Part 1 – Dynamical Parameters

Correlations between
observed snowfall and NAM
forecast parameters, Part I –
Dynamical Parameters
Mike Evans
NOAA/NWS Binghamton, NY
November 1, 2006
Northeast Regional Operational Workshop
Albany, NY
Many case studies have shown utility of
looking at certain key dynamical parameters
– mostly for major events.
Goal : to demonstrate the utility (or lack
thereof) of examining NAM forecasts of
dynamical parameters to differentiate large
events from small events using a large data
base of cases.
Review of conceptual models regarding
banding associated with major storms and
moderate storms.
Our local study methodology
Correlations between snowfall and NAMforecast dynamical banded snowfall
Thermodynamic parameters and examples
Heavy Banded Snowfall Conceptual
Model (from Nicosia and Grumm)
Frontogenesis and Stability (from Novak
et al.)
Frontogenesis (shaded) and saturated equivalent
potential temperature (contoured)
Next Question… What about
“moderate” events?
Observational experience indicates that
many “moderate” events are also banded.
Events with less than 0.5 inches of liquid
equivalent precipitation can still be
Can the same concepts shown in the
previous slides be applied to these types of
Moderate Event Schematic Cross
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).
Summary… Key dynamic factors
effecting snowfall appear to be
strength, depth and persistence of…
Frontogenesis / Frontogenetical Forcing
Forecasters are using these parameters
– especially at short ranges
Can we prove that there are direct
correlations between observed snowfall and
the intensity, depth and persistence
these key factors, 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?
Examine “synoptic” snow events in the BGM CWA since 2002
(throw out lake-effect; look for events with at least 30 dbz
29 events identified - maximum snow accumulations ranged
from 4 to 34 inches.
For each event, choose a time when a well-defined band
could be identified. Identify the maximum event-total snowfall
that occurred in or near the band.
Examine 0-24 hr forecast data in time-height cross-sections
(to look at depth and persistence of features).
Examine 0-24 hr forecast data in conventional cross-sections
(for a better look at structure).
What parameters do we want to be
looking at?
Frontal-scale forcing for upward
motion – look at Fn vector
Instability – look for negative
geostrophic EPV
Omega. Why not just look at omega
and forget the other stuff?
Results – 12 hour forecast
event maximum values
A band of Fn convergence was found
below 500 mb in 25 of 29 events.
A layer of negative EPV was identified
with 28 of 29 events.
Upward motion exceeding 8 µbs-1
found in 26 of 29 events
Today – Focus on looking at timeheight cross-sections to examine depth
and persistence of these types of
Depth and persistence of threshold values of
Fn divergence, EPV and omega
– Calculate “areas” on time-height plots
– Determine yes/no for certain thresholds
Look for combinations of favorable
Define a * Signature – Omega < -8 µbs-1,
EPV < 0, RH > 80 percent.
Look for the depth and persistence of the *
Example – March 30,
2003, 00z
Quantifying depth and persistence of key
Correlations - example
Depth and persistence of Fn convergence 12 hour forecasts
Depth and persistence of negative EPV –
12 hr forecasts
Depth and persistence of omega – 12 hr
Omega and Fn convergence
Omega and negative EPV
Omega and * vs. snowfall
Results – depth and
12-hour forecast depth and persistence of lower-tropospheric
Fn convergence and negative EPV at a point correlates well
with maximum snowfall.
12 hour forecast depth and persistence of co-located upward
vertical motion, negative EPV and high RH (* Signature)
correlate very well to maximum snowfall.
Examining * Signature yields a better correlation with
maximum snowfall than just examining upward vertical
Upward vertical motion and negative EPV are positively
correlated (they don’t just get together by accident).
24 hour forecasts do not correlate well, mainly due to
a handful of major model positioning busts.
Results – depth and
persistence / yes / no
Examine scatter plots of answers to
yes/no questions related to depth and
Try to determine some operationally
useful thresholds.
Result – yes / no
Results – yes / no
Conventional Cross-Sections
– Examine Structure
For the 29 events in our database, crosssections were taken through radar
indicated snow bands, near their time of
maximum development
Use 0-6 hr forecasts from the 40-km NAM
Values were taken at locations on the
cross-section that were within 50 km of
the snow band
Dynamical Parameter
– Maximum omega within areas of negative
EPV and RH > 80 percent (0.65)
– Magnitude of negative EPV (0.63)
– Maximum omega (0.60)
– Magnitude of Fn vector convergence
Summary – Dynamical
Importance of magnitude, depth and
persistence of frontogenetical forcing
and stability confirmed.
24 hour forecasts not reliable at a
Some useful thresholds identified.