Mission Impossible: Episode

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Mission Impossible Episode23, Snowing when it
shouldnt
Douglas Miller UNC Asheville
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Collaborators

• L. Baker Perry, Appalachian State University,
• Sandra Yuter, North Carolina State University,
• Laurence Lee, NOAA/NWS, Greer, SC,
• Stephen Keighton, NOAA/NWS, Blacksburg, VA
• David Hotz, NOAA/NWS, Morristown, TN

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Many students
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Outline

• Butterflies and snow
• On the large (synoptic-) scale
• On the medium (meso-) scale
• On the micro-scale
• What chance have we?

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Butterflies and snow

• Lorenz chaos theory

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Butterflies and snow
BACKGROUND

• An accidental discovery...and a coffee cup is
  involved
• Ran a simple computer weather model, got a
  forecast
• Re-ran portion of integration
• Coffee break
• New forecast was completely different from the
  original forecast

Edward Lorenz
http//en.wikipedia.org/wiki/Edward_Lorenz
Initial round-off errors were the culprit
http//www.exploratorium.edu/complexity/CompLexico
n/lorenz.html
TRY THIS? http//www.exploratorium.edu/complexity/
java/lorenz.html
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Butterflies and snow
BACKGROUND

• Read Ray Bradburys A Sound of Thunder for the
  first mention of the butterfly effect

HERE? http//www.sba.muohio.edu/snavely/415/thunde
r.htm
http//www.vision.caltech.edu/feifeili/101_ObjectC
ategories/butterfly/
http//en.wikipedia.org/wiki/ImageRaybradbury.gif
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Butterflies and snow

• Fundamental theorem of predictability as a
  result
• Even if the computer weather forecast model is
  perfect, and even if the initial conditions are
  known almost perfectly, the atmosphere has a
  finite limit of predictability

http//wwwt.emc.ncep.noaa.gov/mmb/mmbpll/mmbverif/
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Butterflies and snow

• Fundamental theorem of predictability
  implications
• Small errors in the coarser (resolvable)
  structure of the weather pattern tend to double
  in about 2-3 days

http//www.nws.noaa.gov/im/pub/wrta8604.pdf
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Butterflies and snow

• Fundamental theorem of predictability
  implications
• Small errors in the coarser structure
• Every time we cut obs error in half, we extend
  the range of acceptable prediction by three days
• Could make good forecasts several weeks in advance

http//www.nws.noaa.gov/im/pub/wrta8604.pdf
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Butterflies and snow

• Fundamental theorem of predictability
  implications
• Small errors in the finer (unresolvable)
  structure of the weather pattern tend to double
  in hours or less

Ahrens (2005)
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Butterflies and snow

• Fundamental theorem of predictability
  implications
• Errors in the finer structure of the weather
  pattern tend to produce errors in the coarser
  structure
• This appreciable error in the coarse structure
  will grow and inhibit extended range forecasting

Ahrens (2005)
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Butterflies and snow

• Fundamental theorem of predictability
  implications
• Errors in the finer structure of the weather
  pattern tend to produce errors in the coarser
  structure
• Cutting obs error of fine structure in half would
  extend coarse structure forecasts only by hours
  or less

Ahrens (2005)
hopes for predicting two weeks or more in advance
are greatly diminished.
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On the large (synoptic-) scale

• Snowing when it shouldnt

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On the large (synoptic-) scale
http//ww2010.atmos.uiuc.edu/guides/mtr/cyc/gifs/s
at1.gif
http//www.atmos.umd.edu/meto200/4_3_03_lecture_f
iles/v3_slide0044_image025.jpg
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On the large (synoptic-) scale

• Cloud structure of Fronts
• Norwegian school (Bjerknes and Solberg 1922)

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On the large (synoptic-) scale

• Cold Season precipitation amount
• Snow ingredients
• Cold air
• Moisture
• Lift

http//blogs.trb.com/news/weather/weblog/wgnweathe
r/weather_snap_shots/full/
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On the large (synoptic-) scale

• Northwest Flow Snowfall (NWFS)
• Snow
• Cold air
• Moisture
• Lift

1500 UTC 9 Jan 2007
http//www.erh.noaa.gov/gsp/localdat/cases/9-10Jan
2007NWFS/9-10Jan2007NWFS.html
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On the large (synoptic-) scale

• NWFS
• Snow
• Cold air
• Moisture
• Lift

0000 UTC 10 Jan 2007
http//www.erh.noaa.gov/gsp/localdat/cases/9-10Jan
2007NWFS/9-10Jan2007NWFS.html
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On the large (synoptic-) scale

• NWFS
• Snow
• Cold air
• Moisture
• Lift

1600 UTC 10 Jan 2007
http//www.erh.noaa.gov/gsp/localdat/cases/9-10Jan
2007NWFS/9-10Jan2007NWFS.html
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On the medium (meso-) scale

