Ensembles and Probabilistic Forecasting

1. Ensembles and Probabilistic Forecasting

2. Probabilistic Prediction • Because of forecast uncertainties, predictions must be provided in a probabilistic framework, not the deterministic single answer approach that has dominated weather prediction during the last century. • Interestingly…the first public forecasts were probabilistic

3. “Ol Probs” Cleveland Abbe (“Ol’ Probabilities”), who led the establishment of a weather forecasting division within the U.S. Army Signal Corps. Produced the first known communication of a weather probability to users and the public in 1869. Professor Cleveland Abbe, who issued the first public “Weather Synopsis and Probabilities” on February 19, 1871

4. The Trend to Deterministic Forecasts During the Later 19th and First Half of the 20th Centuries.

5. Foundation for probabilistic prediction • The work of Lorenz (1963, 1965, 1968) demonstrated that the atmosphere is a chaotic system, in which small differences in the initialization…well within observational error… can have large impacts on the forecasts, particularly for longer forecasts. • Not unlike a pinball game….

6. Similarly, uncertainty in our model physics also produces uncertainty in the forecasts. • Lorenz is a series of experiments demonstrated how small errors in initial conditions can grow so that all deterministic forecast skill is lost at about two weeks. • Talked about the butterfly effect…

7. The Lorenz Diagram…chaos Is not necessarily random

8. Probabilistic NWP • To deal with forecast uncertainty, Epstein (1969) suggested stochastic-dynamic forecasting, in which forecast errors are explicitly considered during model integration, but this method was not computationally practical. • Another approach, ensemble prediction, was proposed by Leith (1974), who suggested that prediction centers run a collection (ensemble) of forecasts, each starting from a different initial state. The variations in the resulting forecasts could be used to estimate the uncertainty of the prediction. But even the ensemble approach was not tractable at this time due to limited computer resources.

9. Ensemble Prediction • Can use ensembles to provide a new generation of products that give the probabilities that some weather feature will occur. • Can also predict forecast skill! • It appears that when forecasts are similar, forecast skill is higher. • When forecasts differ greatly, forecast skill is less. • To create a collection of ensembles one can used slightly different initializations or different physics.

10. Ensemble Prediction • By the early 1990s, faster computers allowed the initiation of global ensemble prediction at NCEP and ECMWF (European Centre for Medium Range Weather Forecasts). • During the past decade the size and sophistication of the NCEP and ECMWF ensemble systems have grown considerably, with the medium-range, global ensemble system becoming an integral tool for many forecasters. Also during this period, NCEP has constructed a higher resolution, short-range ensemble system (SREF) that uses breeding to create initial condition variations.

11. NCEP Global Ensemble System • Begun in 1993 with the MRF (now GFS) • First tried “lagged” ensembles as basis…using runs of various initializations verifying at the same time. • For the last ten years have used the “breeding” method to find perturbations to the initial conditions of each ensemble members. • Breeding adds random perturbations (+ and -) to an initial state, let them grow, then reduce amplitude down to a small level, lets them grow again, etc. • Give an idea of what type of perturbations are growing rapidly in the period BEFORE the forecast. • Does not include physics uncertainty. • Coarse spatial resolution..only for synoptic features.

12. NCEP Global Ensemble At 00Z: • T254L64 high resolution control out to 7 days, after which this run gets “truncated--just larger scales” and is run out to 16 days at a T170L42 resolution • T62 control that is started with a truncated T170 analysis • 10 perturbed forecasts each run at T62 horizontal resolution. The perturbations are from five independent breeding cycles. At 12Z: • T254L64 control out to 3 days that gets truncated and run at T170L42 resolution out to 16 days • Two pairs of perturbed forecasts based on two independent breeding cycles (four perturbed integrations out to 16 days).

