Applications of Ensemble Prediction - a Historical Perspective

1. Applications of Ensemble Prediction - a Historical Perspective Steve Tracton Office of Naval Research Arlington, VA (Formally of NWS/NCEP)

2. KEY POINTS THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION SCREAMING MESSAGE: THERE WILL ALWAYSBE VARYING DEGREES OF UNCERTAINTIES IN FORECASTS (“Chaos Theory”)

3. KEY POINTS THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION ENSEMBLE PREDICTION – FROM EARLY 90’S ON, REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”), First formal attention to the real possibility of OPERATIONAL ensemble prediction at ECMWF Workshop on Predictability In the Medium Range and Extended Range, 1986 Ultimately led to operational GLOBAL EPS at ECMWF and NMC in Dec 1992 Followed from Sufficient CPU resources becoming available Scientific basis for generating “dynamically constrained” initial state perturbations (SVs, Breeding) Development of output products

4. Give Me Odds ENSEMBLE FORECASTING

5. KEY POINTS THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION ENSEMBLE PREDICTION – FROM EARLY 90’S ON, REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”), THE OBJECTIVES BEING TO: PROVIDE RELIABLE INFORMATION ON FORECAST UNCERTAINTIES FROM THE SPREAD (DIVERSITY) AMONGST ENSEMBLE MEMBERS USE TO: • Ascertain most likely deterministic prediction • Confidence in deterministic forecast • Same, plus identifying relative likelihood of alternative scenarios • Full probability distribution – maximum information • NET RESULT - ENHANCE UTILITY/VALUE OF NWP FOR VIRTUALLY ALL APPLICATIONS NOT NECESSARILY SKILL

6. Providing EPS = Acceptance and Use / MRF ECMWF NOGAPS UKMET

7. WHICH SOLUTION IS PREFERRED?  DETERMINISTIC THINKING RUN TO RUN MODEL CONTINUITY TODAY’S DAY 3 YESTERDAY’S DAY 4 GOOD NEW 84 HR MRF OLD 108 HR MRF BAD NEW 96 HR ECMWF OLD 120 HR ECMWF

8. EPS NOW CONSIDERED INDISPENSIBLE TO HPC MEDIUM RANGE FORECASTS (Jim Hoke) HPC’s Extended Forecast Discussion(released Mar 2, 2001, at 3:38 p.m.) STEEP LEARNING CURVE FOR NEW PARADIGM:NO A- PRIORI BEST SINGLE OUTCOME “DETERMINISM IS DEAD” ?? NOT YET; BUT…

9. American Meteorological Society (AMS) Statement: Enhancing Weather Information with Probability Forecasts (3/02 BAMS!) “The AMS endorses probability forecasts and recommends their use be substantially increased.” “Would allow user (not forecaster!) to make decisions based on quantified uncertainties with resulting economic and social benefits” ( e.g., from taking umbrella along, through canceling a trip ,to evacuation from an impending threat)

10. Schematic of how a probabilistic model forecast can be used for risk-based decision-making. P1 P2 P3 P4 . . PN F(P) Model Pcr Compare T with Tcr Take appropriate action T P P1, P2 … PN = predicted variables of interest, for example, precipitation amount Pcr = user-specified “critical value” of P which, if exceeded, requires an action or decision T = chance of critical value being exceeded Tcr = user-specified tolerance level (depending on societal, monetary, and/or environmental considerations).

11. American Meteorological Society (AMS) Statement: Enhancing Weather Information with Probability Forecasts (3/02 BAMS!) “The AMS endorses probability forecasts and recommends their use be substantially increased.” “Would allow user (not forecaster!) to make decisions based on quantified uncertainties with resulting economic and social benefits” ( e.g., from taking umbrella along, through canceling a trip, to evacuation from an impending threat) “Probability Forecasts are particularly useful, even necessary, to reliably provide early warnings of extreme weather events” TO AVOID, E.G.

12. MAJOR SNOWSTORM AMBUSHES WASHINGTON Not Good- especially when effecting DC (just after announce-ment of new Super Computer by NWSHQ

13. FOR FORECASTERS, ANOTHER …

14. American Meteorological Society (AMS) Statement: Enhancing Weather Information with Probability Forecasts (3/02 BAMS!) “The AMS endorses probability forecasts and recommends their use be substantially increased.” “Would allow user (not forecaster!) to make decisions based on quantified uncertainties with resulting economic and social benefits” ( e.g., from taking umbrella along through canceling a trip to evacuation from an impending threat) Requires: Conveying rationale and nature of uncertainties Products/graphics/presentations that are readily comprehensible and relevant

15. KEY POINTS THERE ARE INEVITABLE UNCERTAINTIES IN NWP DUE TO UNCERTAINTIES IN INITIAL CONDITIONS AND MODEL FORMULATION ENSEMBLE PREDICTION - REVOLUTIONARY CHANGE IN THE THRUST OF OPERATIONAL NWP (“WAVE OF THE FUTURE”) - CONSISTS OF MULTIPLE PREDICTIONS FROM SLIGHTLY DIFFERENT INITIAL CONDITIONS AND/OR WITH VARIOUS VERSIONS OF MODELS, THE OBJECTIVES BEING TO: REALIZING THE PRACTICAL UTILITY/VALUE OF ENSEMBLES ACCOMPLISHED VIA A VARIETY OF PRODUCTS DESIGNED TO CONDENSE AND MAXIMIZE INFORMATION CONTENT FOR USERS Products/graphics/presentations must be readily comprehensible and relevant (MUST BE USER SPECIFIC AND USER FRIENDLY)  USER FEEDBACK ESSENTIAL