• Mountains and convection

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On the medium (meso-) scale
Localized nature of NWFS accumulations
http//www.erh.noaa.gov/gsp/localdat/cases/27Feb20
08NWFS/26-28_february_2008.gif
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On the medium (meso-) scale
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On the medium (meso-) scale
Portable meteorological station near the crest of
Poga Mountain at 1137 m.
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On the medium (meso-) scale
Pluvio weighing precipitation gauge, Parsivel
disdrometer, and vertically-pointing Micro Rain
Radar (L to R) at 1018 m.
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Snow streak
Knoxville
Mt. Mitchell
Asheville
Jan. 3, 2008 1145 EST
Courtesy Grant Goodge
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Mt. Mitchell
Asheville
Jan. 2, 2008 1330 EST
Courtesy Grant Goodge
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On the medium (meso-) scale

• Diurnal convective mode

late afternoon
early morning
Mesoscale snowbands persisting downstream of the
southern Appalachians during northwest flow
upslope events - James Hudgins - 2008, 33rd
National Weather Association Annual Meeting,
Louisville, KY.
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On the medium (meso-) scale
Vertically-pointing radar (MRR) data for 27-28
February 2008 (Storm total 21.1 cm, 47 kg m-3).
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On the medium (meso-) scale
very shallow moist layer
Poga Mountain sounding initiated at 1244 UTC 27
Feb 2008.
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On the micro-scale

• How do we make a snowflake?
• Vertical bias

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On the micro-scale

• How do we make a snowflake?
• Ice nuclei (difficult to find in nature)
• Mixed cloud ice particles and supercooled liquid
  water

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On the micro-scale

• (a) Growth from the vapor phase (vapor
  deposition)
• The difference between the saturated vapor
  pressures over water and ice is a maximum near a
  temperature of about -14oC
• Ice crystals growing by vapor deposition in mixed
  clouds increase in mass most rapidly at
  temperatures around -14oC

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On the micro-scale

• The shape of vapor-to-ice ice crystals

(B. Perry)
Vapor deposition results in shapes (habits) that
are either platelike or prismlike
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On the micro-scale

• Preferred vapor-to-ice habits

4.23(a)
4.23(b)
4.23(c,d)
4.23(b)
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On the micro-scale

• (b) Growth by riming
• Ice particles increase mass by colliding with
  supercooled droplets which then freeze onto them
  (riming)
• When riming proceeds beyond a certain stage it
  becomes difficult to discern the original shape
  of the ice crystal rimed particle is then
  referred to as graupel

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(B. Perry)
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On the micro-scale

• (c) Growth by aggregation
• Growth in clouds caused by ice particles
  colliding and aggregating with one another
• Mechanism works if terminal fall speeds of ice
  particles are different
• Unrimed prismlike ice crystals have greater
  terminal fall speeds for longer crystals
• Unrimed platelike crystals have terminal fall
  speeds that are independent of diameter
• Collisions of ice particles in clouds are greatly
  enhanced if some riming has taken place

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On the micro-scale

• (c) Growth by aggregation one ice particles
  collide, will they adhere together (aggregate)?
  The answer depends on
• Habit (shape) of ice particles
• Dendrites tend to adhere
• Two solid plates tend to rebound
• The temperature of the ice particles
• Probability of adherence increases with
  increasing temperature

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On the micro-scale

• (c) Growth by aggregation examples

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On the micro-scale

• Conclusion
• the growth of ice crystals, first by deposition
  from the vapor phase in mixed clouds and then by
  riming and/or aggregation, can produce
  precipitation-sized particles in reasonable time
  periods ( 40 minutes)

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On the micro-scale

• Clouds in which growth by riming is important
• Overseeding can eliminate supercooled droplets
  and growth by riming significantly reduced
• In absence of riming, ice particles grow by
  deposition, fall speeds are reduced, and wind
  carries them farther across the mountain
• Can be used to divert snowfall from windward to
  leeward slopes

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On the micro-scale

• Vertical bias
• Growth by riming and aggregation is typically
  parameterized in models by assuming a
  distribution of particle sizes and terminal fall
  speeds
• Implies the deeper the cloud, the higher the
  precipitation rate for a mixed cloud having
  heterogeneous-sized particles ? low accumulations
  in NWFS (shallow clouds)

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On the micro-scale

• Vertical bias
• Recent research (Dr. Bart Geerts, Univ. of
  Wyoming) suggests an extended horizontal path for
  the ice particles in a cloud may also yield high
  precipitation rates
• Hailstone analogy (hail diameter related to time
  spent spiraling about cloud)

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On the micro-scale
(a)
(b)
Number of snow events by (a) wind direction and
(b) snow density.
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On the micro-scale
(a)
(b)
Plots of (a) new snowfall density by event type
and (b) new snowfall density vs. surface
temperature.
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On the micro-scale
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What chance have we?

• Computer model results

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What chance have we?

• Computer model results
• Conventional Wisdom (CW)
• If you get the large (synoptic-) scale
  prediction correct, you have a better chance of
  predicting the medium (meso-) and micro-scales
  correctly.
• Is the CW true?

