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UID:pretalx-foss4g-2022-ZZEGLR@talks.staging.osgeo.org
DTSTART;TZID=CET:20220826T123000
DTEND;TZID=CET:20220826T130000
DESCRIPTION:ECMWF is a research institute and a 24/7 operational service\, 
 producing global numerical weather predictions and other data for a broad 
 community of users. To achieve this\, the centre operates one of the large
 st supercomputer facilities and data archives within the meteorological co
 mmunity. ECMWF also operates several services for the EU Copernicus progra
 mme to provide data for Climate Change\, Atmospheric monitoring and Emerge
 ncy services.\n\nAs part of ECMWF's Open data initiative\, more and more m
 eteorological data and web services are freely available to a wider commun
 ity. ECMWF's web services include an interactive web application to explor
 e and visualize its forecast data\, a Web Map Service (WMS) server and man
 y graphical products including geospatial weather diagrams so called Ensem
 ble (ENS) meteograms and vertical profiles.\n\nENS meteograms and vertical
  profile diagrams are among the ECMWF's most popular web products and pres
 ents ECMWF's multi-dimensional real-time ensemble forecast data for a give
 n position globally. They are freely available through various ECMWF web s
 ervices\,  and integrated on ECMWF's GIS based interactive web application
 . Datasets powering the dynamically generated diagrams are formed from a r
 olling archive of 10 days data\, updated twice a day and each update consi
 sts of data around half a Terabyte. An upcoming update on ECMWF's forecast
 ing system will increase the data size by a factor of 3-4 times in the nea
 r future.  In addition to ECMWF's forecast data\, similar services are req
 uested as part of various Copernicus projects producing different datasets
 .\n\nThis talk presents migrating legacy data structure used for ENS meteo
 gram datasets to a more flexible\, extensible\, and high performing one fi
 t to be used by GIS systems by using Free Open Source Software (FOSS). The
  new data structure uses Python ecosystem. The data preparation workflow a
 s well as the challenges and the solutions that are taken when dealing lar
 ge and frequently updated geospatial datasets are presented. Talk will als
 o include early experiments and experiences to offer these datasets as par
 t of OGC's Environmental Data Retrieval (EDR) API.
DTSTAMP:20260403T222554Z
LOCATION:Room Onice
SUMMARY:A high performing data retrieval system for large and frequently up
 dated geospatial datasets - Cihan Sahin
URL:https://talks.staging.osgeo.org/foss4g-2022/talk/ZZEGLR/
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