1. 1 Systematic and Random Errors in Operational Forecasts by the UK Met Office Global Model Tim Hewson Met Office Exeter, England Currently at SUNY, Albany (until Feb 2005)

2. Utility of different model forecasts A multi-model (poor man’s) ensemble can provide the best forecast guidance Operationally, can use be made of different models ? Requires appropriate tools, and a detailed knowledge of typical model performance: –Relative Errors, Seasonal and Regional differences [A] –Individual Model Characteristics (systematic and random errors) [B] A and B will be discussed here, focusing on the UK Met Office global model (~60km resolution, 38 levels)

3. A Global Model Intercomparison: Net, Seasonal and Regional differences

4. Northern Hemisphere RMS Mslp errors vs Lead Time 1 5 days 10 RANK Best - EC  UK  FR  US  JAP  GER  CAN… -Worst

5. Seasonal differences (NH mslp, RMS at T+72) EC  UK  FR  US  JAP  GER  CAN EC Best throughout; then UKMET, but NCEP consistently better in summer

6. Regional Performance – Europe, vs Lead Time Europe-based models perform better in forecasting for Europe EC  UK  FR  US  JAP  GER  CAN

7. Regional Performance – N America, vs Lead Time Relative to performance over Europe: UKMET does worse over US/Canada, GFS better

8. B UK Global Model Characteristics - Systematic and Random Errors Precipitation (net / orographic) Low level Winds (Land / Ocean / Severe cyclonic storms) Handling of Cyclones (Cyclone spectra / Regional / Random errors)

9. Global Precipitation

10. Precipitation ~ 30% overestimate globally Enhanced Resolution 60km30L 3DVar & ATOVS New Dynamics HadAM4 physics c/o Sean Milton Met Office, Exeter

11. Precipitation errors mainly oceanic – tropics and extra- tropical storm tracks Largely ‘balanced’ by too much evaporation – boundary layer locally too dry Soil moisture is one global weakness being addressed – led to under-prediction of daytime temperatures during 2003 European heatwave (UK bias -4C)

12. Orographic Precipitation

13. OD ND MTNS A B C D E F G New Model Old Model Orographic precipitation Smoothed orography (in new model = “New Dynamics”) reduces upslope rainfall, and similarly reduces the rain shadow Older model better (even if for the ‘wrong’ reason!) Magnitude of impact is proportional to flow strength Important for QPF OD ND MTNS A B C D E F G B A C D E F B A C D E F G G

14. NE Region Model orography peaks much lower than reality Many key features missing – eg Hudson Valley Expect similar ppn problems to those found in Europe – eg insufficient upslope rain in flow from SE quadrant (factor of 2?) ‘European’ higher resolution (20km) model may help

15. Convective Precipitation

16. Diurnal cycle in convection A significant problem area (especially tropics, but also mid latitudes) Decay can be too rapid towards dusk

17. Surface Winds over land

18. Example – Oct 2004

20. 15kt winds in GFS model (mslp v similar)

21. UK Global Model Effective Roughness Lengths Account for roughness due to missing orography + … Slows down low level winds considerably 10m winds especially poor in Albany: ~50% of reality GFS model seems much better Changes to be implemented in ~1 year ~50% reduction In 10m winds

22. Surface Winds over Oceans

23. GFS model Peak winds 55kts on S flank of deep, mature low

25. UKMET model Peak 10m winds only 45kts Gradients and low depth the same as GFS Complex interface with ocean GFS seemed to validate better in this case (and may well be better generally)

26. Surface winds in Extreme Storms

27. L High resolution required (90 levels?) to model sting jet Mslp may be OK but winds not 38 Levels (operational) 90 Levels Greater strength along downward trajectory Severe windstorms c/o Pete Clark JCMM, Reading

28. Cyclone Spectra

29. Cyclone Database - Snapshot

30. GM cyclone spectra for year 2000, categorised by ‘max wind speed within 300km radius of centre’ North Atlantic Domain

31. Geographical biases in cyclone forecasts, based on trends in total numbers T+0 to T+144 Under-prediction Over-prediction

32. Random Errors in Cyclogenesis

33. November 2003 Example

34. 15Z 18Z

35. Intense cyclonic storm missed at short range – random error Perhaps 3 similar poorly forecast events per year around UK Expect similar problems elsewhere. High Impact.

36. Summary Met Office global model’s broadscale evolution is on average second only to ECMWF (NH) –Performance over Europe better than over N America –Performance in the 3 summer months lags behind GFS Despite this a number of significant problem areas exist –Precipitation over-forecast globally by 30% –Some significant errors around orography and in convection –Low Level winds under-forecast over land with unresolved orography –Some under-prediction of stronger winds over oceans? –Wind maxima under-forecast in extreme storms (resolution limitation) –No systematic drift with lead time in the number of intense storms –Fewer modest cyclones predicted at longer lead times (main bias regions include Great lakes, Gulf stream wall) –Significant random errors still occur occasionally, even at short leads Many of the above noted through active forecaster-NWP liaison Most are now being addressed within NWP division at Met Office HQ