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What chance have we?
700 am EST 27 Feb 2008
500
SLP
850
700
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What chance have we?
700 am EST 27 Feb 2008
Composite reflectivity for NWFS event over the
TN/NC region valid 1158 UTC 27 Feb 2008.
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What chance have we?
(a)
(b)
Poga Mountain observations of (a) average blue
and maximum pink wind speed m s-1, (b)
relative humidity , and (c) air temperature
oF over the period 0000 UTC 27 Feb - 0000 UTC
28 Feb 2008.
(c)
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What chance have we?

• Methodology
• macro ensembles WRF (v2.1.1)
• 36, 12, 4 km domains, 50 vertical levels
• Initial conditions
• NARR (29 lvls, 32km), NAM (38 lvls, 12km), or
  GFS (22 lvls, 1o)
• Physics options
• Control (ctrl) Betts-Miller-Janjic CPS, YSU PBL,
  and Lin et al. microphysics
• CPS (exp1) Kain-Fritsch CPS
• PBL (exp2) Mellor-Yamada-Janjic PBL
• micro ensembles microphysics tests

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What chance have we?
WRF nested domains.
WRF topography (m).
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What chance have we?
500
SLP
Blue NARR Green NAM Red GFS
700
850
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What chance have we?

• Results
• Macro ensembles winner (Table 2)
• NARR initialization, exp1 physics
• Micro ensembles winner (Table 3)
• mp1, no CPS in innermost domain Hong et al.
  (2004), WSM 3-class scheme

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What chance have we?
Table 2
best synoptic-scale simulation
Accumulated precipitation (In.) liquid equivalent
statistics for the macro WRF experiments for
the 60-h period 1200 UTC 26 Feb 0000 UTC 29
Feb 2008.
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What chance have we?
Table 3
Accumulated precipitation (In.) liquid equivalent
statistics for the micro WRF experiments for
the 60-h period 1200 UTC 26 Feb 0000 UTC 29
Feb 2008.
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What chance have we?
Blue NARR Green NAM Red GFS
Vertical T, Td oC profile forecasts of the
macro simulations valid 0300 UTC 27 Feb 2008.
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What chance have we?
Vertical T, Td oC profile forecasts of the
NARR/exp1 mp1 simulations valid 1500 UTC 27 Feb
2008.
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What chance have we?
Zoomed WRF topography (m) at 4 km.
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What chance have we?
(a)
(b)
Wind
Wind
Vertical cross section (location given in Fig.
15) of q contours, K and water mixing ratio
x10-5 kg/kg through Poga Mountain at 1500 UTC
27 February 2008 for the (a) NARR/exp1 and (b)
mp1 simulations for domain 3 (4 km).
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What chance have we?
(a)
(b)
Accumulated precipitation (liquid equivalent,
Inches) over the 60-h period 1200 UTC 26 Feb
0000 UTC 29 Feb 2008 for the (a)NARR/ exp1 and
(b) mp1 simulations of domain 3 (4 km).
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What chance have we?
(a)
Accum. precip. error (In) over the 60-h period
1200 UTC 26 Feb 0000 UTC 29 Feb 2008 for the
(a) NARR/exp1 and (b) mp1 simulations of domain 3
(4 km).
(b)
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What chance have we?
Backward trajectories ending at Poga Mountain at
1500 UTC 27 Feb 2008 for the outermost (36 km)
NARR/exp1 simulation.
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What chance have we?
(a)
(b)
Backward trajectories ending at Poga Mountain at
1500 UTC 27 Feb 2008 for the (a) NARR/exp1 and
(b) mp1 simulations for domain 3 (4 km).
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What chance have we?

• Results (continued)
• Miscellaneous
• Modest differences between NARR/exp1 and mp1
  simulations in
• vertical T, Td profile
• mountain wave response
• trajectory forecast
• ? lead to significant differences in accumulated
  precipitation forecasts

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What chance have we?
Table 2
best synoptic-scale simulation
Accumulated precipitation (In.) liquid equivalent
statistics for the macro WRF experiments for
the 60-h period 1200 UTC 26 Feb 0000 UTC 29
Feb 2008.
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What chance have we?

• Conclusions and future work
• best synoptic-scale simulation does not assure
  best model acc precip fcst (Table 2)
• contrary to CW
• a probabilistic approach appears as the only way
  to predict the range of realistic potential
  outcomes
• what role sub-grid scale convection (e.g. cloud
  rolls) and mountain waves?

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Acknowledgements

• Funded by a UNC-GA Research Competitiveness grant
  (2007, 2008)
• Funded by RENCI (2008, 2009)
• East Tennessee State University and Prof. Gary
  Henson
• Naval Postgraduate School and Dick Lind for use
  of a sounding base unit and rawinsondes
• NERSC (DOE) for computing resources

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End
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What chance have we?
(a)
(b)
Water species schematics for the (a) NARR/ctrl
and (b) mp1 experiments.