1. NCEP Models and Ensembles By Richard H. Grumm National Weather Service State College PA 16803 and Robert Hart The Pennsylvania State University

2. Introduction NCEPS Global Forecast System NCEP Medium Range Ensemble Forecast System (MREF) Why we need ensembles – one model many potential initial states ! Ensemble Strategies –Lagged Averaged Forecasting and d(Prog)/dt –True ensembles

3. Global Forecast System GFS suite has a medium range forecast model (MRF) and a global data assimilation system (GDAS). We will generally use GFS to refer to both. History –The first global spectral model was developed experimentally during the late 1970s (Sela 1980) and implemented as the global forecast model at the National Meteorological Center (NMC) on 18 March 1981 – Original Model: Triangular truncation at 30 waves with 12 levels (T30L12) T24L12 from 48 to 84 hours T24L6 from 84 to 144 hours (TPBs 282A and 282B )

4. Global Forecast System Early 1980s computer power allowed for the use of spectral models. This model replaced the older grid- point base 7-level primitive equation model (PE) which had a 191 km grid. The PE model was implemented in the late1960s. 1985 after big changes model was renamed the Medium Range Forecast (MRF) Model. new physics packages, an increase in the number of waves resolved to rhomboidal truncation at 40 waves (R40), and an increase in the number of equally spaced layers from 12 to 18. Currently, the GFS is run four times per day (00 UTC, 06 UTC, 12 UTC, and 18 UTC) out to 384 hours. The initial forecast resolution was changed on. Many changes have taken place since 1985.

5. GFS-2003 29 October 2002 GFS upgraded to T254 –equivalent to about 55-km grid resolution –with 64 levels out to 3.5 days (84 hours). –Later forecast times, GFS has a resolution of T170 (equivalent to about 80-km resolution) with 42 levels from 3.5 to 7.5 days (180 hours), T126 (110-km resolution) with 28 levels beyond to day 16 (384 hours). The reduction in GFS model resolution at 84 hours is consistent with studies showing that the GFS benefits from higher resolution only out to about day 3-4. All GFS runs get their initial conditions from the Spectral Statistical Interpolation (SSI) global data assimilation system (GDAS), which is updated continuously throughout the day.

6. GFS Summary GFS Model Type Spectral Vertical Coordinate Sigma 64 layers Resolution T254 (1.6o or 55 km) Domain Global CPS Arakawa-Schubert Runs 4 times daily (00,06,12, 18 UTC) Resolution with time Varies with less resolution at longer times

7. MREF  NCEPS Global Forecast System NCEP Medium Range Ensemble Forecast System (MREF) Why we need ensembles – one model many potential initial states ! Ensemble Strategies –Lagged Averaged Forecasting and d(Prog)/dt –True ensembles

8. Based on the GFS system and uses the core model of the GFS to make all predictions. Uses positive and negative perturbations as input to the model for each run. 0000 UTC – GFS run Full T170 GFS resolution to day 7 T62 thereafter to day 16 – 1 MREF control Member T126 medium resolution control out to 7 days truncated top T62 at day 7 and out to day 16 – 10 Perturbed Runs 1200 UTC – GFS (AVN) T170 to 126 hours T60 to day 16 – 10 Perturbed runs NCEP MREF SYSTEM Medium Range Ensemble Forecast system

9. 5 Negatively & 5 positively perturbed Members –0000 and 1200 UTC with 10 total members – each run at T126 to 84 hrs, then truncated to T62 horizontal resolution. –Lower resolution provides speed NCEP MREF SYSTEM Perturbations

10. A word about Perturbations Breeding method –goal to find most rapidly growing modes. So random will generally not work. –Initial seed is random, get differences between control and perturbed over several 12-hour forecasts cycles,by definition, the difference reflects only the growing modes. Each seed gives both 1 positive and 1 negative perturbation. Different initial seeds provide a set of 2 new growing modes. –In SREF, 09Z 12-hour forecast referenced to control makes the 21Z forecast initial perturbed conditions. The 21Z 12-hour forecasts makes the next 09Z initial conditions etc.

11. Breeding Start with initial random seed. Very first forecast is truly random At 12-hours get difference and correct forecast from control. This is the next cycles perturbation –finish forecast to 63 hours Next cycle use perturbation and get next runs value 12-hours into the forecast Self breeding methodology

12. SREF Breeding N SEEDS GIVE 2*N PERTURBATIONS Scaled + perturbation Initial random seed Opposite sign is negative perturbation Adjust magnitude to typical analysis errors 12-h forecast CONTROL-CTL Complete cycle forecast

13. The 6  rule Deals with the number of grid points needed to resolve a wave As general rule –to fully resolve a wave you must have a grid spacing of 1/6 the length/width of the feature –hence the 6  rule CSI band about ~30km wide 30/6=~5 km The GFS and MREF have too coarse resolution to resolve many important meteorological phenomena.

