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Big Data in Meteorology

Big Data in Meteorology. Dr. Jürgen Seib Deutscher Wetterdienst E-mail: juergen.seib@dwd.de. Questions. How big is meteorological data? What are the challenges? What are the solutions?. best. Meteorological input data. MODIS AQUA. NOAA POES. METOP (EPS). SUOMI NPP. METEOSAT.

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Big Data in Meteorology

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  1. Big Data in Meteorology Dr. Jürgen Seib Deutscher Wetterdienst E-mail: juergen.seib@dwd.de

  2. Questions • How big is meteorological data? • What are the challenges? • What are the solutions? best

  3. Meteorological input data

  4. MODIS AQUA NOAA POES METOP (EPS) SUOMI NPP METEOSAT FENGYUN 3 MODIS TERRA FENGYUN 1D JPSS1 Weather Satellites Higher resolution Color Visible channel by night

  5. Satellite data for numerical weather prediction

  6. Observations … now +1 hour +3 hours +4 hours +2 hours Weather forecasts ProductGeneration Products Numerical weather prediction

  7. Future Past Climate and weather forecasts 100 years 10 years 1 year 1 month today • Climate projections • Decade forecasts • Seasonal forecasts • Monthly forecasts • Medium range forecasts (72-360 hours) • Short range forecasts (12-72 hours) • Shortest range forecasts (2-12 hours) • Nowcasting ( < 2 hours) • Climate monitoring

  8. COSMO-DE x = 2.8 km COSMO-EU x = 7 km ICON x = 20 km DWD NWP model suite +EPS (20 members) COSMO-DE: Grid spacing: 2.8 km 421 * 461 * 50 grid points Forecast range: 21 hours Runs per day: 8 COSMO-EU: Grid spacing: 7 km 665 * 657 * 40 grid points Forecast range: 78 hours Runs per day: 4 ICON: Grid spacing: 20 km 1482250 * 60 grid points Forecast range: 174 hours Runs per day: 2 Model output size per day: ~2.5 TBytes

  9. Numerical weather forecast data at DWD 6000 5400 4800 4200 3600 TByte 3000 2400 Ensemble technique 1800 1200 600 0 2004 2005 2006 2007 2008 2009 2010 2011 2012

  10. What are the challenges? • 500.000 short range forecasts • 240.000 aviation briefings annually • 20.000 warnings annually • 8.000 expertises p.a. • Data + model products • Public weather service: basic warnings and forecasts, climate information • Deliver the right data to the right people • Efficient storage and access in time • new analysis tasks: • - earlier storm tracking • - better climate analysis • - optimization problems in aviation and energy • Privacy and security issues

  11. Use case 1: Extreme weather events

  12. Use case 2: Cross Section for flight from Frankfurt to Rome

  13. Use case 3: Provide Big Data in Apps • Standard interfaces to Big Data • Many users • Detect most valuable data in Big Data

  14. What are the solutions? • Fast computers and fast storage systems • Very fast computers and very fast storage systems • Even faster computers and even faster storage systems

  15. High performance computing at DWD since 1966 8 TeraFLOP/s 4 TeraFLOP/s

  16. Cray XC30 NECSX-9 IBMpSeries CrayT3E Cray YMP Cyber 76 CDC-3800 High performance computing at DWD since 1966 1 TeraFLOP/s 1 GigaFLOP/s 1 MegaFLOP/s

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