Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly executed in data centers with energy-efficient hard-ware accelerators since FPG As are well-suited for this task due to their inherent parallelism. We present the H2020 project EVEREST, which has developed a system development kit (SDK) to simplify the creation of FPGA-accelerated kernels and manage the execution at runtime through a virtualization environment. This paper describes the main components of the EVEREST SDK and the benefits that can be achieved in our use cases.

I can’t change the world by myself. This is a common thought citizens share when it comes to climate action. Climate change campaigns usually aim at raising awareness and triggering action to reduce our footprint. However, these campaigns often fail as they are designed in a top-down manner. The ChallengeYeti app was created on a psychological-technological foundation focusing on motivational and bottom-up approaches to bridge this knowledge-action gap. Hence, the app primarily strives to trigger behavioral change in a fun and social way. This paper introduced the app and its functionalities, its theoretical and technical background, its implementation, its expected impact on behavioral change, and lastly, future challenges of the app.

Climate change and environmental degradation present significant threats globally. The various sources report on different climate records. The European Green Deal aims to counteract these challenges by achieving zero emissions by 2050, with a significant focus on citizen involvement. Citizen science initiatives play a critical role in engaging the public in climate action through data collection and monitoring of environmental impacts. Technological tools, such as dashboards, facilitate this involvement by visualizing complex climate data, raising awareness, and bridging the knowledge-action gap. This paper discusses a dashboard (developed within the Horizon 2020 project I-CHANGE) designed to engage citizens in environmental data collection and analysis. The Dashboard, co-designed with scientists and stakeholders, supports citizens in viewing, understanding, and interpreting data collected with low-cost sensors and through crowdsourcing activities. The paper gives an overview of the Dashboard's design, implementation, data integration, and its role in fostering public engagement and environmental consciousness.

Big data analytics largely rely on data. Because of their central role, it is fundamental to ensure the security and correctness of data used in these applications. Anomaly detection could help to increase the security of big data analytics applications. However, these applications are very diverse both for the properties of the data analyzed and for the computations to be carried out on them. As a result, the selection of the most appropriate anomaly detection method is a challenging and time consuming task for designers. Hierarchical Temporal Memory (HTM) is as an anomaly detection technique sufficiently generic to achieve satisfactory performance on a wide range of applications, thus suitable to ease the burden of selecting the anomaly detection method. To confirm this, in this paper we explore the performance of HTM on a dataset used for air quality prediction. Our preliminary results show that HTM achieves excellent performance when compared to other popular anomaly detection methods.

Nowadays, predicting the exact location and timing of severe convective phenomena at small spatial and temporal scales is still a challenge. In this respect, the H2020 SESAR project “Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM” (SINOPTICA) aims to improve the forecast of severe weather events by using the numerical weather prediction model in nowcasting mode and the benefit of assimilating non-conventional observations, such as weather radar, GNSS and lightning to predict the convective cells developing in the vicinity of airports to support air traffic control operations. In this work, we present the results related to the Venice case study pointing out the positive impact of assimilating radar data with lightning and GNSS for a very short-range forecast.

High-Performance Big Data Analytics (HPDA) applications are characterized by huge volumes of distributed and heterogeneous data that require efficient computation for knowledge extraction and decision making. Designers are moving towards a tight integration of computing systems combining HPC, Cloud, and IoT solutions with artificial intelligence (AI). Matching the application and data requirements with the characteristics of the underlying hardware is a key element to improve the predictions thanks to high performance and better use of resources. We present EVEREST, a novel H2020 project started on October 1, 2020, that aims at developing a holistic environment for the co-design of HPDA applications on heterogeneous, distributed, and secure platforms. EVEREST focuses on programmability issues through a data-driven design approach, the use of hardware-accelerated AI, and an efficient runtime monitoring with virtualization support. In the different stages, EVEREST combines state-of-the-art programming models, emerging communication standards, and novel domain-specific extensions. We describe the EVEREST approach and the use cases that drive our research.

In the framework of the E-SHAPE "EuroGEOSS Showcase: Applications powered by Europe" project, one of the pilot applications concerns the disasters in urban environment. One of the objectives of this application is the conceiving of innovative services for extreme-scale hydro-meteorological modelling that make use of Copernicus Earth Observation data. An example of innovative service is the ingestion of high-resolution Copernicus remote sensing products in numerical weather prediction (NWP) models. The rationale is that NWP models are presently able to produce forecasts with a spatial resolution in the order of 1 km, but unreliable surface information or poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. It is expected that forecast inaccuracies could be reduced by ingesting high resolution Earth Observation products into the models. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed over sea, columnar water vapor) used in NWP model runs. The possible availability of a spatially dense network of Global Navigation Satellite Systems (GNSS) stations could also be exploited to allow NWP models to assimilate timely updated data about water vapor in the atmosphere. As a preliminary activity carried out in the frame of the E-SHAPE project, this paper presents the results of the experiments regarding the insertion/assimilation of surface information derived from Sentinel data into a NWP model. The experiments concern a flood event occurred in Italy in 2017 that affected an urban area, namely the Livorno city.

