Correlations between observed snowfall and NAM forecast parameters

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					    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
Purpose

   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.
Outline
   Review of conceptual models regarding
    banding associated with major storms and
    moderate storms.
   Our local study methodology
   Correlations between snowfall and NAM-
    forecast dynamical banded snowfall
    parameters.
   Thermodynamic parameters and examples
    (MJ)
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
    disruptive.
   Can the same concepts shown in the
    previous slides be applied to these types of
    events?
Moderate Event Schematic Cross
Section




 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
   Stability
   Moisture
Forecasters are using these parameters
– especially at short ranges
Questions…

   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?
Methodology
   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.
   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
    convergence.
   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 time-
height cross-sections to examine depth
   and persistence of these types of
               features


  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
factors

   Define a * Signature – Omega < -8 µbs-1,
    EPV < 0, RH > 80 percent.
   Look for the depth and persistence of the *
    Signature
Example – March 30,
2003, 00z
Quantifying depth and persistence of key
parameters
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
forecasts
Omega and Fn convergence
Omega and negative EPV
Omega and * vs. snowfall
Results – depth and
persistence
   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
    motion.
   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
questions
   Examine scatter plots of answers to
    yes/no questions related to depth and
    persistence.
   Try to determine some operationally
    useful thresholds.
Result – yes / no
questions
Results – yes / no
questions
     Conventional Cross-Sections
     – Examine Structure
   For the 29 events in our database, cross-
    sections 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
Correlations
 – 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
   (0.48)
Summary – Dynamical
Parameters
   Importance of magnitude, depth and
    persistence of frontogenetical forcing
    and stability confirmed.
   24 hour forecasts not reliable at a
    point.
   Some useful thresholds identified.