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Day 1 Diagnosis

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http://www.crh.noaa.gov/arx/micrope.html. Precipitation type ... This workshop linked to. http://www.wdtb.noaa.gov. Precipitation efficiency ... – PowerPoint PPT presentation

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Title: Day 1 Diagnosis


1
Day 1 Diagnosis Forecast methodology
  • J. LaDue
  • Winter Wx workshop 2003
  • Boulder, CO

2
A miniscenario
  • It is 05 UTC
  • Its Tuesday night and the first arctic outbreak
    of the season has arrived
  • A large scale upper-level trough lies to the
    west.
  • An passing shortwave failed to produce expected
    snow the previous afternoon
  • The ETA is out and its time to start thinking of
    a forecast.

3
A miniscenario
  • You could just use model Omega and forecast
    precip
  • This could result in meteorological cancer
  • To avoid that, we will look at real data and
    conceptual models.

4
850 mb 00 UTC
5
700 mb 00 UTC
6
500 mb 00 UTC
7
300 mb 00 UTC
DCVA
8
A short-term forecast methodology
  • Check accuracy of model analysis
  • Diagnose model analysis for winter precipitation
    ingredients
  • Planview maps and cross-sections
  • Adjust model-based ingredients with real data
  • Make the forecast
  • Further adjustments with real data into forecast
    period

9
Check the sanity of model analysis
  • For example with ETA analysis, compare these
    parameters to raobs, profilers
  • SFC T, Td, wind, pressure
  • 925 - 500 mb T, T-Td, wind, height
  • 300 mb and up T, wind, height
  • These offer direct comparisons to check validity
    of model analysis
  • WV/IR imagery can be used to qualitatively
    evaluate strength of systems

10
Diagnosing ingredients for winter precip
  • Forcing
  • Stability (response to forcing)
  • Moisture
  • Precip efficiency
  • Precip type
  • Also helps to figure out where that dang precip
    came from

11
Diagnosing ingredients for winter precip
  • Forcing What type, where and how strong will it
    be?
  • Stability What will be the response to the
    forcing?
  • Moisture Will there be saturation (RH) and how
    much moisture (q) will there be?
  • Precip efficiency How will forcing,
    instability, and saturation coincide with the
    dendrite formation layer?
  • Precip type Top down approach

12
Forcing
  • Start with synoptics first QG
  • DCVA (differential cyclonic vort advection,
  • ?2WAA (local max in warm advection),
  • QG frontogenesis
  • Diagnostics
  • Isentropic
  • Q-vectors

13
850 mb 00 UTC
Frntgenesis
14
700 mb 00 UTC
Frntgenesis
WAA
15
500 mb 00 UTC
DCVA
16
300 mb 00 UTC
DCVA
17
Forcing
  • Q-vectors
  • Exist when geostrophic wind alters the thickness
    gradient vectors
  • Anytime the thickness gradient changes the
    thermal wind goes out of balance
  • Secondary ageostrophic circulations attempt to
    restore balance
  • Ageostrophic winds keep us employed
  • ?Q-vector

18
Forcing Flow inflections
Qs Q component parallel to isotherms
?T3
T
?
T?T
?T2
Q2
?T1
???
Q1
?T2
?T3
Q2
?T2
?T1
Q1
19
Forcing QG frontogenesis
Qn Q component parallel to isotherms
?
?T1
T
Q1
?T2
???
T?T
?2??
Q2
?T3
?T3
T2?T
?T1
?T2
Q1
20
Forcing QG
Qn Qs total Q vector
T
T?T
?
T2?T
???
?2??
(??Qn ?? Qs) 21
Q-vectors
22
Forcing
Best convergence means strongest QG forcing for
vertical motion
23
Forcing ??Q
650 mb 00 UTC ETA
24
Forcing
  • If QG cannot explain the vertical motion (fcst or
    real)
  • Then go downscale to mesoscale
  • Next is 2-D full wind frontogenesis

25
Frontogenesis (definition)
  • The 2-D frontogenesis function (F) quantifies
    the change in (potential) horizontal temperature
    gradient following air parcel motion
  • F0 frontogenesis, F
  • Refer to Banacos presentation

26
Forcing Frontogenesis
27
Forcing Frontogenesis
28
Forcing
  • If 2-D frontogenesis fails to explain ascent,
    then
  • Externally forced mesoscale lifting?
  • Orographic ascent
  • Frictional convergence
  • Shoreline
  • Cyclonic sfc flow
  • Local diabatic heating
  • Water land thermal differences
  • Land cover thermal gradients
  • Outflow boundaries

29
Forcing
  • Orographic ascent

Weak cross-barrier winds or high stability KE PE Fr
Strong cross-barrier winds or low stability KE
PE Fr 1
http//www.meted.ucar.edu/mesoprim/flowtopo/
30
Forcing
  • Diabatic heating gradients

In this case, lake effect enhancement due to land
breeze circulations
http//www.meted.ucar.edu/mesoprim/seabreez/
31
Forcing
  • Frictional convergence

fr
f
f
?P
?P
32
Stability response
33
Stability response
34
Stability response
35
Stability response
36
Stability response
You can get away with Looking at only EPV
to Evaluate CSI and CI
37
Stability response
Courtesy Pete Banacos
38
Stability response
http//www.nssl.noaa.gov/schultz/csi.shtml
39
Stability response
This is where MPVg http//www.nssl.noaa.gov/schultz/csi.shtml
40
Combined forcing and stability
  • PVQ (??Q) (MPVg) for negative MPVg and
    negative ??Q
  • 0 for positive MPVg and
    positive ??Q
  • Negative PVQ means both Q-vector convergence and
    CSI, CI or both are occurring in the same layer
  • This will not occur often as the best instability
    lies on a different layer than the best forcing

41
PVQ
42
Moisture
  • Relative Humidity
  • Mixing Ratio

43
Precipitation efficiency
  • See Dan Baumgardts talk
  • http//www.crh.noaa.gov/arx/micrope.html

44
Precipitation efficiency
45
Precipitation efficiency
  • See Dan
  • Baumgardts talk
  • http//www.crh.noaa.gov/arx/micrope.html

46
Precipitation type
  • Refer to the top-down approach in Dan Baumgardts
    presentation
  • http//www.cira.colostate.edu/ramm/visit/ptype/tit
    le.asp

47
Model Forecast
  • Add to typical displays
  • 4-panel ingredients for 600-650mb, 650-700mb,
    700-750mb with 80 km model
  • Ingredients cross-sections across thickness
    gradients in areas of forcing
  • Full frontogenesis
  • Ensembles

48
Adjust model forecast
  • How will analysis errors affect the ingredients?
  • Does the current data agree with model forecasts?
  • If not, then how will you adjust model forecasts?

49
A short-term forecast methodology
  • Check accuracy of model analysis
  • Diagnose model analysis for winter precipitation
    ingredients
  • Planview maps and cross-sections
  • Adjust model-based ingredients with real data
  • Make the forecast
  • Further adjustments with real data into forecast
    period

50
Related links
  • Ingredients-based methodology
  • http//cimss.ssec.wisc.edu/goes/visit/ingredients.
    html
  • CSI, mesoscale circulations online training
  • http//www.meted.ucar.edu/topics_meso.php
  • Precipitation type and efficiency
  • http//www.cira.colostate.edu/ramm/visit/ptype.htm
    l
  • This workshop linked to
  • http//www.wdtb.noaa.gov

51
Precipitation efficiency
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