The European Sentinel missions and the latest generation of the United States Landsat satellites provide new opportunities for global environmental monitoring. They acquire imagery at spatial resolutions between 10 and 60 m in a temporal and spatial coverage that could before only be realized on the basis of lower resolution Earth observation data (>250 m). However, images gathered by these modern missions rapidly add up to data volume that can no longer be handled with standard work stations and software solutions. Hence, this contribution introduces the TimeScan concept which combines pre-existing tools to an exemplary modular pipeline for the flexible and scalable processing of massive image data collections on a variety of (private or public) computing clusters. The TimeScan framework covers solutions for data access to arbitrary mission archives (with different data provisioning policies) and data ingestion into a processing environment (EO2Data module), mission specific pre-processing of multi-temporal data collections (Data2TimeS module), and the generation of a final TimeScan baseline product (TimeS2Stats module) providing a spectrally and temporally harmonized representation of the observed surfaces. Technically, a TimeScan layer aggregates the information content of hundreds or thousands of single images available for the area and time period of interest (i.e. up to hundreds of TBs or even PBs of data) into a higher level product with significantly reduced volume. In first test, the TimeScan pipeline has been used to process a global coverage of 452,799 multispectral Landsat-8 scenes acquired from 2013 to 2015, a global data-set of 25,550 Envisat ASAR radar images collected 2010-2012, and regional Sentinel-1 and Sentinel-2 collections of ∼1500 images acquired from 2014 to 2016. The resulting TimeScan products have already been successfully used in various studies related to the large-scale monitoring of environmental processes and their temporal dynamics. ARTICLE HISTORY

This article describes statistical evaluation of the computational model for precipitation forecast and proposes a method for uncertainty modelling of rainfall-runoff models in the Floreon + system based on this evaluation. The Monte-Carlo simulation method is used for estimating possible river discharge and provides several confidence intervals that can support the decisions in operational disaster management. Experiments with other parameters of the model and their influence on final river discharge are also discussed.

The Sentinel fleet will provide a so-far unique coverage with Earth observation data and therewith new opportunities for the implementation of methodologies to generate innovative geo-information products and services. It is here where the TEP Urban project is supposed to initiate a step change by providing an open and participatory platform based on modern ICT technologies and services that enables any interested user to easily exploit Earth observation data pools, in particular those of the Sentinel missions, and derive thematic information on the status and development of the built environment from these data. Key component of TEP Urban project is the implementation of a web-based platform employing distributed high-level computing infrastructures and providing key functionalities for i) high-performance access to satellite imagery and derived thematic data, ii) modular and generic state-of-the art pre-processing, analysis, and visualization techniques, iii) customized development and dissemination of algorithms, products and services, and iv) networking and communication. This contribution introduces the main facts about the TEP Urban project, including a description of the general objectives, the platform systems design and functionalities, and the preliminary portfolio products and services available at the TEP Urban platform.

Floods are the most frequent natural disasters affecting the Moravian-Silesian region. Therefore a system that could predict flood extents and help in the operative disaster management was requested. The FLOREON + system was created to fulfil these requests. This article describes utilization of HPC (high performance computing) in running multiple hydrometeorological simulations concurrently in the FLOREON + system that should predict upcoming floods and warn against them. These predictions are based on the data inputs from NWFS (numerical weather forecast systems) (e.g. ALADIN) that are then used to run the rainfall-runoff and hydrodynamic models. Preliminary results of these experiments are presented in this article.

Part 5: Industrial Management and Other ApplicationsInternational audienceIn the future, the silicon technology will continue to reduce following the Moore’s law. Device variability is going to increase due to a loss in controllability during silicon chip fabrication. Then, the mean time between failures is also going to decrease. The current methodologies based on error detection and thread re-execution (roll back) can not be enough, when the number of errors increases and arrives to a specific threshold. This dynamic scenario can be very negative if we are executing programs in HPC systems where a correct, accurate and time constrained solution is expected. The objective of this paper is to describe and analyse the needs and constraints of different applications studied in disaster management processes. These applications fall mainly in the domains of the High Performance Computing (HPC). Even if this domain can have differences in terms of computation needs, system form factor and power consumption, it nevertheless shares some commonalities