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FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF long range forecast systems Dr. Tim Stockdale European Centre for Medium-Range Weather Forecasts.

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Presentation on theme: "FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF long range forecast systems Dr. Tim Stockdale European Centre for Medium-Range Weather Forecasts."— Presentation transcript:

1 FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF long range forecast systems Dr. Tim Stockdale European Centre for Medium-Range Weather Forecasts

2 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Outline Overview of System 4 Some recent research results EUROSIP multi-model forecasts Forecasts for JJA 2013

3 FOCRAII-9, Beijing 10 th April 2013 © ECMWF System 4 seasonal forecast model IFS (atmosphere)  T L 255L91 Cy36r4, 0.7 deg grid for physics (operational in Dec 2010)  Full stratosphere, enhanced stratospheric physics  Singular vectors from EPS system to perturb atmosphere initial conditions  Ocean currents coupled to atmosphere boundary layer calculations NEMO (ocean)  Global ocean model, 1x1 resolution, 0.3 meridional near equator  NEMOVAR (3D-Var) analyses, newly developed. Coupling  Fully coupled, no flux adjustments  Sea-ice based on sampling previous five years

4 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Reduced mean state errors S4 S3 T850U50

5 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Tropospheric scores Spatially averaged grid-point temporal ACC One month leadFour month lead

6 FOCRAII-9, Beijing 10 th April 2013 © ECMWF S4 extended hindcast set Scores are smoother and systematically higher with 51 member hindcasts

7 FOCRAII-9, Beijing 10 th April 2013 © ECMWF S4 extended hindcast set Gain over S3 is now stronger and more robust

8 FOCRAII-9, Beijing 10 th April 2013 © ECMWF More recent ENSO forecasts are better.... 1981-19951996-2010

9 FOCRAII-9, Beijing 10 th April 2013 © ECMWF QBO 50hPa 30hPa System 3 System 4

10 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Problematic ozone analyses

11 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Land surface Snow depth limits, 1 st April

12 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Sea ice

13 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Tropical storm forecasts

14 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Recent Research

15 FOCRAII-9, Beijing 10 th April 2013 © ECMWF QBO Period and downward penetration match observations Semi-annual oscillation still poorly represented A big reduction in vertical diffusion, and a further tuning of non-orographic GWD, has given a big additional improvement in the QBO compared to S4.

16 FOCRAII-9, Beijing 10 th April 2013 © ECMWF QBO forecasts S3 S4 New

17 FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts 0.319 0.371

18 FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts Even with 101 members, ensemble mean signal not always well defined

19 FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts New version has weaker signal, more noise

20 FOCRAII-9, Beijing 10 th April 2013 © ECMWF NH winter forecasts Forecast skill is above perfect model predictability limit

21 FOCRAII-9, Beijing 10 th April 2013 © ECMWF EUROSIP A European multi-model seasonal forecast system  Operational since 2005  Data archive and real-time forecast products  Initial partners: ECMWF, Met Office, Météo-France  NCEP an Associate Partner; forecasts included since 2012  Products released at 12Z on the 15 th of each month  Aim is a high quality operational system  Data policy issues are always a factor in Europe

22 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Recent changes: variance scaling Robust implementation  Limit to maximum scaling (1.4)  Weakened upscaling for very large anomalies Improves every individual model Improves consistency between models Improves accuracy of multi-model ensemble mean

23 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Revised Nino plumes

24 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Error vs spread (uncalibrated)

25 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Calibrated p.d.f. ENSO forecasts have good past performance data  We can calibrate forecast spread based on past performance  We can also allow varying weights for models  We have to be very careful not to overfit data at any point. Represent forecast with a p.d.f.  This is the natural output of our calibration procedure  Easier visual interpretation by user Calibration and combination in general case  Ideally apply similar techniques to all forecast values (T2m maps etc)  More difficult because less information on past (higher noise levels)  Hope to get there eventually …...

26 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Nino 3.4 plume and p.d.f.

27 FOCRAII-9, Beijing 10 th April 2013 © ECMWF P.d.f. interpretation P.d.f. based on past errors  The risk of a real-time forecast having a new category of error is not accounted for. E.g. Tambora volcanic eruption.  We plot 2% and 98%ile. Would not go beyond this in tails.  Risk of change in bias in real-time forecast relative to re-forecast. Bayesian p.d.f.  Explicitly models uncertainty coming from errors in forecasting system  Two different systems will calculate different pdf’s – both are correct Validation  Rank histograms show pdf’s are remarkably accurate (cross-validated)  Verifying different periods shows relative bias of different periods can distort pdf – sampling issue in our validation data.

28 FOCRAII-9, Beijing 10 th April 2013 © ECMWF Forecasts for JJA 2013

29 FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF forecast: ENSO Past performance

30 FOCRAII-9, Beijing 10 th April 2013 © ECMWF EUROSIP forecast: ENSO Past performance

31 FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF forecast: JJA 2mT Tercile probabilities ACC skill (1981-2010)

32 FOCRAII-9, Beijing 10 th April 2013 © ECMWF ECMWF forecast: JJA precip Tercile probabilities ACC skill (1981-2010)


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