The European Space Agency (ESA) STEAM (SaTellite Earth observation for Atmospheric Modelling) project aims at investigating new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2 and 3 satellites. An example of synergy is the ingestion of surface information derived from Sentinel data in numerical weather prediction models. The rationale is that Sentinels 1, 2 and 3 are able to provide high spatio-temporal resolution information on the surface boundary (as well as the atmosphere column) and that an inaccurate representation of the boundary conditions represents a major source of uncertainty for weather forecasts. For a profitable ingestion of EO data in numerical weather prediction models, a critical aspect is the choice of a suitable model. Once the numerical model is chosen, the problem of the selection of the Sentinel-derived surface variables that have to be ingested in the model has to be tackled. While some data, such as sea and land surface temperature, are directly available, other surface data, such as soil moisture, have to be retrieved. Being STEAM currently in its initial phase, this paper gives a general overview of the project and focuses on the first activities performed in its framework. In particular, it describes the rationale behind the choice of the Numerical Weather Prediction Model and the multi-temporal approach designed to retrieve soil moisture from Sentinel-1 data. Moreover, the first results of the ingestion of Sentinel derived soil moisture, land surface temperature and sea surface temperature data into the selected model are shown. These results concern an extreme weather event that occurred in Tuscany (central Italy) in September 2017.

Summary A full hydrometeorological (HM) simulation, from rainfall to impact on urban areas, is a multidisciplinary job, which relies on the execution of a workflow composed of complex and heterogeneous model engines. Moreover, the accuracy of the simulation is strongly dependent on an extensive set of configuration parameters, which have to be selected in a consistent way among the models. Within the Distributed Research Infrastructure for Hydro‐Meteorology project, a Web‐based science gateway was developed with the aim to support HM researchers in designing, executing, and managing HM experiments. The core of this science gateway is the portal, which takes care of generating all the configuration files and handles the execution of simulation steps on a heterogeneous computing infrastructure composed of high‐performance computing, Grid resources, and Cloud resources. This paper presents technological insights about the implementation of the portal, with an analysis of the adopted technologies and infrastructures. Our experience highlights the need of coherent policies in the management of data, computational resources, and software components that represent the ecosystem to develop science gateways. Copyright © 2015 John Wiley & Sons, Ltd.

Earth science disciplines have imaginary borders. Earth systems are connected and work integrally. Therefore for geosciences researchers it is critical to find, analyze and publish data across domains. Unfortunately, while searching for and accessing enormous amount of heterogeneous data, scientists and professionals often tackle various types of obstacles that hinder their daily activity. Based on a survey on international initiatives in the field of Hydro-Meteorological research, the paper presents a lightweight approach to answer weather-data issues related to data accessing and retrieving, as well as it briefly describe the OGC proposal to cope with interoperability requirements and introduces a technical solution to deal with high volumes of Hydro-Meteorological sensor data.

Predicting weather and climate and its impacts on the environment, including hazards such as floods and landslides, is a big challenge that can be efficiently supported by a distributed and heterogeneous infrastructure, exploiting several kinds of computational resources: HPC, Grids and Clouds. This can help researchers in speeding up experiments, improve resolution and accuracy, simulate with different numerical models and model chains. Such numerical models are complex with heavy computational requirements, huge numbers of parameters to tune, and not fully standardized interfaces. Hence, each research entity is usually focusing on a limited set of tools and hard-wired solutions to enable their interaction. The DRIHM approach is based on strong standardization, well defined interfaces, and an easy to use web interface for model configuration and experiment definition. A researcher can easily compare outputs from different hydrologic models forced by the same meteorological model, or compare different meteorological models to validate or improve her research. This paper presents the benefit of a web-based interface for hydro-meteorology research through a detailed analysis of the portal (based on liferay-gUse) developed by the DRIHM project.

The distributed research infrastructure for hydrometeorology (DRIHM) project focuses on the development of an e-Science infrastructure to provide end-to-end hydro meteorological research (HMR) services (models, data, and post processing tools) by exploiting HPC, Grid and Cloud facilities. In particular, the DRIHM infrastructure supports the execution and analysis of high-resolution simulations through the definition of workflows composed by heterogeneous HMR models in a scalable and interoperable way, while hiding all the low level complexities. This contribution gives insights into best practices adopted to satisfy the requirements of an emerging multidisciplinary scientific community composed of earth and atmospheric scientists. To this end, DRIHM supplies innovative services leveraging high performance and distributed computing resources. Hydro meteorological requirements shape this IT infrastructure through an iterative "learning-by-doing" approach that permits tight interactions between the application community and computer scientists, leading to the development of a flexible, extensible, and interoperable framework.

This paper presents an investigation on the rainfall signature for two COSMO-SkyMed (CSK) satellite case studies. Both of them are relative to a severe precipitation weather event, occurred in northwestern Italy (close to Liguria region) on November 3-8, 2011. This event was monitored by using a number of CSK images provided by the Italian Space Agency (ASI). In this case CSK X-SAR data have been compared with the weather radar (WR) Italian Radar National Mosaic. A third case study is relative to Hurricane “Irene” event, occurred in Eastern United States (close to Delaware) on late August 2011. CSK X-SAR images are compared with respect to concurrent ground-based S-band NEXRAD weather radar reflectivities. The correlation of the precipitating cloud fields between CSK X-SAR and WR images is significant in all case studies. An application of a refined XSAR-based precipitation retrieval method is presented. The X-SAR surface response is estimated using ancillary data, such as land cover maps and a digital elevation model (DEM).