16. U.S. Navy Also Has A Global Ensemble System Using NOGAPS

17. NCEP Short-Range Ensembles (SREF) • Resolution of 32 km • Out to 87 h twice a day (09 and 21 UTC initialization) • Uses both initial condition uncertainty (breeding) and physics uncertainty. • Uses the Eta and Regional Spectral Models and recently the WRF model (21 total members)

18. SREF Current System Model Res (km) Levels Members Cloud Physics Convection RSM-SAS 45 28 Ctl,n,p GFS physics Simple Arak-Schubert RSM-RAS 45 28 n,p GFS physics Relaxed Arak-Schubert Eta-BMJ 32 60 Ctl,n,p Op Ferrier Betts-Miller-Janjic Eta-SAT 32 60 n,p Op Ferrier BMJ-moist prof Eta-KF 32 60 Ctl,n,p Op Ferrier Kain-Fritsch Eta-KFD 32 60 n,p Op Ferrier Kain-Fritsch with enhanced detrainment PLUS * NMM-WRF control and 1 pert. Pair * ARW-WRF control and 1 pert. pair

19. UW Short Range Ensemble System

20. a) b) Configurations of the MM5 short-range ensemble grid domains. (a) Outer 151127 domain with 36-km horizontal grid spacing. (b) Inner 103100 domain with 12-km horizontal grid spacing. UW Mesoscale Ensemble System • Single limited-area mesoscale modeling system (MM5) • 2-day (48-hr) forecasts at 0000 UTC and 12 UTC in real-time since January 2000. 36 and 12-km domains. 12-km 36-km

21. UW Ensemble System • UW system is based on the use of analyses and forecasts of major operational modeling centers. • The idea is that differences in initial conditions of various operational centers is a measure of IC uncertainty. • These IC differences reflect different data inventories, assimilation schemes, and model physics/numerics and can be quite large, often much greater than observation errors. • In this approach each ensemble member uses different boundary conditions--thus finessing the problem of the BC restraining ensemble spread. • Also include physics diversity

22. “Native” Models/Analyses Available Resolution (~@ 45 N ) Objective Abbreviation/Model/Source Type ComputationalDistributed Analysis avn, Global Forecast System (GFS), Spectral T254 / L64 1.0 / L14 SSI National Centers for Environmental Prediction ~55km ~80km 3D Var cmcg, Global Environmental Multi-scale (GEM), Finite 0.90.9/L28 1.25 / L11 3D Var Canadian Meteorological Centre Diff ~70km ~100km eta, limited-area mesoscale model, Finite 32km / L45 90km / L37 SSI National Centers for Environmental Prediction Diff. 3D Var gasp, Global AnalysiS and Prediction model, Spectral T239 / L29 1.0 / L11 3D Var Australian Bureau of Meteorology ~60km ~80km jma, Global Spectral Model (GSM), Spectral T106 / L21 1.25 / L13OI Japan Meteorological Agency ~135km ~100km ngps, Navy Operational Global Atmos. Pred. System, Spectral T239 / L30 1.0 / L14 OI Fleet Numerical Meteorological & Oceanographic Cntr. ~60km ~80km tcwb, Global Forecast System, Spectral T79 / L18 1.0 / L11 OI Taiwan Central Weather Bureau ~180km ~80km ukmo, Unified Model, Finite 5/65/9/L30 same / L12 3D Var United Kingdom Meteorological Office Diff. ~60km

26. Relating Forecast Skill and Model Spread Mean Absolute Error of Wind Direction is Far Less When Spread is EXTREME (Low or High)

27. Ensemble-Based Probabilistic Products

29. Verification The Thanksgiving Forecast 2001 42h forecast (valid Thu 10AM) SLP and winds • Reveals high uncertainty in storm track and intensity • Indicates low probability of Puget Sound wind event 1: cent 5: ngps 11: ngps* 8: eta* 2: eta 3: ukmo 6: cmcg 9: ukmo* 12: cmcg* 4: tcwb 7: avn 13: avn* 10: tcwb*

30. Ensemble-Based Probabilistic Products

31. Ensemble Prediction in the U.S.