16. Products: Stamp maps

17. TYPES OF PRODUCTS Roots of EPS products largely provided by Ed Epstein with graphical depictions to illustrate how uncertainty information could enhance forecast value (1971); But, no further formalconsideration of post processing and presenting EPS output until 1992 ECMWF Workshop on New Developments in Predictability (List of recommended generic products largely reflected “concept demonstration” mode of EPS experiments at NMC/CAC, Tracton and Kalnay, 1993)  SPAGHETTI CHARTS: now one of the most recognizable and symbolic products of EPS

21. TYPES OF PRODUCTS SPAGHETTI CHARTS MEAN/SPREAD PROBABILITIES STORM TRACKS CLUSTERS VERTICAL PROFILES METEOGRAMS ENSEMBLE DERIVED MOS

22. 010519/0000V63 SREFX-CMB; LIFTED INDEX PROB 0F < -4

24. 5-Day ECMWF Ensemble Prediction of Typhoon Rusa

25. TYPES OF PRODUCTS SPAGHETTI CHARTS MEAN/SPREAD PROBABILITIES STORM TRACKS CLUSTERS VERTICAL PROFILES METEOGRAMS ENSEMBLE DERIVED MOS • CAN BE APPLIED TO VIRTUALLY ALL MODEL AND MODEL DERIVED PARAMETERS AND MODEL OUTPUT DRIVERS OF SECONDARY SYSTEMS, E.G., WAVE, HYDROLOGICAL,POLUTION DISPERSION MODELS ULTIMATELY CAN (MUST) BE PROPOGATED TO USER SPECIFCIC QUANTITIES, E.G., UMBRELLA SALES, ENERGY USEAGE, TYPE OF “SMART” MUNITIONS, ETC., ETC., ETC., ETC., ETC., ETC ….

26. MANY NEW AND VARIED PRODUCTS POSSIBLE; BUT, MOST SIGNIFICANT ADVANCE OVER PAST YEARS HAS BEEN EXPONENTIAL GROWTH IN GRAPHICS CAPABILITIES - ANIMATION, ZOOMING, 3-D, ETC. - AND PROLIFERATION OF PCs AND HIGH SPEED INTERNET

27. MANY NEW AND VARIED PRODUCTS POSSIBLE; BUT, MOST SIGNIFICANT ADVANCE OVER PAST YEARS HAS BEEN EXPONENTIAL GROWTH IN GRAPHICS CAPABILITIES - ANIMATION, ZOOMING, 3-D, ETC. - AND PROLIFERATION OF PCs AND HIGH SPEED INTERNET SO, REAL CHALLENGE IS MORE FULLY EXPLOITING POTENTIAL VALUE OF EPS IN BOTH PUBLIC AND PRIVATE SECTORS

28. CONVEYING UNCERTAINTY TO PUBLIC VIA MEDIA - A CRITICAL LINK (see IABM.org) Bob Ryan

29. MON TUES WED THURS FRI 40% 36 28 23 21 31 40 30 20 THIS 10

30. INCONSISTENT WITH PREDICTABILITY LIMITS NOT THIS

31. Visualizing UncertaintyinMesoscale Meteorology APL Verification Methodology 21 May 2002 Scott Sandgathe

33. Questions ??

34. SPAGHETTI CHART

35. What a mess!!

36. END

37. BACKUP SLIDES

38. EVEN EXPERTS CAN HAVE A BAD DAY

40. High Resolution Mesoscale models • allow us to see features not in coarser models • But: even small timing and placement errors can be significant in attempt to accurately forecast details (see Mass, et al., 3/02 BAMS!!!). • ButBut: Forcaster judgement could mitigate • One model (even with forecaster input) • is an all or nothing proposition => “One detailed mesoscale model based forecast could allow the user to make highly specific and detailed inaccurate forecasts.” (after Grumm)

41. Why we need ensembles • Deal with uncertainties in analyses and model formulation • But: Requires tradeoffs when computer resources limited (e.g., model resolution) • ButBut: Mesoscale predictability often substantially controlled by synoptic predictability (and uncertainties therein) • => Subjective or statistically based downscaling possible to get uncertainties in mesoscale weather • Ideal: Ensembles with highest resolution justifiable • Compromise: Combination of single (or few) high resolution and coarser resolved ensemble

42. ADDITIONAL WEB SITES WITH ENSEMBLE PRODUCTS AND INFORMATION Univ of Utah: http://www.met.utah.edu/jhorel/html/models/ model_ens.html FNMOC: http://152.80.49.204/PUBLIC/ Canada: http://www.cmc.ec.gc.ca/rpn/ensemble_products/ index.html CDC: http://www.cdc.noaa.gov/~map/maproom/ENS/ ens.html State College: http://bookend.met.psu.edu/ensembles/Ensemble.html

43. Give Me Odds ENSEMBLE FORECASTING

46. EVEN EXPERTS CAN HAVE A BAD DAY

47. YOUR FORECAST HAS A 30% CHANCE OF BEING 70% CORRECT

48. 3-day forecast from 00 UTC 11/2/01, spaghetti diagram for ensemble global Uncertain location of incoming western trough Uncertain amplitude of eastern trough From CDC web site: http://www.cdc.noaa.gov/map/images/ens/ens.html

49. “SPAGHETTI” DIAGRAM 564 DM CONTOUR NCEP ENSEMBLE SOME NCEP ENSEMBLE MEMBERS OFFER A MORE “DIGGY” CENTRAL U.S. TROF THAN THE OPERATIONAL MRF..BUT NOT AS STRONG AS THE ECMWF DAY3