Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro-Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP). This paper reports about some ESiWACE WP3 activities described in the deliverable D3.10 "ESiWACE Scheduler development and support activities"

Journal of grid computing 16(3), 381 - 408 (2018). doi:10.1007/s10723-018-9453-3, This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications., Published by Springer Nature, Dordrecht

The MEDiterranean Monitoring and Forecasting Center (Med-MFC) is part of the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), provided on an operational mode by Mercator Ocean in agreement with the European Commission. Specifically, Med MFC system provides regular and systematic information about the physical state of the ocean and marine ecosystems for the Mediterranean Sea. The Med-MFC service started in May 2015 from the pre-operational system developed during the MyOcean projects, consolidating the understanding of regional Mediterranean Sea dynamics, from currents to biogeochemistry to waves, interfacing with local data collection networks and guaranteeing an efficient link with other Centers in Copernicus network. The Med-MFC products include analyses, 10 days forecasts and reanalysis, describing currents, temperature, salinity, sea level and pelagic biogeochemistry. Waves products will be available in MED-MFC version in 2017. The consortium, composed of INGV (Italy), HCMR (Greece) and OGS (Italy) and coordinated by the Euro-Mediterranean Centre on Climate Change (CMCC, Italy), performs advanced R&D activities and manages the service delivery. The Med-MFC infrastructure consists of 3 Production Units (PU), for Physics, Biogechemistry and Waves, a unique Dissemination Unit (DU) and Archiving Unit (AU) and Backup Units (BU) for all principal components, guaranteeing a resilient configuration of the service and providing and efficient and robust solution for the maintenance of the service and delivery. The Med-MFC includes also an evolution plan, both in terms of research and operational activities, oriented to increase the spatial resolution of products, to start wave products dissemination, to increase temporal extent of the reanalysis products and improving ocean physical modeling for delivering new products. The scientific activities carried out in 2015 concerned some improvements in the physical, biogeochemical and wave components of the system. Regarding the currents, new grid-point EOFs have been implemented in the Med-MFC assimilation system; the climatological CMAP precipitation was replaced by the ECMWF daily precipitation; reanalysis time-series have been increased by one year. Regarding the biogeochemistry, the main scientific achievement is related to the implementation of the carbon system in the Med-MFC biogeochemistry model system already available. The new model is able to reproduce the principal spatial patterns of the carbonate system variables in the Mediterranean Sea. Further, a key result consists of the calibration of the new variables (DIC and alkalinity), which serves to the estimation of the accuracy of the new products to be released in the next version of the system (i.e. pH and pCO2 at surface). Regarding the waves, the system has been validated against in-situ and satellite observations. For example, a very good agreement between model output and in-situ observations has been obtained at offshore and/or well-exposed wave buoys in the Mediterranean Sea.

Climate and biodiversity systems are closely interlaced across a wide range of scales. To better understand the mutual interaction between climate change and biodiversity there is a strong need for multidisciplinary skills, tools and a large variety of heterogeneous, distributed data sources. In this regard, the EUBrazilCloudConnect project provides a user-centric research environment built on top of a federated cloud infrastructure across Europe and Brazil to serve scientific needs. One of the test cases implemented in this project focuses on climate change and biodiversity research. The BioClimate is the Science Gateway of the use case. It aims at providing end-users with a highly integrated environment, addressing mainly data analytics requirements. This paper presents a complete overview about BioClimate and the scientific environment delivered to the user community at the end of the project.

An efficient, secure, and interoperable data platform solution has been developed in the TESSA project to provide fast navigation and access to the data stored in the data archive, as well as a standard-based metadata management support. The platform mainly targets scientific users and the Situational Sea Awareness high-level services such as the Decision Support Systems (DSS). These datasets are accessible through the following three main components: the Data Access Service (DAS), the Metadata Service, and the Complex Data Analysis Module (CDAM). The DAS allows access to data stored into the archive by providing interfaces for different protocols. OPeNDAP, THREDDS, and WMS are just some of the solutions that have been integrated into the TESSA infrastructure. Metadata Service is the heart of the information system of the TESSA products and completes the overall infrastructure for data and metadata management. This component enables data search & discovery, and addresses interoperability by exploiting ISO standards for geospatial data (ISO 19115 and ISO 19139). Finally, the CDAM represents the back-end of the TESSA DSS by performing on-demand complex data analysis tasks.

A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).

Many e-science initiatives are currently investigating the use of cloud computing to support all kinds of scientific activities. The objective of this chapter is to describe the architecture and the deployment of the EUBrazilCC federated e-infrastructure, a Research & Development project that aims at providing a user-centric test bench enabling European and Brazilian research communities to test the deployment and execution of scientific applications on a federated intercontinental e-infrastructure. This e-infrastructure exploits existing resources that consist of virtualized data centers, supercomputers, and even opportunistically exploited desktops spread over a transatlantic geographic area. These heterogeneous resources are federated with the aid of appropriate middleware that provide the necessary features to achieve the established challenging goals. In order to elicit the requirements and validate the resulting infrastructure, three complex scientific applications have been implemented, which are also presented here.

