1. Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Numerical weather prediction from short to long range Predicting uncertainty Pierre Eckert Head of regional office, MeteoSwiss, Geneva

2. 2 Numerical weather prediction Pierre Eckert ¦ September 2009 Topics Global models (ECMWF) Grids Parameters Data and initialisation Time evolution Products Regional models (COSMO) Chaos and ensemble forecasting (EPS) Probabilistic forecasts Monthly and seasonal forecasting Climate models

3. 3 Numerical weather prediction Pierre Eckert ¦ September 2009 Global model: horizontal grid Latitude-longitude gridding of the planet. Present resolution at ECMWF: about 25 km (ECMWF = European Centre for Medium range Weather Forecast) About 800’000 grid points

4. 4 Numerical weather prediction Pierre Eckert ¦ September 2009 Vertical levels Vertical levels follow the orography 91 levels in the ECMWF model 800’000 x 91 = 75’000’000 coordinate points

5. 5 Numerical weather prediction Pierre Eckert ¦ September 2009 Meteorological parameters  Température  Wind (2 horizontal dimensions)  Humidity (quantity of water vapour)  Quantity of liquid water  Quantity of ice Pressure and vertical wind are deduced (from temperature and horizontal wind)  6 variables on each of the 75’000’000 points  The state of the atmosphere is described by about 450 millions of numbers.

6. 6 Numerical weather prediction Pierre Eckert ¦ September 2009 Analysis The analysis procedure aims to determine all parameters of the model at a time t (for example 00z et 12z). Most of the available observations is used. These observations do not usually lie on the coordinate points of the model, nor at the eact analysis time. The analysis process is complex. The equilibrium of the model has to be kept « Incoherent » observations have to be rejected A lot of computing time is already used in this phase

7. 7 Numerical weather prediction Pierre Eckert ¦ September 2009 Observations from the ground

8. 8 Numerical weather prediction Pierre Eckert ¦ September 2009 Observations of the sea

9. 9 Numerical weather prediction Pierre Eckert ¦ September 2009 Radiosoundings

10. 10 Numerical weather prediction Pierre Eckert ¦ September 2009

11. 11 Numerical weather prediction Pierre Eckert ¦ September 2009 Geostationary satellites

12. 12 Numerical weather prediction Pierre Eckert ¦ September 2009 Polar satellites

13. 13 Numerical weather prediction Pierre Eckert ¦ September 2009 Time evolution of the model Once the initial state is known, the future evolution is computed. The set of equations from the atmosphere physics is applied (dynamics, thermodynamics) These equations allow to compute a new state of the model by steps of 10 minutes up to 10 days (240h) The equations are highly non-linear: the solutions are chaotic (see later)

14. 14 Numerical weather prediction Pierre Eckert ¦ September 2009 Example of equation: change of temperature + heat from compression - heat from expansion + head from condensation - heat from evaporation + solar radiation + …

15. 15 Numerical weather prediction Pierre Eckert ¦ September 2009

16. 16 Numerical weather prediction Pierre Eckert ¦ September 2009

17. 17 Numerical weather prediction Pierre Eckert ¦ September 2009 Products: pressure and humidity

18. 18 Numerical weather prediction Pierre Eckert ¦ September 2009 Products: pressure and humidity

19. 19 Numerical weather prediction Pierre Eckert ¦ September 2009 Products: rainfall

20. 20 Numerical weather prediction Pierre Eckert ¦ September 2009 Other global models GFS (NCEP, USA) UM, Unified Model (UKMO, UK) Arpège (MétéoFrance, France) GME (DWD, Allemagne) Japan Korea …

21. 21 Numerical weather prediction Pierre Eckert ¦ September 2009

22. 22 Numerical weather prediction Pierre Eckert ¦ September 2009 Regional models For the moment it is not possible to build models covering the whole planet with resolutions below 10 km (but we are not far!) Regional models are built in consequence in order to catch regional details in the short range (1-3). Several consortia or academic initiatives: COSMO, Aladin, HIRLAM, MM5, WRF,… It is possible to add very local information to the initial state: rainfall rates from radars, wind profilers,…

23. 23 Numerical weather prediction Pierre Eckert ¦ September 2009 COSMO-7: orography on Switzerland

24. 24 Numerical weather prediction Pierre Eckert ¦ September 2009 IFS (ECMWF) Model embedding COSMO-7 COSMO-2 COSMO-7 393x338 grid points 6.6km, 60 levels 3x72h forecasts per day COSMO-2 520x350 grid points 2.2km, 60 levels 8x 24h forecasts per day Radar

25. 25 Numerical weather prediction Pierre Eckert ¦ September 2009 Products: 7km rainfall

26. 26 Numerical weather prediction Pierre Eckert ¦ September 2009 Products: 2 km winds

