1. An Ensemble Primer NCEP Ensemble Products By Richard H. Grumm National Weather Service State College PA and Paul Knight The Pennsylvania State University

2. Introduction Model basics – What a single model can do for you! – What is a model – The 6  rule on predictability Why we need ensembles – one model many potential initial states ! Effective use of mesoscale model ensembles –Lagged Average Forecasts and dProg/dt – Samples of NCEP MREF and SREF data

3. 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

4. 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

5. What one model can do One detailed mesoscale model will allow the user to make highly specific and detailed inaccurate forecast. – Uncertainties in data – Uncertainties in data verses resolution – Uncertainties in physics – live by a model, die by that model – to pick a model of the day, see line above!

6. The 5  rule Deals with the number of grid points needed to resolve a wave As a general rule –to fully resolve a wave you must have a grid spacing of 1/5 the length/width of the feature –hence the 5  rule Thunderstorm is ~2 km so need 2/5=~.40 km CSI band about ~30km wide 30/5=~6 km

7. Ensembles help us Deal with uncertainties in data – the ability to properly resolve the feature Deal with uncertainties in data verses resolution of the model – 5  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.

8. Ensemble Strategies Lagged Average Forecasts (LAF) – dProg/dt feature in AWIPS – simple uses 1 model and different runs – last member considered most skillful Single model with perturbed members – all members should be of similar skill – all intialize at same time (better than LAF) – Our SREF and MRF data Ensemble of many models –vary conditions and physics –“ super ensemble ”

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

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

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

12. Putting it all together Why we need to consider ensembles –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 ?

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

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

15. Real example bulls eye is GREEN…MRF is BLUE

16. 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

18. SREF-enhanced by Eta/AVN weighted AVN/Eta Weights: more skillful Eta/AVN given 3X weight Produced deeper cylone Stronger Low-level Jet A bit more precipitation to the west

19. Conclusions 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)