Climate Change research is even more becoming a data intensive and oriented scientific activity. Petabytes of climate data, big collections of datasets are continuously produced, delivered, accessed, processed by scientists and researchers at multiple sites at an international level. This work presents the Euro-Mediterranean Centre for Climate Change (CMCC) initiative, discussing data and metadata issues and dealing with both architectural and infrastructural aspects concerning the adopted grid enabled solution. A complete overview of the grid services deployed at the Centre is presented as well as the client side support (CMCC data portal and monitoring dashboard).

In this paper, we describe the process of parallelizing an existing, production level, sequential Synthetic Aperture Radar (SAR) processor based on the Range-Doppler algorithmic approach. We show how, taking into account the constraints imposed by the software architecture and related software engineering costs, it is still possible with a moderate programming effort to parallelize the software and present an message-passing interface (MPI) implementation whose speedup is about 8 on 9 processors, achieving near real-time processing of raw SAR data even on a moderately aged parallel platform. Moreover, we discuss a hybrid two-level parallelization approach that involves the use of both MPI and OpenMP. We also present GridStore, a novel data grid service to manage raw, focused and post-processed SAR data in a grid environment. Indeed, another aim of this work is to show how the processed data can be made available in a grid environment to a wide scientific community, through the adoption of a data grid service providing both metadata and data management functionalities. In this way, along with near real-time processing of SAR images, we provide a data grid-oriented system for data storing, publishing, management, etc.

We present an integrated Grid system for the prediction of protein secondary structures, based on the frequent automatic update of proteins in the training set. The predictor model is based on a feed-forward multilayer perceptron (MLP) neural network which is trained with the back-propagation algorithm; the design reuses existing legacy software and exploits novel grid components. The predictor takes into account the evolutionary information found in multiple sequence alignment (MSA); the information is obtained running an optimized parallel version of the PSI-BLAST tool, based on the MPI Master–Worker paradigm. The training set contains proteins of known structure. Using Grid technologies and efficient mechanisms for running the tools and extracting the data, the time needed to train the neural network is dramatically reduced, whereas the results are comparable to a set of well-known predictor tools.

This paper describes the Grid Resource Broker (GRB), a Grid portal built leveraging a set of high-level, Globus-Toolkit-based Grid libraries called GRB libraries. The portal leverages the Liferay framework to provide users with an intuitive, highly customizable Web GUI. The underlying GRB middleware allows trusted users seamless access to their computational Grid environments. Copyright (c) 2007 John Wiley & Sons, Ltd.

Prevention is one of the most important stages in wildfire and other natural hazard management. Fire Danger Rating Systems (FDRSs) have been adopted by many countries to enhance wildfire prevention and suppression planning. With the aim to provide real-Time fire danger forecasts and finer-scale fire behaviour analysis, an operational fire danger prevention platform has been developed within the OFIDIA project (Operational FIre Danger preventIon plAtform). The OFIDIA Fire Danger Rating System platform consists of (1) a data archive for managing weather forecasting and wireless sensors data, (2) a data analytics platform for post-processing weather data and for computing fire danger indices, and (3) a web application system for the visualization of weather and fire index maps and related timeseries. The OFIDIA platform is also connected to a Wireless Sensor Network (WSN) that gathers data from several sites in the Apulia (Italy) and Epirus (Greece) regions. The WSN is made by a primary station and several wireless sensors dislocated in wooded areas, the data acquisition process relates to variables like air temperature, relative humidity, wind speed and direction, precipitation, solar radiation, and fuel moisture.

Sea Situational Awareness (SSA) is strategically important for management purposes of Italian Seas and coastal areas. The lack of adequate dissemination of marine environmental data and consequent poor knowledge available for operations at sea reduce the response capacity, leading to loss of lives and potential socio-economic damages. The SSA topic is being addressed by "TESSA", an industrial research project funded under the PON "Ricerca & Competitivita 2007–2013" program of Ministero Italiano dell'Istruzione, dell'Universita' e della Ricerca. TESSA is a joint effort of research groups of operational oceanography and scientific computing, and aims to strengthen and consolidate the operational oceanography service and to integrate it with advanced technological platforms in order to disseminate information for the SSA. The first product of TESSA is “SeaConditions”, a public service providing ocean and weather forecasts for the Mediterranean Sea, on the web and mobile applications. Every day, forecasts are produced by operational services, such as the Mediterranean Monitoring and Forecasting Center (www.myocean.eu) for the ocean variables and ECMWF for the atmospheric variables. The service delivers detailed information with high spatial and temporal resolution. Main variables displayed on Google Maps are: bathymetry, weather and oceanographic forecasts and satellite ocean colour data. Ocean forecasts are given at different resolution since nested limited area models for Mediterranean sub-regions are also displayed. SeaConditions provides a user friendly interface with zoom and drag Google Maps' features allowing to display data with different levels of details. SeaConditions' main strength is to provide a single point of access to meteo-marine forecasts, which are based on advanced oceanographic models, remote sensing products and bathymetry, and to deliver high quality information. The SeaConditions products are available through web and mobile channels. The web portal www.sea-conditions.com is compatible with all modern web-browsers on all operating systems. For the mobile users, APPs were also developed to consider the different kind of screens and gesture/interactions. The APPs are available on AppleStore and Google Play.