1. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 1 / 95 Operational forecasting at ECMWF: Science, Components and Products  Ervin Zsoter  ECMWF, Meteorological Operations Section  ervin.zsoter@ecmwf.int With contributions from: Renate Hagedorn, David Richardson, Antonio Garcia Mendez, Gerald van der Grijn, Lars Isaksen and others

2. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 2 / 95 Outline  ECMWF as an operational and research centre  EMOS – ECMWF Meteorological Observational System  Quality control at ECMWF  Important characteristics of the ECMWF’s operational analysis and forecasting system  ECMWF 4D-VAR data assimilation system  Model computational characteristics  Model performance  Some applications  Different forecast products

3. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 3 / 95 ECMWF as an organisation ECMWF is an independent international organization, supported by 18 member states and 8 co-operating states Co-operating states: Iceland Czech Republic Slovenia Romania Serbia and Montenegro Hungary Croatia Estonia Convention establishing ECMWF entered into force on 1st Nov 1975 Co-operating organisations:

4. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 4 / 95 ECMWF Budget 2006 Main Revenue 2006 Member States’ contributions£27,460,600 Co-operating States’ contributions £425,100 Other Revenue£1,454,600 Total £29,340,300 Main Expenditure 2006 Staff£12,961,900 Leaving Allowances & Pensions£1,807,500 Computer Expenditure£11,785,900 Buildings£1,858,000 Supplies£927,000 Total£29,340,300

5. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 5 / 95 Objectives of the centre  Development of global models and data assimilation systems for the dynamics, thermodynamics and composition of the Earth’s fluid envelope and interacting parts of the Earth-system  Preparation and distribution of medium-range weather forecasts  Scientific and technical research directed towards improving the quality of these forecasts  Collection and storage of appropriate meteorological data  Make available research results and data to Member States  Provision of supercomputer resources to Member States  Assistance to WMO programmes  Advanced NWP training

6. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 6 / 95 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.  Impressive improvement in the quality of the NWP  2-3 days over 15-20 years

7. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 7 / 95 Operational activities at ECMWF  Observations –Acquisition / Pre-processing / Quality control / Bias correction  Data assimilation –Dynamical fit to observations  Forecasts  Product dissemination and archiving  Verification –Operational / pre-operational validation  Data Monitoring

8. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 8 / 95 Data sources for the ECMWF Meteorological Operational System (EMOS) Number of observed data assimilated in 24 hours 13 th February 2006

9. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 9 / 95 Conventional observations used MSL Pressure, 10m-wind, 2m-Rel.Hum. BUOY: MSL Pressure, Wind-10m Wind, Temperature, Spec. Humidity PILOT/Profilers: Wind Aircraft: Wind, Temperature SYNOP/METAR/SHIP: TEMP: Land - ASAP - Dropsonde

10. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 10 / 95

11. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 11 / 95 Positive trend in the number of Radiosondes reaching the upper Startosphere

12. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 12 / 95 Manual obs Automatic obs

13. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 13 / 95 NOAA AMSUA/B HIRS, AQUA AIRS DMSP SSM/I SCATTEROMETERS GEOS TERRA / AQUA MODIS OZONE 28 satellite data sources used in the operational ECMWF analysis

14. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 14 / 95 Satellite data important  Satellite measurements are increasingly important: –Global coverage (often only source of observations over ocean and remote land) –High spatial and temporal resolution –Decrease in conventional observing networks (fewer radiosonde stations)  But satellite data are not easy to use: –Satellites do not measure the model variables (temperature, wind, humidity) –They measure radiances, so either use derived products (e.g. cloud motion and scatterometer winds) or calculate ‘model radiances’ and compare with observations  Recent developments in data assimilation are designed to improve the use of satellite data –Variational data assimilation: can use radiance data directly –Added model levels in upper stratosphere allow use of additional satellite data –4D-Var: use observations at appropriate time –Increased resolution – more in agreement with resolution of measurements

15. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 15 / 95 Example: Tropical cyclone Bonnie near Florida satellite data complement conventional data L. Isaksen ‘Assimilation of ERS-1 and ERS-2 scatterometer winds in ERA-40’ ECMWF ERA-40 proceedings 2002

16. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 16 / 95 Large increase in number of observations used  Especially number of satellite data increases  A scientific and technical challenge

17. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 17 / 95 Observations for one 12h 4D-Var cycle 0900-2100UTC 26 March 2006  Synop: 389.000 (0.49%)  Aircraft: 362.000 (0.46%)  Dribu: 20.000 (0.03%)  Temp: 135.000 (0.17%)  Pilot: 108.000 (0.14%)  AMV’s: 2.811.000 (3.56%)  Radiance data: 74.825.000 (94.81%)  Scat: 269.000 (0.34%) TOTAL: 78.918.000 (100.00%)  Synop: 60.000 (1.84%)  Aircraft: 179.000 (5.50%)  Dribu: 5.600 (0.17%)  Temp: 67.000 (2.06%)  Pilot: 48.000 (1.48%)  AMV’s: 127.000 (3.90%)  Radiance data: 2.646.000 (81.34%)  Scat: 122.000 (3.75%) TOTAL: 3.253.000 (100.00%) ScreenedAssimilated 99% of screened data is from satellites86% of assimilated data from satellites

18. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 18 / 95 Data extraction Thinning Skipped data to avoid Over sampling Even so departures from FG and ANA are generated and usage flags also Blacklist Data skipped due to systematic bad performance or due to different considerations (e.g. data being assessed in passive mode) Departures and flags available for further assessment 4DVAR QC Rejections Used data  increments Check out duplicate reports Ship tracks check Hydrostatic check ANALYSIS Observations – Quality control - Analysis

19. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 19 / 95 Data inputData assimilation OI 3DVAR 4DVAR Feedback files (BUFR) ODB Raw observation Departures (FG & AN) Flags (data used, thinned, rejected) Monthly BUFR files for different Obs types Long term statistics Observations – Quality control - Analysis

20. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 20 / 95 Data Monitoring (Procedures)  The basic information is included in the feedback files or ODB (feedback from the assimilation scheme)  The statistics are normally computed by comparing the observations with a FG (6 or 12 hours forecast) –Model independent statistics should be used also  Co-locations  But the quality of those forecasts is not the same everywhere  no fixed criteria should be applied when assessing data quality

21. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 21 / 95 Blacklists  The idea behind the blacklist usage is to remove from the system observations with a systematic bad performance. A blacklisted observation is considered as passive data in the data assimilation  The blacklist at ECMWF is flexible enough to consider partial blacklisting depending on –Parameters, areas, atmospheric layers, time cycles –And of course different observation types……. –MetOps Data Monitoring elaborates a proposal to update the blacklist which then is discussed with HMOS and HDA. In cases with heavy changes sensitivity experiments are carried out before implementing the new blacklist

22. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 22 / 95 Quality problems in Asia & Russia Blacklists

23. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 23 / 95 Quality problems in Africa and southern Asia Blacklists

24. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 24 / 95 NH All data Used data: Blacklist and 4DVAR QC applied Reduced random deviation Ob-FG Ob-AN What’s the benefit of using a blacklist?

25. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 25 / 95 All data considered What’s the benefit of using a blacklist?

26. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 26 / 95 Blacklist applied What’s the benefit of using a blacklist?

27. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 27 / 95 Blacklist plus 4DVAR Quality Control applied What’s the benefit of using a blacklist?

28. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 28 / 95 Site’s directional setting changed by 6.5 degrees

29. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 29 / 95 Strong biases related to wrong station heights in the catalogue Example for data monitoring – SYNOP pressure bias correction

30. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 30 / 95 Bias correction applied Example for data monitoring – SYNOP pressure bias correction

31. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 31 / 95 Pressure bias correction at ECMWF  Applied to Synop, Ship, Buoy and Metar data when needed  OI and Kalman filter schemes run in parallel –OI is used for the corrections although Kalman filtering can be switch on on request  The scheme is not applied when –The difference between the station height and the model orography is larger then 200 hPa –The observation is RDB flagged –The history of the station is not long enough

32. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 32 / 95  Time series of Original Ps departure, Ps bias estimate and Corrected Ps departure for station 82353 (Dec-Apr ’05)  Once the sample size (30) was reached (Warm-up period) bias correction kicked in  Long-term bias of about -3hPa was recognised and corrected for  Station height is thought to be correct and real reason for bias is unknown  If not bias corrected the station was just surviving the “First Guess” check but to be rejected by the analysis check  When bias corrected, the station survived all the checks and was successfully used in the analysis Warm-up ≈3hPa Bias Adaptive bias correction scheme for surface pressure data

33. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 33 / 95 Example for data monitoring – SYNOP pressure bias correction

34. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 34 / 95 Example for data monitoring – SYNOP pressure bias correction

35. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 35 / 95 Example for data monitoring – SYNOP pressure bias correction

36. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 36 / 95 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 Spectral model - T L 799L91 (799 waves around a great circle on the globe, 91 hybrid vertical levels 0-80 km (0.01 hPa)) Semi-Lagrangian time scheme 12 minutes timestep  Prognostic variables: wind, temperature, humidity, cloud fraction and water/ice content, pressure at surface grid-points, ozone  Grid: Gaussian grid for physical processes, ~25 km, 76,757,590 grid points (843,490 on the surface)

37. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 37 / 95 Spectral and grid point representations  ECMWF model uses both spectral and grid point representations  Most upper air model variables (wind, temperature) are stored as spectral fields  Horizontal derivatives of these variables are calculated in spectral space  Surface variables and upper air humidity are stored in grid point space  Dynamical tendencies and physical parameterizations are calculated in grid point space  Resolution is the same in physical (grid point) and spectral space  Grid: – Gaussian grid for physical processes, ~25 km, 76,757,590 grid points (843,490 / level) –‘Gaussian grid’ is regular in latitude, almost regular in longitude –On regular grid (same number of points on each latitude row) points get closer together nearer the poles –‘Reduced Gaussian grid’ keeps distance between points nearly constant over globe

38. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 38 / 95 Operational model levels

39. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 39 / 95 Operational model grid (reduced Gaussian)

40. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 40 / 95 T799 orography, grid spacing ~25 km Model approximations: orography and spatial resolution High spatial resolution is needed to impose accurate boundary conditions. For example, the representation of the orography becomes more realistic with increased horizontal resolution. T255 orography, grid spacing ~80 km

41. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 41 / 95 ECMWF model 10m wind (T799, 25 km)

42. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 42 / 95 Katrina (2005 Aug): 90h forecasts - T511 versus T799 Central pressure 940hPa, 448mm/24h rain Central pressure 909hPa, 785mm/24h rain T511 T799

43. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 43 / 95 Hurricane Gordon – T799 forecast AN 30 hrs 78 hrs 126 hrs

44. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 44 / 95 Limitation: Model grid box still large Grid box 25 km x 25 km

45. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 45 / 95 Physical processes in the ECMWF model

46. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 46 / 95 Data assimilation for weather prediction A short-range forecast provides an estimate of the atmosphere that is compared with the observations. The two kinds of information are combined to form a corrected atmospheric state: the analysis. Corrections are computed and applied twice per day. This process is called ‘Data Assimilation’. The FORECAST is computed on a grid over the globe. The meteorological OBSERVATIONS can be taken at any location in the grid. The computer model’s prediction of the atmosphere is compared against the available observations, in near real time

47. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 47 / 95 Observations= “True” state of the atmosphere Time Model variables, e.g. temperature 00 UTC 13 March 12 UTC 13 March 00 UTC 14 March 12 UTC 14 March 4D-Var Data assimilation Analysis values = Background values = 12-hour forecast Analysis

48. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 48 / 95  Observation minus model differences are computed at the observation time using the full forecast model at T799 (25 km) resolution  4D-Var finds the 12-hour forecast evolution that optimally fits the available observations. A linearized forecast model is used in the minimization process based on the adjoint method (2 minimisation loops – T95/T255)  It does so by adjusting surface pressure, the upper-air fields of temperature, wind, specific humidity and ozone  The analysis vector consists of 30,000,000 elements at T255 resolution (80 km) A few 4D-Var Characteristics All observations within a 12-hour period (~3,300,000) are used simultaneously in one global (iterative) estimation problem 9z 12z 15z 18z 21z

49. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 49 / 95 ECMWF 4D-Var procedure Use all data in a 12-hour window (0900-2100 UTC for 1200 UTC analysis) 1.Group observations into ½ hour time slots 2.Run the T799 (25km) high resolution forecast from the previous analysis and compute “observation”- “model” differences 3.Adjust the model fields at the start of assimilation window (0900 UTC) so the 12-hour forecast better fits the observations. This is an iterative process using a lower resolution linearized model T255 (80 km) and its adjoint model 4.Rerun the T799 high resolution model from the modified (improved) initial state and calculate new observation departures 5.The 3-4 loop in repeated twice to produce a good high resolution estimate of the atmospheric state – the result is the ECMWF analysis

50. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 50 / 95 Multi-incremental quadratic 4D-Var at ECMWF T799L91 T95L91 T255L91

51. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 51 / 95 Analysis increments: 1 st and 2 nd minimization 1 st minimization: T95 T increments 2 nd minimization: Additional T159 T increment Most of the increment is formed at the lower resolution with smaller additions and corrections obtained at the higher resolution. Temperature level 60 (10metre). 0.2K contours (blue is negative; red is positive )

52. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 52 / 95 Forecast versus observations 12-hour forecast temperature change Correction, as a result of data assimilation The corrections are ~10 times smaller than the 12-hour forecast temperature change

53. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 53 / 95 Tropical cyclone LILI - Impact of NSCAT data in 4D-Var No SCAT analysis First guess MSL pressure S.M. Leidner, L. Isaksen and R.S. Hoffman ‘Impact of NSCAT Winds on Tropical Cyclones in the ECMWF 4DVAR assimilation system’ Mon. Wea. Rev. 131,1,3-26 (2003) First guess MSL pressure Analysis MSL pressure Analysis increments NSCAT analysis

54. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 54 / 95 4D-Var is using more a-synoptic data than 3D-Var 4D-Var is using more data from frequently reporting stations. The plots show the use of SYNOP surface pressure observations. Column height gives the number of observations available, while the shaded part displays those actually used in the assimilation. 4D-Var SYNOP Screening 3D-Var SYNOP Screening 3D-Var is like 4D-Var without the time dimension. The analysis is performed at synoptic times only (0000, 0600, 1200 and 1800 UTC). Mostly only data valid a synoptic time is used. The 12 hour forecast evolution is NOT an integral part of the analysis.

55. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 55 / 95 4D-Var versus 3D-Var and Optimum Interpolation  4D-Var is comparing observations with background model fields at the correct time  4D-Var can use observations from frequently reporting stations  The dynamics and physics of the forecast model in an integral part of 4D-Var, so observations are used in a meteorologically more consistent way  4D-Var combines observations at different times during the 4D-Var window in a way that reduces analysis error  4D-Var propagates information horizontally and vertically in a meteorologically more consistent way  More complex: needs linearized perturbation forecast model and its adjoint to solve the cost function minimization problem efficiently

56. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 56 / 95 4D-Var versus 3D-Var performance 6h T319 3DVAR 12h T511 3DVAR 6h T319 4DVAR N. HEM S. HEM

57. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 57 / 95 Computational cost of the model  Higher horizontal resolution  31 more vertical levels  12 min timestep instead of 15 min (T511)  Altogether 4 times more floating point operations are required to complete a 10-day forecast than with the T511 version  1.700.000.000.000.000 operations

58. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 58 / 95 02:00 (w aiting 5h to 17h for observation to arrive) 12h 4D-Var, obs 09-21Z 18 UTC analysis (DCDA) 12h FC 6h 4D-Var 21-03Z 00 UTC analysis (DA) T799 10 day forecast 51*T255 EPS forecasts 03:40 03:55 04:00 ( Waiting 1h to 7h for observations to arrive) 05:10 06:25 05:20 Time (UTC) Dissemination 07:25 Operational schedule for 0000UTC cycle Early delivery suite introduced June 2004

59. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 59 / 95 (G.-R. Hoffman) * * * Supercomputer performance at ECMWF 1978-2003 Mflops/s Peak performance

60. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 60 / 95 2004/2006 – A significant Performance increase Peak performance 7.6 Gflops per processor IBM p690 2 x 960 processors Peak performance 5.2 Gflops per processor 8 processors per shared memory node Switch 350 Mbytes/s 20062002 Switch 2000 Mbytes/s (Deborah Salmond/Sami Saarinen ) IBM p575+ 2 x 2400 processors 16 processors per shared memory node

61. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 61 / 95 T511 1-day Forecast on IBM CPUs

62. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 62 / 95 768 1024 1536 512 4D-Var T799/T95/T255 with 91 levels on present ECMWF IBM system

63. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 63 / 95 Time series Z500 N Hemisphere – against analysis

64. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 64 / 95 Time series Z500 N Hemisphere – against radiosondes

65. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 65 / 95 ECMWF Re-analysis project (ERA)  Main objective is to promote the use of global analysis of the state of the atmosphere, land and surface conditions over the period  ERA-15 1979 – 1993  ERA-40 1957 - 2002 –T159L60 –3DVAR  ERA interim 1989- –T255L91 –12h 4DVAR

66. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 66 / 95 Different application of the ECMWF products

67. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 67 / 95 Link with limited-area ensemble systems Over Europe, there are 4 operational Limited-area EPSs (SRNWP-PEPS, COSMO- LEPS, NORLAMEPS and PEACE) that produce daily 81 forecasts with horizontal resolution ranging from 7 to 120 km, and with forecast length ranging from 30 to 120 hours. 8 further centres (Met Office, INM, DMI, HMS, MeteoSwiss, SAR, PIED-SE) are developing and testing LEPSs. Studies have shown that compared to global EPSs, limited-area EPSs are better able to predict small-scale, local phenomena. - Boundary conditions from the global ECMWF model This figure shows the t+96h forecast of the probability of total precipitation exceeding 20mm/d given by the EPS (left) and the COSMO-LEPS system for 29 Aug 2003 (Ticino flood).

68. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 68 / 95 Hydrological application - EFAS the European Flood Alert System EFAS is a forecasting tool designed to give early-warnings for European rivers with catchments in excess of 2000 km. A pre-operational prototype is under testing at the Joint Research Center (JRC, Ispra). The system uses meteorological inputs from DWD (forecasts up to 7 days), ECMWF (high-resolution and ensemble forecasts up to 10 days) and aims to provide single and probabilistic predictions. This figure shows the prediction of the risk of flooding from 28 Oct 2004 for the subsequent 10-days computed using the ECMWF and DWD high-resolution forecasts (left) and the EPS (right).

69. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 69 / 95 ECMWF operational system 2006 – Forecast Products  Data assimilation (4D-VAR) –Four-dimensional variational data assimilation based on T799 (~25 km) / T255 (~80 km) / T95 (~200 km) horizontal resolution and 91-level vertical resolution (4 times a day)  Medium-range atmospheric global model –High resolution deterministic: T799 (~25 km) 91-level high resolution model for single deterministic forecast, twice a day up to 10 days –Ensemble: T399 (~50 km) 62-level model for 50-member ensemble forecasts, twice a day up to 15 days  Coupled ocean wave model (WAM cycle4) –2 versions: global and regional (European Shelf & Mediterranean) –Numerical scheme: irregular lat/lon grid, 40 km spacing spectrum with 30 frequencies and 24 directions –Coupling: wind forcing of waves every 15 minutes, two way interaction of winds and waves –Extreme sea state forecasts: freak waves –Wave model forecast results can be used as a tool to diagnose problems in the atmospheric model  Monthly forecast system  Seasonal forecast system

70. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 70 / 95 Global forecasts (deterministic, fields) Mean Sea Level Pressure + Rain (06-18UTC) Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC 500 hPa height and 850 hPa temperature

71. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 71 / 95 Global forecasts (deterministic, fields) T2m and 30m-winds Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC Cloud cover (high, medium, low)

72. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 72 / 95 Global forecast to ten days from 00 and 12 UTC at 50 km resolution ECMWF deterministic Ocean wave forecasts European waters forecast to five days from 00 and 12 UTC at 25 km resolution AFRICA !!!!

73. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 73 / 95 The Ensemble Prediction System (EPS)  A Stochastic Medium-range model (EPS) –Spans the unstable sub-space of initial conditions with a Gaussian samples of 50 T42 singular vectors + 5 per tropical target (TC) –Runs with stochastic perturbations of physical tendencies –T L 399/L62; range = 15 days – Schedule: twice per day: 00UTC (all products available before 1000UTC) 12UTC (all products available before 2200UTC)  Ensemble Forecasting (Thursday afternoon)

74. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 74 / 95 EPS forecasts: time series (EPSgram) EPSgram for Pretoria Base Friday 27/10/06 00UTC

75. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 75 / 95 EPS forecasts (field probabilities) Probability of 10m wind speed more than 10 m/s Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC Probability of precipitation more than 1mm in 24 hours

76. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 76 / 95 EPS forecasts (post-processed products) Extreme forecast index for 2m temperature Base Fri. 10/03/06 00UTC, Valid Tue 14/03/06 12UTC

77. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 77 / 95 Katrina forecasts (days from landfall) 4 days before landfall

78. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 78 / 95 Monthly forecasting  Coupled atmosphere / ocean model  Atm.: T159 (~125 km) 62 vertical levels (same model as oper)  Ocean: 29-level, 0.3° equator - 1° mid-latitudes  51 member ensemble  Runs once a week up to 32 days  Compared to 5 forecasts for same day over last 12 years –60 member ensemble  Results interpreted in terms of anomalies and probabilities –For example: probability that 2m temperature averaged over day 12 to 18 is in the upper/middle/lower tercile  Products become available every Thursday at 22UTC

79. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 79 / 95 Monthly forecast Probability that 2m temperature is in the upper tercile (third) of the climate distribution - warmer than normal Base Thu 26-10-2006. Valid days 5-11 (30-10 to 0511)

80. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 80 / 95 Monthly forecast Probability that 2m temperature is in the upper tercile (third) of the climate distribution - warmer than normal Base Thu 19-10-2006. Valid days 12-18 (30-10 to 0511)

81. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 81 / 95 Monthly forecast Probability that 2m temperature is in the upper tercile (third) of the climate distribution - warmer than normal Base Thu 12-10-2006. Valid days 19-25 (30-10 to 0511)

82. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 82 / 95 Monthly forecast Probability that precipitation is in the upper tercile (third) of the climate distribution – more wet than normal Base Thu 26-10-2006. Valid days 5-11 (30-10 to 0511)

83. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 83 / 95 Monthly forecast Probability that precipitation is in the upper tercile (third) of the climate distribution – more wet than normal Base Thu 19-10-2006. Valid days 12-18 (30-10 to 0511)

84. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 84 / 95 Monthly forecast Probability that precipitation is in the upper tercile (third) of the climate distribution – more wet than normal Base Thu 12-10-2006. Valid days 19-25 (30-10 to 0511)

85. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 85 / 95 Monthly forecast performance over the Northern Extratropics Monthly Forecast Persistence of day 5-11 ROC area of probability of 2-metre temperature in upper third of climate range Day 12-18 Day 19-32 Monthly Forecast Persistence of day 5-18

86. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 86 / 95 ECMWF seasonal forecast System 2  6-month ensemble produced each month with coupled atmosphere-ocean forecast system  ECMWF atmospheric model (same cycle as used for ERA): ~200 km (T95), 40 levels  HOPE ocean model: 29 levels; 1°x1° mid-latitudes, increased latitudinal resolution to 0.3° at equator  40 ensemble members: 5 ocean analyses, 40 SST perturbations; stochastic physics  Products issued as anomalies relative to model climatology (15 years of ensemble re-forecasts)  Initial date 1 st of each month; forecasts issued 15 th of month

87. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 87 / 95 ECMWF seasonal forecast System 2 Anomaly of 2m temperature Base 01-10-2006. Valid December-January-February (DJF)

88. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 88 / 95 ECMWF seasonal forecast System 2 Anomaly of precipitation Base 01-10-2006. Valid December-January-February (DJF)

89. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 89 / 95 ECMWF seasonal forecast System 2

90. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 90 / 95 Seasonal forecasting: EUROSIP multi-model ensemble  Three models running at ECMWF: –ECMWF – System 2 –Met Office – HADCM3 model, Met Office ocean analyses –Meteo-France – Arpege/Climat, Mercator ocean analyses –Spain + Germany may join  Unified system –All data in ECMWF operational archive –Common operational schedule (products released at 12UTC on the 15 th of each month)  Common products being developed  EUROSIP appears to be better than the individual systems

91. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 91 / 95 EUROSHIP seasonal forecast EUROSHIP anomaly of 2m temperature Base 01-10-2006. Valid December-January-February (DJF)

92. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 92 / 95 EUROSHIP seasonal forecast EUROSHIP anomaly of precipitation Base 01-10-2006. Valid December-January-February (DJF)

93. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 93 / 95 ECMWF Products for WMO members  A new web page has been created as a single entry point for all services to WMO members –www.ecmwf.int/about/wmo_nmhs_access/index.html  Council enhanced in July 2006 the product set available to all WMO members: –For several parameters, an extension of the forecast range from day 7 to day 10 –Global products from the EPS in support of high impact weather –Site-specific forecasts at selected locations, targeting synoptic stations in developing countries, especially the least developed ones

94. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 94 / 95 ECMWF Products for GTS dissemination Paramlevelsteps Z500G 0,24,48,72,96,120,144,168, 192, 216, 240 T850G0,24,48,72,96,120,144,168 u,v850, 700, 500, 200G0,24,48,72,96,120,144,168 Rh850, 700G0,24,48,72,96,120,144,168 MSLPG0,24,48,72,96,120,144,168 Div700T0,24,48,72,96,120,144 Vort700T0,24,48,72,96,120,144

95. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 95 / 95 ECMWF Products for ACMAD Paramlevelsteps u,v9250,24,48,72,96,120,144,168 div925, 2000,24,48,72,96,120,144,168 2m T 0,06,12,24,30,36,42,48,54,60,66, 72,78,84,90,96,102,108,114,120 10m u,v 0,06,12,24,30,36,42,48,54,60,66, 72,78,84,90,96,102,108,114,120 precip0,06,12,24,30,36,42,48,54,60,66, 72,78,84,90,96,102,108,114,120

96. Severe Weather Forecasting in Africa – ECMWF NWP Process – Ervin Zsoter 96 / 95 ECMWF Web site (www.ecmwf.int)