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

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

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

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?

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

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…

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

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

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

2 km 6 km  18 km 

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)

4 5 20 20 2 2

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

Example of WRF Under-bandedness

Example of WRF Under-bandedness

Example of WRF Over-bandedness

Example of WRF Over-bandedness

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

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

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

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

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

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

2014-11-26 Case – 600 hPa MPV data: False Alarm and Miss 95 percent confidence interval: -0.9889583 -0.3730520 sample estimates: mean of x mean of y -0.4469577 0.2340474 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.

2017-01-07 Case – 650 hPa MPV data: False Alarm and Miss 95 percent confidence interval: -0.5469952 -0.2021646 sample estimates: mean of x mean of y -0.1788601 0.1957199 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.)

So What Can I Make Out of This?

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).

Questions?

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

“Classic” “SKEBS/SPPT”

“Classic” “SKEBS/SPPT”

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

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

Opposite signal just one hour later! Argh!