27. 27 Numerical weather prediction Pierre Eckert ¦ September 2009 An approach to chaos The atmosphere is chaotic: this means: 1) there is an « attractor » of possible states. This is the climate. 2) the time evolution is very dependent on the initial conditions (butterfly effect). Toy model: the Lorenz attractor Weak dependencyStrong dependency

28. 28 Numerical weather prediction Pierre Eckert ¦ September 2009 An approach to chaos: Ensemble forecasting Ensemble forecasting (EPS: Ensemble Prediction System) aims at catching the dependency of the weather evolution from the initial state by generating small perturbation of this initial state (50 at ECMWF). The 50 models have a coarser resolution (~50 km)

29. 29 Numerical weather prediction Pierre Eckert ¦ September 2009 +24h

30. 30 Numerical weather prediction Pierre Eckert ¦ September 2009 +144h

31. 31 Numerical weather prediction Pierre Eckert ¦ September 2009 +144h

32. 32 Numerical weather prediction Pierre Eckert ¦ September 2009 « spaghetti » charts All members, 3 levels

33. 33 Numerical weather prediction Pierre Eckert ¦ September 2009 Ensemble mean and 4 scenarii Confidence index

34. 34 Numerical weather prediction Pierre Eckert ¦ September 2009 EPSgram: Probabilistic evolution of weather elements

35. 35 Numerical weather prediction Pierre Eckert ¦ September 2009 EPSgram

36. 36 Numerical weather prediction Pierre Eckert ¦ September 2009 Monthly and seasonal forecasts The model can be integrated for several weeks or months. The goal is not to produce a forecast for day 25 or week 12. But it is in principle possible to produce a mean forecast for the week 3 or the month 4. The ocean temperature cannot be kept constant during this long time. In addition to the atmospheric model, a model of the ocean has to be built. Variables: speed, temperature, salinity. This kind of forecast presents good results in the tropics, fair results in north America, poor results over central Europe. The monthly forecast is run once a week, the seasonal forecast once a month.

37. 37 Numerical weather prediction Pierre Eckert ¦ September 2009 Monthly forecast: Temperatures

38. 38 Numerical weather prediction Pierre Eckert ¦ September 2009 Prévision mensuelle: Températures Monthly forecast: skill

39. 39 Numerical weather prediction Pierre Eckert ¦ September 2009 Seasonal forecast: Sept, Oct, Nov 2009 Upper tercile

40. 40 Numerical weather prediction Pierre Eckert ¦ September 2009 Seasonal forecast: temperatures

41. 41 Numerical weather prediction Pierre Eckert ¦ September 2009 Seasonal forecast: temperatures

42. Seasonal forecast: skil in summer Temperature

43. 43 Numerical weather prediction Pierre Eckert ¦ September 2009 Climate models Climate models are basically similar to seasonal models They include a model of the ocean The difference is that some properties of the atmosphere or boundary conditions can be changed: Solar irradiation Aerosols, volcanic ashes Greenhouse gases: CO 2, methane,… The new equilibrium is computed

44. 44 Numerical weather prediction Pierre Eckert ¦ September 2009 Atmospheric radiation balance Absorption by the atmosphere

45. 45 Numerical weather prediction Pierre Eckert ¦ September 2009 Model simulations of the past climate (IPCC 2001, confirmed by IPCC 2007) Source: IPCC 2007; Max Planck Institut Hamburg, 2007 Observed temperature Range of model simulations Mean of model simulations Range of model simulations Mean of model simulations Natural forcing only Natural and anthropogenic forcing 190019201940196019802000 190019201940196019802000 0.0 +0.5 +1.0 -0.5 Anomaly in °C 0.0 +0.5 +1.0 -0.5 Anomaly in °C Observed temperature

46. 46 Numerical weather prediction Pierre Eckert ¦ September 2009 Projected change of the global temperature until 2100 (IPCC 2007) Concentration of greenhouse gases in CO 2 -equivalent: B1: 600 ppm A1T: 700 ppm B2:800 ppm A1B: 850 ppm A2:1250 ppm A1Fl:1550 ppm Best estimate: +1.8 ° to +4.0° C 1250 ppm 600 ppm 850 ppm Year Changes in global surface temperature Constant greenhouse gases concentration 20est century B1 A1B A2 200021001900 °C 6.0 4.0 5.0 3.0 2.0 1.0 0.0 A1Fl B2 A1T

47. 47 Numerical weather prediction Pierre Eckert ¦ September 2009 Conclusions Numerical weather models are able to use a lot of measurements to produce useful forecasts in the medium range (up to ~6 days) to reproduce structures down to the kilometre scale They have to be interpreted by professional meteorologists They are able to reproduce the past climate… … and to estimate the future climate

48. 48 Numerical weather prediction Pierre Eckert ¦ September 2009 Discussion How to address climate issues during weather presentations? How “normal” is the weather today, tomorrow? Is this storm exceptional Explain that the fact that a cold January is not incompatible with long term warming. Give advice on reducing use of fossil fuels (heating, traffic,…)