1. Models: General Characteristics Much better in Short Term –Doubling of error about every 2.5 days

2. Models: General Characteristics

3. Much better at Gross Features –Especially beyond 2/3 days

4. Models: General Characteristics

5. Some Variables better predicted than others –Precipitation vs Temperature

6. Models: General Characteristics More “model” cyclones than real cyclones

7. Models: General Characteristics

9. Model errors –Position –Timing –Intensity –ETA 12-km MID-ATLANTIC LOOPETA 12-km MID-ATLANTIC LOOP Fast zonal flow –Challenges: more timing-related Meridional flow –Challenges: more intensity-related

10. GENERAL MODEL BIASES: Precipitation Over predicts coverage –Especially with lighter amounts –Especially in coarse models –NGM MODEL LOOPNGM MODEL LOOP Over predicts duration –Especially in coarse models Under predicts local maxima (esp. conv) –Will miss the 5”+ events –THE NGM ALMOST ALWAYS SIGNIFICANTLY UNDERPREDICTS THE MAXIMUMTHE NGM ALMOST ALWAYS SIGNIFICANTLY UNDERPREDICTS THE MAXIMUM

11. Models: General Characteristics

12. GENERAL MODEL BIASES: Precipitation Under predicts gradient –Smooths out precipitation accumulation

15. GENERAL MODEL BIASES: Precipitation Warm Advection-Driven: Models too Slow SIGNATURE: Overunning with VV “bullseye” at leading edge ADVICE: Go with the fastest model!

16. GENERAL MODEL BIASES: Precipitation Too dry with Pacific s. branch “closed low” shortwaves when/after they push ashore –Not true if shortwave part of a baroclinic zone –ADVICE: If looks impressive on WV& 500mb h/v  Go with wetter solution Under predicts upslope precipitation Over predicts downslope regions

17. Higher Resolution: Improves Terrain-forced weather! Model Terrain vs. Actual Terrain ADVICE: Go wetter (drier) than model in Upslope (Downslope) areas

18. GENERAL MODEL BIASES: Precipitation Worse for convective precipitation –Most true for coarse models

19. GENERAL MODEL BIASES: Precipitation Convective Precipitation NON- Convective Precipitation

20. GENERAL MODEL BIASES: Precipitation Under predicts COLD CONVEYOR precipitation –True for well-developed cyclones

21. Model Precipitation Forecasts: Questions to Ask Is precipitation stratiform ? Is there “synoptic scale backing”? –SHORTWAVE ? –FRONTAL LIFT ? Is 700mb RH > 90% ?

22. GENERAL MODEL BIASES: Precipitation More confident in Dallas, TX or Pittsburgh, PA ? ADVICE: Dynamic supported/Non-convective features-> highest confidence

23. SPECIFIC MODEL BIASES: Precipitation NGM –Over predicts: Coverage of “air mass” convection in East (warm) –Under predicts: Local max greatly, esp. convective/terrain driven –Under predicts: Heavy rain events Gulf States (cool season) –Over predicts: Lee side of Pacific Coast mountains (cool season) –Under predicts: Windward side of Pacific Coast mountains (cool) –Under predicts max amounts: Monsoon rain (warm season) –Over predicts coverage: Monsoon rain (warm season)

24. SPECIFIC MODEL BIASES: Precipitation ETA/AVN/WRF???? –Cyclogenesis can occur too “quick” off East Coast Models can “recover” air over ocean too fast (Air/Sea Fluxes) However …. Not True if: –Shortwave is slow-moving, or –Atmosphere doesn’t need to “recover”

25. SPECIFIC MODEL BIASES: Precipitation

26. Perfect Recipe for Cyclogenesis Will impact NE Seaboard?

27. SPECIFIC MODEL BIASES: Precipitation

29. GENERAL MODEL BIASES: Temperature Poorest at the surface (aka. 2m) –Can’t handle fluxes –Especially in mountains

30. GENERAL MODEL BIASES: Temperature

31. THIS IS WHY WE HAVE MR. MOS! - STATISTICALLY CORRECTS FOR THESE MODEL DEFICIENCIES

32. GENERAL MODEL BIASES: Temperature Model Terrain vs. Actual Terrain A B C What will the model biases be at each station? ADVICE: Model 2m temp will be too low if actual elevation is lower than model ADVICE: Model 2m temp will be too high if actual elevation is higher than model Especially true during the day! (max temperature fcsting) http://www.meteo.psu.edu/~m415mgr/compelv.txt

33. GENERAL MODEL BIASES: Temperature Models too fast with Cold Advection –Especially true downwind of mountains during daytime Models too slow with “Edge Wave”-driven CFs

34. GENERAL MODEL BIASES: Temperature

35. SPECIFIC MODEL BIASES: Temperature (Low-level cold air) NGM –Under predicts Arctic chill near ground –Under predicts downslope (upslope) warming (cooling) ETA/AVN/WRF?? –Over predicts cold air near ground in Northeast if snow cover (daytime) AVN/MRF –Slight cold bias in boundary layer (2m-850mb) over NE States … grows with time