Operational and Research Activities at ECMWF

1. Operational and Research Activities at ECMWF Renate Hagedorn European Centre for Medium-Range Weather Forecasts

2. ECMWF’s… …background and structure …research activities  Integrated Forecast System (IFS) …operational activities  production, delivery, archiving

3. Background • • Convention establishing ECMWF entered in force on 1st Nov 1975, • having been ratified by the following 13 Member states: • • Recognition of importance and potential to improve medium-range • weather forecasts with benefits to the •  European economy •  Protection and safety of population •  Development of meteorology in Europe / post university training •  Development of European industry in the field of data-processing • • Recognition that resources are needed on a scale exceeding those • normally practicable at national level Denmark Spain Ireland Netherlands Switzerland Sweden Belgium Germany France Yugoslavia Austria Finland United Kingdom

4. Today… …ECMWF is an independent international organization, supported by 18 member states 13 co-operating states Czech Republic Croatia Co-operating agreements: Estonia Hungary Latvia Iceland Montenegro Lithuania Morocco Romania Slovakia Serbia Slovenia

5. New Convention • Amendments to the ECMWF Convention were unanimously adopted by Council at its 62nd extraordinary session on 22 April 2005 • Finalization of the ratification process is expected by the middle of 2010 • The adopted amendments concern mainly:  allowing new Member States to join  enlarging ECMWF’s mission to environmental monitoring  re-defining some decision making processes (voting rights)  widening the possibilities for externally funded projects (e.g. EU)  extending official languages to all official languages in Member States (on a request-and-pay basis)

6. Objectives • Operational forecasting up to 15 days ahead (including waves) • R & D activities in forecast modelling • Data archiving and related services • Operational forecasts for the coming month and season • Advanced NWP training • Provision of supercomputer resources • Assistance to WMO programmes • Management of Regional Meteorological Data Communications Network (RMDCN)

7. ECMWF Budget 2010 Spain 7.95% Main Revenue 2010 Member States’contributions £36,703,200 Co-operating States’contributions £861,600 Other Revenue £1,275,000 Total£38,839,800 Germany 20.20% France 15.46% Luxembourg 0.23% Denmark 1.87% Greece 1.74% Belgium 2.71% Ireland 1.23% Main Expenditure 2010 Staff £15,199,900 Leaving Allowances& Pensions £3,298,600 ComputerExpenditure £15,809,000 Buildings £3,647,700 Supplies £884,600 Total £38,839,800 United Kingdom 16.43% Italy 12.66% Turkey 2.38% Netherlands 4.61% Sweden 2.66% Norway 2.13% Finland 1.42% Austria 2.16% Switzerland 2.89% Portugal 1.29% GNI Scale 2009–2011

8. Organizational structure Policy Advisory Committee 7-18 Members Scientific Advisory Committee 12 Members COUNCIL 18 Member States Technical Advisory Committee 18 Members Finance Committee 7 Members DIRECTOR Dominique Marbouty (France) (230) Advisory Committee on Data Policy 8-31 Members Advisory Committee of Co-operating States 12 Members Operations Walter Zwieflhofer (Austria) (111) Administration Ute Dahremöller (Germany) (25) Research ErlandKällén (Sweden) (90) Meteorological Division Erik Andersson (Sweden) (42) Computer Division Isabella Weger(Austria) (65) Model Division Martin Miller (UK) (24) Data Division Jean-Noel Thépaut (France) (37) Probabilistic Forecasting and Diagnostics Division Tim Palmer (UK) (19)

9. Principal Goal • Maintain the current, rapid rate of improvement of its global, medium-range weather forecasting products, with particular effort on early warnings of severe weather events.

10. Principal Goal

11. Principal Goal

12. Complimentary Goals • In addition to the principal goal of maintaining the current, rapid rate of improvements, the complimentary goals are: To improve the quality and scope of monthly and seasonal-to-interannual forecasts To enhance support to Member States national forecasting activities by providing suitable boundary conditions for limited-area models To deliver real-time analysis and forecasts of atmospheric composition To carry out climate monitoring through regular re-analyses of the Earth-system To contribute towards the optimization of the Global Observing System

13. Numerical Weather Prediction • The behaviour of the atmosphere is governed by a set of physical laws • Equations cannot be solved analytically, numerical methods are needed • Additionally, knowledge of initial conditions of system necessary • Incomplete picture from observations can be completed by data assimilation • Interactions between atmosphere and land/ocean important