14. Why Ensembles  NCEPS Global Forecast System NCEP Medium Range Ensemble Forecast System (MREF) Why we need ensembles – one model many potential initial states ! Ensemble Strategies –Lagged Averaged Forecasting and d(Prog)/dt –True ensembles

15. * Ensembles help us Deal with uncertainties in data – the ability to properly resolve the feature Deal with uncertainties in data verse resolution of the model – 6  rule, we may under sample a system. Deal with uncertainties in physics Displaying uncertainties in forecasts – spaghetti plots – probability charts (the most likely outcome) – consensus forecast charts – to visualize these is to see limits of any single solution.

16. Ensemble Strategies  NCEPS Global Forecast System NCEP Medium Range Ensemble Forecast System (MREF) Why we need ensembles – one model many potential initial states ! Ensemble Strategies –Lagged Averaged Forecasting and d(Prog)/dt –True ensembles

17. Ensemble Strategies Lagged Average Forecasts (LAF) – dProg/dt is often used instead of true LAF – simple uses 1 model and different runs of that model – See trends in 0000,0600, 1200 and 1800 UTC model runs – See areas of disagreement/dispersion – last member considered most skillful Single model with perturbed members – all members should be of similar skill – all initialized at same time (better than LAF) – Our SREF and MREF data Ensemble of many models (The best!) –vary conditions and physics –“ super ensemble ”

18. Ensemble from LAF may convey uncertainty in initial conditions Forecast Length Envelope of solutions at single time Solution Forecasts Initialized at different Times

19. AVN dProg/dt at 18Z 30 Dec 2000 Derived LAF

20. Ensembles with different initial conditions Forecast Length Forecasts Initialized at most recent data time Envelope of solutions at single time Solution

21. Putting it all together Why we need to consider ensembles – Avoid the binary yes/no single model solution – The more probable ensemble solution Do you a hit the bulls-eye yes/no? Do you want to approximate the bulls- eyes most likely position ?

22. Hit the bull's-eye one arrow could be a near miss (the MRF)

23. Approximate the Bulls-eye Error analysis approximates the bull's-eye verse our one “red arrow ” MREF Forecasts

24. Real example 30 Dec 2000 bulls eye is GREEN…MRF is BLUE

25. A brief word about displays A good ensemble system requires 30-50 members – NCEPs system will grow in members! – Displaying individual members is rapidly becoming problematic – We need to move away from thumb nails (next slide) individual model diagnostics We need to move toward – Spaghetti and dispersion to see variations between members – consensus and clustered means – probabilistic displays

26. Thumb Nail Surface Low 00 UTC 7 Jan 2002

27. 12 QPF Thumbnails Valid 06 UTC 6-12Z 7 Jan imagine 45 of these!

28. MREF Forecasts Arctic Outbreak-From this…

29. To…Arctic Air for 23 Jan 2003 not too shabby for 10 days!

30. Cold air NW Flow Lake Effect 10 days out/near miss cyclone off coast?

31. Lake Effect 10 days out 23 Jan 2002 1200 UTC

32. Conclusions NCEPS Global Forecast System – MRF model – Global data assimilation system – About 55 km resolution NCEP Medium Range Ensemble Forecast System (MREF) – Run 2X daily with 11-12 members – GFS is the model behind the MREF – MREF uses perturbed initial states to get the 11-12 different forecasts.

33. Ensemble Summary Ensembles are – collections of forecasts from different forecasters different models and models with different conditions – ensemble mean (consensus) is a high probability outcome Use ensembles to deal with uncertainty – in initial conditions – in model physic and parameterizations Operationally – view spaghetti plots: the range of solutions – ensemble mean and probability forecasts – Avoid the binary YES/NO (right/wrong forecast)

34. Ensembles Capabilities Great promise on some big snowstorms – Did well 3-4 Dec and 30 Dec 2000 – Did will 5 December and 25 December 2002 – Did well 3-4 January 2003 – Did poorly 6-7 Jan 2002 MREFsystem is not perfect (but getting better!) – more members being added – improved quality of models used in system – Eventually 4 runs per day as 06 and 18 UTC will be added NCEP MREF/SREF Data is a valuable forecast tool.

35. Consider this Consider this: Would you want to shoot one arrow at the bullseye or a quiver full of arrows at the bullseye. Or…pick the Eta and maybe miss the mark or use the ensembles and have a better chance of hitting something.