{"id":561,"date":"2018-06-27T08:14:15","date_gmt":"2018-06-27T08:14:15","guid":{"rendered":"http:\/\/www.bigdataocean.eu\/site\/?page_id=561"},"modified":"2018-07-27T15:36:51","modified_gmt":"2018-07-27T15:36:51","slug":"mare-protection","status":"publish","type":"page","link":"http:\/\/www.bigdataocean.eu\/site\/mare-protection\/","title":{"rendered":"MARE PROTECTION"},"content":{"rendered":"<p><strong>Current Status &amp; Challenges<\/strong><br \/>\nThe following scenario summarises the operational tools that are currently used as part of a Decision Support system, which provides essential information for the proper handling of a pollution accident in the marine environment. Furthermore, how the connection to a big data infrastructure could be notably beneficial will be explained.<\/p>\n<p><strong>Dispersion of the oil spill in the marine environmen<\/strong>t: Oil spill models constitute an essential element in contingency planning and in preparing effective response strategies to combat hazardous oil spills at sea. Such models rely on the ability to predict meteo-marine conditions of the sea through the use of cross-sectorial data, including atmospheric, wave and hydrodynamical numerical models. Integrating and combining the information on the location, rate, nature and characteristics of an oil spill, the derived forecasted fields is used to provide, in advance, some knowledge on the fate and track that the oil slick will follow in time. Such a chain of numerical model activities can be automated and run in operational mode to provide a round-the-clock service, relying on sufficient computing power, which is nowadays becoming more affordable.<\/p>\n<div id=\"attachment_551\" style=\"width: 1503px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2.jpg\"><img aria-describedby=\"caption-attachment-551\" loading=\"lazy\" class=\"size-full wp-image-551\" src=\"http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2.jpg\" alt=\"\" width=\"1493\" height=\"837\" srcset=\"http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2.jpg 1493w, http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2-300x168.jpg 300w, http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2-768x431.jpg 768w, http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2-1024x574.jpg 1024w, http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2-250x140.jpg 250w, http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2018\/06\/pilot2-150x84.jpg 150w\" sizes=\"(max-width: 1493px) 100vw, 1493px\" \/><\/a><p id=\"caption-attachment-551\" class=\"wp-caption-text\">Mare Protection<\/p><\/div>\n<p>The POSEIDON Oil Spill Model has been implemented at the Hellenic Centre for Marine Research (HCMR) and it is used operationally for the Aegean and Ionian Seas. It is a fully 3D oil spill model capable of simulating the movement, spreading and aging of the oil particles in the 3-D space. In the current operational implementation of POSEIDON Oil Spill Model (OSM) in the Greek seas, the model uses atmospheric data from the POSEIDON ETA weather forecasting system, wave data from the POSEIDON WAM Cycle 4 for Aegean and oceanographic data from the POSEIDON Aegean model. The POSEIDON OSM is a standard module of the POSEIDON operational Oceanography System that has been implemented and has operated in Greek seas since 2000. Tt was further developed during ESA\u2019s projects ROSES (2003-2004) and MARCOAST (2005-2008) and EU project ECOOP (2008-2011), while it is one of four numerical components of the multi-model MEDESS4MS project (2012-2015).<\/p>\n<p><strong>Challenges:<\/strong> The presented-above numerical modelling activity is widely used to support operational activities in the marine environment and thus the reliability of its output is of crucial importance. The integration of existing systems and data, in one unified big data infrastructure accompanied by proper services, and additional datasets of interest, could provide additional information that could increase the forecasting skill and improve the effectiveness of operational activities; and thus, have significant impact on marine, and environmental in general, protection.<\/p>\n<p><strong>BigDataOcean Solution<\/strong><br \/>\nAs already explained above, the oil spill dispersion models rely on the results of other numerical models that provide the necessary atmospheric, hydrodynamic and sea state input. Thus, what would add significant value is the integration and exploitation of additional information coming from different sectors that would significantly improve the input into the series of interconnected systems of numerical models.<br \/>\n<!--\nThe BigDataOcean oil spill use case scenario includes a hypothetical accident in the Aegean sea where the standard forecasting service would provide estimation of the oil spill dispersion in the marine environment based on the standard input provided by the numerical models suite (AS-IS situation).\nThrough BigDataOcean (TO-BE situation), existing data would be imported in the platform and integrated with relevant data available the big data infrastructure, originating from different sources and sectors. The additional cross-sectorial data to be ingested into the numerical models system could indicatively, yet not exhaustively, include:\n\n\n<ul>\n \t\n\n<li>Weather data from ships or from stations;<\/li>\n\n\n \t\n\n<li>Data from fixed stations (coastal\/offshore) about the atmospheric, hydrodynamic and sea state conditions;<\/li>\n\n\n \t\n\n<li>Relevant satellite data;<\/li>\n\n\n \t\n\n<li>Pollution reports from ships;<\/li>\n\n\n \t\n\n<li>AIS data. This kind of data can be used for a first approach in order to have an identification of the possible polluter.<\/li>\n\n\n<\/ul>\n\n\nAfter the data insertion, the oil spill forecasting service would be rerun, and would be expected to provide more reliable results for the area of the accident. It has to be noted that analytics and knowledge offered by BigDataOcean could further enrich the results of the existing spill model.\n\n<strong>Expected Benefits & KPIs<\/strong>\nThe resulting knowledge, models and enriched datasets can be further processed and released as products to environment protection organisations and maritime authorities for rapid intervention against oil spills originating from the sea, to control and limit impacts and damages on the coast and essential resources and structures. Such organisations could indicatively include the Greek Ministry of the Mercantile Marine, which is the responsible authority for combating the oil spill pollution in the marine environment, the Regional Authorities of the high-risk areas in case of an accident, Non-Governmental Organizations dealing with the protection of the marine environment and of the marine life. KPIs and\/or monitoring metrics will be applied during the whole process cycle in order to point out the differences in accuracy and reliability of the model output when inserting the additional dataset.\n--><br \/>\n<a href=\"http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2016\/12\/hcmr-LOGO-version-01-e1483972407554.jpg\"><img loading=\"lazy\" class=\"aligncenter size-medium wp-image-121\" src=\"http:\/\/www.bigdataocean.eu\/site\/wp-content\/uploads\/2016\/12\/hcmr-LOGO-version-01-300x267.jpg\" alt=\"\" width=\"300\" height=\"267\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Current Status &amp; Challenges The following scenario summarises the operational tools that are currently used as part of a Decision Support system, which provides essential information for the proper handling of a pollution accident in the marine environment. Furthermore, how the connection to a big data infrastructure could be notably<\/p>\n<p> <a class=\"continue-reading-link\" href=\"http:\/\/www.bigdataocean.eu\/site\/mare-protection\/\"><span>Continue Reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/pages\/561"}],"collection":[{"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/comments?post=561"}],"version-history":[{"count":11,"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/pages\/561\/revisions"}],"predecessor-version":[{"id":637,"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/pages\/561\/revisions\/637"}],"wp:attachment":[{"href":"http:\/\/www.bigdataocean.eu\/site\/wp-json\/wp\/v2\/media?parent=561"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}