14. Strategy • Development of a suitably comprehensive Earth-system assimilation capability to make best use of all available data • Development of a suitably comprehensive and integrated high-resolution Earth-system modelling facility • Development of the methodology of ensemble forecasting for medium-range and seasonal forecasting • Operational delivery of an enhanced range of meteorological and associated products • Maintenance and extension of the Centre’s scientific and technical collaborations

15. Research Department Model Division Martin Miller (UK) (24) Data Division Jean-Noel Thépaut (France) (37) Probabilistic Forecasting & Diagnostics Division Tim Palmer (UK) (19) Physical Aspects Anton Beljaars (Netherlands) (13) Satellite Data Peter Bauer (Germany) (17) Seasonal Forecast Franco Molteni (Italy) (9) Numerical Aspects AgatheUntch (Germany) (8) Data Assimilation Lars Isaksen (Denmark) (14) Predictability & Diagnostics Tim Palmer (UK) (8) Re-Analysis Project Dick Dee (Netherlands) (4) Ocean Waves Peter Janssen (Netherlands) (3)

16. ECMWF’s operational analysis and forecasting system The comprehensive earth-system model developed at ECMWF forms the basis for all the data assimilation and forecasting activities. All the main applications required are available through one integrated computer software system (a set of computer programs written in Fortran) called the Integrated Forecast System or IFS • Numerical scheme:  TL1279L91 (1279 waves around a great circle on the globe, 91 levels 0-80 km) semi-Lagrangian formulation 6,300,000,000,000,000 computations required for each 10-day forecast • Time step:  10 minutes • Prognostic variables:  wind, temperature, humidity, cloud fraction and water/ice content, ozone, pressure at surface grid-points • Grid:  Gaussian grid for physical processes, ~16 km, 194,804,064 grid points

17. Model grids T1279 ~16km T639 ~ 32km

18. The wave model • Coupled ocean wave model (WAM cycle4) •  2 versions: global and regional (European Shelf & Mediterranean) •  numerical scheme: irregular lat/lon grid, ~28km (HR), ~55km (EPS) • spectrum with 36 (30) frequencies and 36 (24) directions •  coupling: wind forcing of waves every 15 minutes, two way • interaction of winds and waves, sea state dep. drag coefficient •  extreme sea state forecasts: freak waves •  wave model forecast results can be used as a tool to diagnose • problems in the atmospheric model Numerical Methods and Adiabatic Formulation of Models: 12 - 16 April2010

19. Physical aspects, included in IFS • Orography (terrain height and sub-grid-scale characteristics) • Four surface and sub-surface levels (allowing for vegetation cover, gravitational drainage, capillarity exchange, surface / sub-surface runoff) • Stratiform and convective precipitation • Carbon dioxide (345 ppmv fixed), aerosol, ozone • Solar angle • Diffusion • Ground & sea roughness • Ground and sea-surface temperature • Ground humidity • Snow-fall, snow-cover and snow melt • Radiation (incoming short-wave and out-going long-wave) • Friction (at surface and in free atmosphere) • Sub-grid-scale orographic drag • Gravity waves and blocking effects • Evaporation, sensible and latent heat flux Parameterization of Diabatic Processes 17 – 27 May 2010

20. Starting a forecast: The initial conditions Data sources for the ECMWF Meteorological Operational System (EMOS)

21. Data Assimilation • Observations measure the current state, but provide an incomplete picture  Observations made at irregularly spaced points, often with large gaps  Observations made at various times, not all at ‘analysis time’  Observations have errors  Many observations not directly of model variables • The forecast model can be used to process the observations and produce a more complete picture (data assimilation)  start with previous analysis  use model to make short-range forecast for current analysis time  correct this ‘background’ state using the new observations

22. Analysis 12-hour forecast Data Assimilation Background Analysis Observations Every 12 hours ~ 60 million observations are processed to correct the 8 million numbers that define the model’s virtual atmosphere Model variables, e.g. temperature “True” state of the atmosphere 12 UTC 5 May 00 UTC 6 May 12 UTC 6 May 00 UTC 5 May

23. Data Assimilation • Observations measure the current state, but provide an incomplete picture  Observations made at irregularly spaced points, often with large gaps  Observations made at various times, not all at ‘analysis time’  Observations have errors  Many observations not directly of model variables see next eightdays • The forecast model can be used to process the observations and produce a more complete picture (data assimilation)  start with previous analysis  use model to make short-range forecast for current analysis time  correct this ‘background’ state using the new observations • The forecast model is very sensitive to small differences in initial conditions  accurate analysis crucial for accurate forecast  EPS used to represent the remaining analysis uncertainty

