Correlations between observed snowfall and NAM “banded snowfall” forecast parameters Mike Evans and Mike Jurewicz WFO BGM

Banded vs. Non-banded events (from Novak et al.)

Frontogenesis and Stability (from Novak et al.) Frontogenesis (shaded) and saturated equivalent potential temperature (contoured)

Moderate Event Schematic Cross Section (from Wagner) Schematic cross section through a cool-season moderate precipitation band showing frontogenesis (red ellipse), negative EPV* (dashed blue ellipse), WMSS (brown dotted region), saturation equivalent potential temperature (dark green contours), and transverse circulation (arrows).

Lift in the “dendrite zone” (from Waldstreicher)

Questions… Key factors appear to be magnitude and persistence of frontal-scale forcing for lift and stability, plus favorable thermodynamics. Key factors appear to be magnitude and persistence of frontal-scale forcing for lift and stability, plus favorable thermodynamics. Can the significance of these factors be validated using 40 km AWIPS forecasts from a large number of heavy and moderate snow events? Can the significance of these factors be validated using 40 km AWIPS forecasts from a large number of heavy and moderate snow events? Can we identify thresholds of these values that would be useful to forecasters? Can we identify thresholds of these values that would be useful to forecasters?

Methodology Examine “synoptic” snow events in the BGM CWA since 2002 (throw out lake-effect; look for events with at least 30 dbz reflectivity). Examine “synoptic” snow events in the BGM CWA since 2002 (throw out lake-effect; look for events with at least 30 dbz reflectivity). 29 events identified - maximum snow accumulations ranged from 4 to 34 inches. 29 events identified - maximum snow accumulations ranged from 4 to 34 inches. Examine 6-24 hour NAM time-height forecasts of “banding factors” at these locations. Examine 6-24 hour NAM time-height forecasts of “banding factors” at these locations. Examine data in time-height cross-sections (to look at depth and persistence of features). Examine data in time-height cross-sections (to look at depth and persistence of features). Examine data in conventional cross-sections (for a better look at structure). Examine data in conventional cross-sections (for a better look at structure).

Results – Depth and Persistence Looking for depth and persistence of “favorable” conditions (areas on time- height diagrams). Looking for depth and persistence of “favorable” conditions (areas on time- height diagrams). Examine frontogenesis / Fn Convergence Examine frontogenesis / Fn Convergence Examine “* Signatures” – omega 80 percent. Examine “* Signatures” – omega 80 percent. Examine “** Signatures” = omega 80 percent. Examine “** Signatures” = omega 80 percent.

Correlations with event max snowfall - Depth and persistence of Frontogenesis and Fn convergence (12 hour forecasts)

Co-located upward vertical motion and negative EPV – 12 hr forecasts 25 of 29 events had a * Signature (omega < -8 with negative EPV). 25 of 29 events had a * Signature (omega < -8 with negative EPV). 17 of 29 events had a ** Signature (omega < -12 with negative EPV). 17 of 29 events had a ** Signature (omega < -12 with negative EPV).

Results – 12 hour forecasts

Negative Geostrophic EPV correlations – 12 hour forecasts

Result – yes / no questions

Results – yes/no questions

Correlations with max snowfall associated with conventional cross-sections (structure) Change in EPV over the band location from (T-6) to (T) (-0.89) Change in EPV over the band location from (T-6) to (T) (-0.89) Maximum omega in the Dendrite Zone (-12 to -18 degrees C): (0.75) Maximum omega in the Dendrite Zone (-12 to -18 degrees C): (0.75) Maximum omega within areas of negative EPV and RH > 80 percent (0.65) Maximum omega within areas of negative EPV and RH > 80 percent (0.65) Magnitude of negative EPV (0.63) Magnitude of negative EPV (0.63) Maximum omega (0.60) Maximum omega (0.60) Magnitude of Fn vector convergence (0.48) Magnitude of Fn vector convergence (0.48)

Acknowledgements Keith Wagner, SUNY Albany Keith Wagner, SUNY Albany Lance Bosart, SUNY Albany Lance Bosart, SUNY Albany Dan Keyser, SUNY Albany Dan Keyser, SUNY Albany David Novak, NWS ER, Scientific Services David Novak, NWS ER, Scientific Services Jeff Waldstreicher, NWS ER, Scientific Services Jeff Waldstreicher, NWS ER, Scientific Services