An innovative newly developed modular and standards based Decision Support System (DSS) is presented which forms part of the German Indonesian Tsunami Early Warning System (GITEWS). The GITEWS project stems from the effort to implement an effective and efficient Tsunami Early Warning and Mitigation System for the coast of Indonesia facing the Sunda Arc along the islands of Sumatra, Java and Bali. The geological setting along an active continental margin which is very close to densely populated areas is a particularly difficult one to cope with, because potential tsunamis' travel times are thus inherently short. National policies require an initial warning to be issued within the first five minutes after an earthquake has occurred. There is an urgent requirement for an end-to-end solution where the decision support takes the entire warning chain into account. The system of choice is based on pre-computed scenario simulations and rule-based decision support which is delivered to the decision maker through a sophisticated graphical user interface (GUI) using information fusion and fast information aggregation to create situational awareness in the shortest time possible. The system also contains risk and vulnerability information which was designed with the far end of the warning chain in mind – it enables the decision maker to base his acceptance (or refusal) of the supported decision also on regionally differentiated risk and vulnerability information (see Strunz et al., 2010). While the system strives to provide a warning as quickly as possible, it is not in its proper responsibility to send and disseminate the warning to the recipients. The DSS only broadcasts its messages to a dissemination system (and possibly any other dissemination system) which is operated under the responsibility of BMKG – the meteorological, climatological and geophysical service of Indonesia – which also hosts the tsunami early warning center. The system is to be seen as one step towards the development of a "system of systems" enabling all countries around the Indian Ocean to have such early warning systems in place. It is within the responsibility of the UNESCO Intergovernmental Oceonographic Commission (IOC) and in particular its Intergovernmental Coordinating Group (ICG) to coordinate and give recommendations for such a development. Therefore the Decision Support System presented here is designed to be modular, extensible and interoperable (Raape et al., 2010)
A certainty model and appropriate visualization techniques are presented which are applied in a newly developed Decision Support System (DSS) for tsunami early warning deployed in Jakarta, Indonesia. Our decision support approach makes use of multi-sensor fusion and pre-computed tsunami scenario simulations to create situational awareness as basis for reasonable early warning. As the Indonesian coastline is prone to near-field tsunami scenarios decision making must take place under time-critical conditions based on incomplete and uncertain information. In order to reduce the probability and the consequences of a false decision, we have developed and employed a certainty model which implies a classification of imperfect information suitable for the tsunami early warning domain and the quantification of imperfect data. The model is mapped onto and supported by appropriate visual representations.
This article presents and analyses the modular architecture and capabilities of CODE-DE (Copernicus Data and Exploitation Platform-Deutschland, www.code-de.org), the integrated German operational environment for accessing and processing Copernicus data and products, as well as the methodology to establish and operate the system. Since March 2017, CODE-DE has been online with access to Sentinel-1 and Sentinel-2 data, to Sentinel-3 data shortly after this time, and since March 2019 with access to Sentinel-5P data. These products are available and accessed by 1,682 registered users as of March 2019. During this period 654,895 products were downloaded and a global catalogue was continuously updated, featuring a data volume of 814 TByte based on a rolling archive concept supported by a reload mechanism from a long-term archive. Since November 2017, the element for big data processing has been operational, where registered users can process and analyse data themselves specifically assisted by methods for value-added product generation. Utilizing 195,467 core and 696,406 memory hours, 982,948 products of different applications were fully automatically generated in the cloud environment and made available as of March 2019. Special features include an improved visualization of available Sentinel-2 products, which are presented within the catalogue client at full 10 m resolution.
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