24. What is an ensemble forecast? Temperature Forecast time Initial condition Forecast Complete description of weather prediction in terms of a Probability Density Function (PDF)

25. Flow dependence of forecast errors 26th June 1995 26th June 1994 If the forecasts are coherent (small spread) the atmosphere is in a more predictable state than if the forecasts diverge (large spread)

26. • Test the system for 100 days:  30 x T>24ºC -> 30 x (100 – 20) = 2400  70 x T<24ºC -> 70 x ( 0 – 20) = -1400 +1000 Why Probabilities? • Open air restaurant scenario:  open additional tables: £20 extra cost, £100 extra income (if T>24ºC)  weather forecast: 30% probability for T>24ºC  what would you do? • Employing extra waiter (spending £20) is beneficial when probability for T>24 ºC is greater 20% • The higher/lower the cost loss ratio, the higher/lower probabilities are needed in order to benefit from action on forecast

27. ECMWF’s Ensemble Prediction Systems • Account for initial uncertainties by running ensemble of forecasts from slightly different initial conditions  singular vector approach to sample perturbations • Model uncertainties are represented by “stochastic physics” • Medium-range VarEPS (15-day lead) runs twice daily (00 and 12 UTC)  day 0-10: TL639L62 (~32km), 50+1 members  day 9-15: TL319L62 (~65km), 50+1 members once a week (Thu, 00): runs out to day-32 (monthly forecast) • Extended time-range EPS systems: seasonal forecasts  coupled atmosphere-ocean model (IFS & HOPE), ~125km  seasonal forecast (7 months lead) runs once a month, (12 months lead) every quarter Predictability, Diagnostics and Extended Range Forecasting 19 - 28 April 2010

28. Operations Department Computer Division Isabella Weger (Austria) (65) Meteorological Division Erik Andersson (Sweden) (42) Computer Operations Sylvia Baylis (UK) (26) Meteorological Applications Alfred Hofstadler (Austria) (8) Network and Computer Security Rémy Giraud (France) (9) Meteorological Operations David Richardson (UK) (13) Servers & Desktops Richard Fisker (Denmark) (8) Data & Services BaudouinRaoult (France) (11) Systems Software Neil Storer (UK) (8) Graphics Stefan Siemen (Germany) (5) User Support Umberto Modigliani (Italy) (5)

29. Current Computer Configuration

30. RMDCN Network

31. User support for special projects http://www.ecmwf.int/about/computer_access_registration/Special_Projects.html

32. ECMWF model suites • Deterministic high-resolution global atmospheric model  TL1279 91 levels; range=10 days • Medium-range ensemble prediction system  TL639 / TL319 62 levels; range=15 days  control + 50 perturbed members • Monthly forecast system  TL319 62 level (atm.), 1.4 º x 0.3-1.4º, 29 vertical levels (ocean)  51-member ensemble; range=32 days • Seasonal forecast system  TL159 62 level (atm.), 1.4 º x 0.3-1.4º, 29 vertical levels (ocean)  41-member ensemble; range=7 months

33. Main operational suites

34. Data Dissemination

35. The ECMWF archive • The largest NWP archive worldwide • Built since ECMWF operations started in 1979 • Holds more than 10 petabytes today • 6 terabytes added daily • Contains: • All data used • All analyses • All forecasts • Reanalyses • Fully accessible on-line to Member States users

36. MARS

37. ECMWF Data Server A new service that gives researchers immediate and free access to datasets from ECMWF. • DEMETER • ERA-40 • ERA-interim • ENACT • ENSEMBLES / GEMS - Monthly and daily data - Select area - GRIB or NetCDF - Plotting facility

38. Meteorological Operations • Daily report (data and forecast monitoring, unusual events,…) • Forecast verification • Development of new products (EFI, tropical cyclones,…) • Data and satellite monitoring • User guides / meetings

39. Met Ops daily report

40. Monitoring of model performance

41. Monitoring of model performance

42. Product Development

43. Forecast Products: 1979 1 forecast (200 km resolution) issued 5 days a week

44. Forecast Products: 2010 wide range of forecast products from deterministic high resolution forecast to probabilistic EPS products www.ecmwf.int/products/forecasts

45. Products for end users

46. More Information… http://www.ecmwf.int