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Much better at Gross Features –Especially beyond 2/3 days
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Models: General Characteristics
• Much better in Short Term– Doubling of error about every 2.5 days
Models: General Characteristics
Models: General Characteristics
• Much better at Gross Features– Especially beyond 2/3 days
Models: General Characteristics
Models: General Characteristics
• Some Variables better predicted than others– Precipitation vs Temperature
Models: General Characteristics
• More “model” cyclones than real cyclones
Models: General Characteristics
Models: General Characteristics
Models: General Characteristics
• Model errors– Position– Timing– Intensity– ETA 12-km MID-ATLANTIC LOOP
• Fast zonal flow– Challenges: more timing-related
• Meridional flow– Challenges: more intensity-related
GENERAL MODEL BIASES: Precipitation
• Over predicts coverage– Especially with lighter amounts– Especially in coarse models– NGM 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 MAXIMUM
Models: General Characteristics
GENERAL MODEL BIASES: Precipitation
• Under predicts gradient– Smooths out precipitation accumulation
GENERAL MODEL BIASES: Precipitation
Warm Advection-Driven: Models too Slow
SIGNATURE: Overunning with VV “bullseye” at leading edge
ADVICE: Go with the fastest model!
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
Higher Resolution: Improves Terrain-forced weather!Model Terrain vs. Actual Terrain
ADVICE: Go wetter (drier) than model in Upslope (Downslope) areas
GENERAL MODEL BIASES: Precipitation
• Worse for convective precipitation– Most true for coarse models
GENERAL MODEL BIASES: Precipitation
Convective Precipitation
NON- Convective Precipitation
GENERAL MODEL BIASES: Precipitation
• Under predicts COLD CONVEYOR precipitation– True for well-developed cyclones
Model Precipitation Forecasts: Questions to Ask
• Is precipitation stratiform ?• Is there “synoptic scale backing”?
– SHORTWAVE ?– FRONTAL LIFT ?
• Is 700mb RH > 90% ?
GENERAL MODEL BIASES: Precipitation
More confident in Dallas, TX or Pittsburgh, PA ?
ADVICE: Dynamic supported/Non-convective features-> highest confidence
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)
GENERAL MODEL BIASES: Temperature
• Poorest at the surface (aka. 2m)– Can’t handle fluxes– Especially in mountains
GENERAL MODEL BIASES: Temperature
GENERAL MODEL BIASES: Temperature
THIS IS WHY WE HAVE MR. MOS!- STATISTICALLY CORRECTS FOR THESE MODEL DEFICIENCIES
GENERAL MODEL BIASES: TemperatureModel Terrain vs. Actual Terrain
A
BC
What will the model biases be at each station?
ADVICE: Model 2m temp will be too low if actual elevation is lower than modelADVICE: 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
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
GENERAL MODEL BIASES: Temperature
