1. Predictability of Snow Multi-Bands Using a 40-Member WRF Ensemble
Ryan Connelly and B. A. Colle
School of Marine and Atmospheric Sciences
Stony Brook University - Stony Brook, NY
Northeast Regional
Operational Workshop 18
2 Nov 2017

2. Motivation Experimental Design Results Cases WRF Ensemble
Objective Classification (MODE)
Results
Overall WRF Ability
SKEBS/SPPT vs Classic
Physical Differences in Under- and Over-Banded Cases

3. Introduction
Several different precipitation structures within the cyclone comma head of Northeast U.S. winter storms
Single band
Multi-banded
Non-banded
High-res mesoscale models can simulate primary single bands
Similar snowfall rates, visibility in multi-bands

4. Research Questions
With what reliability can mesoscale models reproduce multi-bands?
Does the stochastic ensemble generation technique improve on this reliability from more classical methods?
What physical features must the model resolve to produce multi-bands?

5. Motivation Experimental Design Results Cases WRF Ensemble
Objective Classification (MODE)
Results
Overall WRF Ability
SKEBS/SPPT vs Classic
Physical Differences in Under- and Over-Banded Cases

6. Experimental Design Pick multi-band cases, where 26-27 Nov 2014
Bands well-defined and long-lasting
Peak multi-bandedness between 18 UTC and 06 UTC following day
GEFSR only init 00 UTC, best skill should be FHR 18-30
26-27 Nov 2014
7-8 Jan 2017
Working on a third case…

7. Experimental Design Define Multi-bands: Also define…
MODE-identified objects in radar or WRF-simulated reflectivity
5 km < width < 20 km
Have > 2:1 aspect ratio (Novak et al. 2004)
Also define…
Single bands: 20 km < width < 100 km AND length > 200 km
Cells: width < 5 km, or > 5 km AND < 2:1 aspect ratio
Miscellaneous

8. Motivation Experimental Design Results Cases WRF Ensemble
Objective Classification (MODE)
Results
Overall WRF Ability
SKEBS/SPPT vs Classic
Physical Differences in Under- and Over-Banded Cases

9. WRF Ensemble Run 40-member WRF ensemble for each at 18-6-2 km dx
SKEBS and SPPT used for half of members
4 stochastic perturbations to u, v, potential temperature, physical tendencies, controlled by seed num in WRF namelist
KE backscattered upscale, rather than lost (Shutts 2005, Berner 2009, etc.)
Run objective classification tool on output, compare with observed radar reflectivity interpolated to z = 1 km, dx = 2 km
MODE - Method for Object-Based Diagnostic Evaluation (Davis et al. 2006, 2009)
Compile band type counts for each run
Examine good and bad cases for differences in variables

10. 2 km
6 km
 18 km 

11. WRF Ensemble GEFS Reanalysis IC/BC 45 vertical levels
Contains 10 members; using 5
45 vertical levels
“Classical” ensemble – 20 members
Contrast 2 PBL & 2 MP schemes
SKEBS+SPPT ensemble – 20 members
4 stoch perturbations
Grell-Freitas cumulus convection (18 km domain only)

12. 4 5 20 20 2 2

13. Motivation Results Experimental Design Cases WRF Ensemble
Objective Classification (MODE)
Results
Overall WRF Ability
SKEBS/SPPT vs Classic
Physical Differences in Under- and Over-Banded Cases

14. Example of WRF Under-bandedness

15. Example of WRF Under-bandedness

16. Example of WRF Over-bandedness

17. Example of WRF Over-bandedness

18. Motivation Experimental Design Results Cases WRF Ensemble
Objective Classification (MODE)
Results
Overall WRF Ability
SKEBS/SPPT vs Classic
Physical Differences in Under- and Over-Banded Cases

21. Motivation Experimental Design Results Cases WRF Ensemble
Objective Classification (MODE)
Results
Overall WRF Ability
SKEBS/SPPT vs Classic
Physical Differences in Under- and Over-Banded Cases

24. Summary of WRF Skill Ensemble consistently underbanded.
SKEBS/SPPT not significantly different than classical ensemble.

25. Motivation Experimental Design Results Cases WRF Ensemble
Objective Classification (MODE)
Results
Overall WRF Ability
SKEBS/SPPT vs Classic
Physical Differences in Under- and Over-Banded Cases

26. Non-banded
Over-banded
Fgen signal changes with level when significant, generally not even statistically significant.

27. Non-banded
Over-banded
More –MPV upstream of region of interest in over-banded composite.

28. Case – 600 hPa MPV
data: False Alarm and Miss 95 percent confidence interval: sample estimates: mean of x mean of y
Looking at all the hours and members, rather than just one hour, shows False Alarm members have a negative MPV bias (too unstable); Miss members have opposite.

29. Case – 650 hPa MPV
data: False Alarm and Miss 95 percent confidence interval: sample estimates: mean of x mean of y
Same for other case, but note 650 hPa instead of 600 hPa. (Same signal at 600 hPa but without sign change: both means are negative.)

30. So What Can I Make Out of This?

31. Summary of Physical Differences
Physical differences depend heavily on right band ID – still tweaking.
For now, signals pretty unclear.
Fgen seems to be less significant than in single-band conceptual model.
Stability (MPV) matters.
Sign of dT/dz errors flips between these two cases (not shown).

32. Questions?

33. Are Different Sites Different for the Same Case?
Let’s look at the Nov 2014 case using the area around KENX instead.

34. “Classic”
“SKEBS/SPPT”

35. “Classic”
“SKEBS/SPPT”

36. Non-banded
Over-banded
dT/dz signal also pretty subtle. If anything, opposite of expected.

37. Lapse rate less stable when multi-bands present. But…

38. Opposite signal just one hour later! Argh!