Highest Confidence Forecasts

1. Highest Confidence Forecasts • Model agreement • CMC=NAM=GFS • Run-to-run changes (dMod/dt) very small • Models trending toward agreement • Example: • OLD run: NAM=GFS but *not* CMC • NEW run: CMC trends toward WRF & GFS • Models have current weather “in hand” • Parameterized processes not significant part of feature

2. Lowest Confidence Forecasts • Large model disagreement • ECMWF, WRF, GFS all have different solutions • Run-to-run changes (dMod/dt) large • Don’t have current weather “in hand” • Parameterized processes significant part of feature

3. When models disagree ….. • In a 12-36 hr. fcst, lean toward model/s that has “best” handle on current weather! • Lean toward a model whose run-to-run change is small, especially if other models are trending toward it • Lean away from a model if it is showing its bias! • Take consensus!

4. When models disagree ….. NAM Rainfall forecast: Cape Canaveral, FL Postpone a launch? AVN CMC

5. When models disagree ….. 15Z RADAR NAM AVN Which model do you go with? CMC KJAX 141756Z 33008KT 1 1/2SM -RA BR OVC010 15/15 A3013 60086

6. MODEL TREND: Single Model Is the Trend a useful forecast technique?

7. MODEL TREND: Single Model Is the Trend a useful forecast technique?

8. MODEL TREND: Single Model Is trend any help at all in this case?

9. MODEL TREND: Single Model • LAGGED AVERAGE FORECAST • Average of each forecast valid at same time • “Poor man’s” Ensemble

10. MODEL TREND Trending toward New York City?

11. MODEL TREND Trend = Bust-o-matic

12. MODEL TREND

13. Interpreting Model Trends: What’s Legitimate ?? • Least significant if associated with “parameterized” situation • 3-model run trend stronger signal than 2-model trend • Hierarchy of model run-to-run trends • 24 ->12 hours most significant • 60-> 48 hours least significant

14. MODEL CONFIDENCE: Utilizing Trend & Agreement MOST CONFIDENT!

15. MODEL CONFIDENCE: Utilizing Trend & Agreement TRENDING TOWARD AGREEMENT

16. MODEL CONFIDENCE: Utilizing Trend & Agreement TRENDING TOWARD AGREEMENT

17. MODEL CONFIDENCE: Utilizing Trend & Agreement TRENDING TOWARD AGREEMENT

18. MODEL CONFIDENCE: Utilizing Trend & Agreement TRENDING TOWARD AGREEMENT

19. MODEL CONFIDENCE: Utilizing Trend & Agreement TRENDING TOWARD AGREEMENT

20. MODEL CONFIDENCE: Utilizing Trend & Agreement What’s a forecaster to do? Suggestions???

21. MODEL CONFIDENCE: Utilizing Trend & Agreement LEAST CONFIDENT!

22. ENSEMBLE FORECASTS • What are ENSEMBLE FORECASTS? • Model’s initial conditions are perturbed • Variety of solutions occur • Ensembles on e-wall

23. ENSEMBLE FORECASTS THESE ARE THE MEMBERS OF THE ENSEMBLE - Negative and Positive tweaks ONE MODEL … MANY TWEAKS

24. ENSEMBLE FORECASTS EACH MEMBER IS RUN OUT IN TIME - Provides “unique” solution

25. ENSEMBLE FORECASTS ENSEMBLE MEAN IS “most likely” SOLUTION averaged over ALL cases

26. ENSEMBLE FORECASTS HOW CONFIDENT ARE WE IN THE ENSEMBLE MEAN?

27. ENSEMBLE FORECASTS IS THE ENSEMBLE MEAN more likely than the CLUSTERS?

28. ENSEMBLE FORECASTS MEM 1 ENSEMBLE MEAN MEM 2 Which solution is LEAST likely?

29. ENSEMBLE FORECASTS: Another Approach THESE ARE DIFFERENT MODELS - WRF, GFS, NGM, MM5, EUR, MRF, UKM, CMC MANY MODELS … MANY DIFFERENT “PHYSICS” & IC

30. ENSEMBLE FORECASTS MULTI-MODEL CONSENSUS What’s the better approach?

31. ENSEMBLE FORECASTS Many “perturbations”, Many People Many “perturbations”, One YOU What’s the better approach?

32. ENSEMBLE FORECASTS • Variance measures forecast reliability • Measures “robustness” of a model solution • How much confidence in model forecast • Ensemble mean is “most accurate” averaged over all cases • Member clustering can be useful • TARGETING OBSERVATIONS

33. OPTMIZING MODEL OUTPUT • MODEL AGREEMENT • Agreement of different models on same solution = POWERFUL • Confidence high if models converge on solution • Confidence low if models diverge • MODEL TREND • Run-to-run changes of a model • Confidence higher if run-to-run changes are small • Confidence lower if run-to-run changes are large