Analysis and Prediction of Convective
Initiation on 24 May, 2002
June 14, 2004
Toulouse 2nd IHOP_2002 Science Meeting
Ming Xue, William Martin and Geoffrey Stano
School of Meteorology and Center for Analysis and Prediction of Storms
University of Oklahoma
Our work on May 24, 2002 CI Case
• MS Thesis work of Geoffrey Stano: Multiscale study based-on ADAS analyses
including special IHOP data sets, and by
examining observations directly.
• High-resolution simulation study (focus of
this talk)
• Very large (~2000) ensemble and adjoint
I.C. sensitivity study. dP/dqv sensitivity
fields.
Objectives
• Understand convective
initiation in this case
• Predict and understand the
convective systems involved
• Assess the sensitivity of
precipitation forecast to initial
conditions
Methodology
• Make use of special data sets collected during
IHOP
• Create high-resolution gridded data to perform
diagnostic analysis and for model initialization
• Verify model simulations against available data
• Analyze realistic high-resolution model
simulations to understand CI process
Synopsis of the Event
• Convection started between 20:00 and 20:30
UTC in Texas panhandle area along a dryline.
An intensive observation of IHOP_2002.
• Rapidly developed into a squall line and
advanced across Oklahoma and northern
Texas
• SPC reported almost 100 incidents of large hail,
15 wind reports, and two tornadoes in central
Texas
Time and Location of Initiation
(17UTC – 22 UTC)
Isotach Analysis: 250mb
19 UTC
20 UTC
TX Panhandle located at left-rear of upper level jet
A short wave trough moved over western TX
CIN and CAPE Analyses
CIN 19UTC
CIN 20UTC
CAPE 19UTC
CAPE 20UTC
Results of Stano’s diagnostic study
• Favorable condition pointing to initial initiation near
Childress, TX
– Placement of 250mb jet max and minimum
– Surface heating and high surface dew points
– Low Convective Inhibition values
• Causes for initiation
– Weakening of cap over the boundary layer by turbulently
mixing
– Break down of cap led to higher CAPE values
– Convergence along the dryline or possibly from the cold
front approaching dryline
• More specifics limited by data resolutions
Model Simulation Study
• Model can provide much more complete
data in both space and time
• Easier to examine cause and effect
• Model fields are dynamically consistent
• Caution - model solution may deviate from
truth therefore verification against truth is
necessary
Forecast Grids
4
Model Configurations
• 1 km grid nested inside 3 km one
• ADAS analyses for ICs and 3 km BCs
• NCEP ETA 18UTC and 00UTC analyses and
21UTC forecast used as analysis background
• ARPS model with full physics, including ice
microphysics + soil model + PBL and SGS
turbulence
• LBCs every 3h for 3km grid and every 15min
for 1 km grid
• 6 hour simulation/forecast, starting at 18 UTC
OBS Used by ADAS
•
•
•
•
•
•
•
•
•
ARM
COAG
IHOP Composite Upper Air - rawinsondes
KS Ground Water District 5
OK Mesonet
SAO
SW Kansas Mesonet
Western TX Mesonet
Profiler data absent
Surface Data Sets
Upper-Air Observing Sites
Dropsondes on 20 UTC Isodrosotherms
20:02UTC
20:32UTC
21:02UTC
21:32UTC
22:02UTC
22:58UTC
23:58UTC
Animation 20UTC-00UTC, KLBB rada
Animation 20UTC-00UTC, KFRD rada
3km model simulation/forecast
1 km simulation
T=2.5h
20:30UTC
T=3.0h
21:00UTC
T=3.5h
21:30UTC
T=4.0h
22:00UTC
T=4.5h
22:30UTC
T=5h
23:00Z
T=5h
23:00Z
T=5.5h
23:30Z
T=5h
23:00Z
Animations
• Surface reflectivity
• 2km level w, winds
See movies
(18:30 UTC – 00:00 UTC)
See Movies
19:16UTC
19:45UTC
20:30UTC
23:00UTC
Vertical cross-section animations
• w and q
• w and qv
• qv and qe
See Movies
See Movies
+
+
+
X-section at y=170 km
See movie
Conclusions
• Dryline convection occurred in favorable
synoptic-scale environment (upper jet, sfc trough,
convergence, moisture supply, CAPE, etc.)
• Realistic CI predicted by the model, especially at 1
km resolution
• Model CI associated with dryline was ~ 30min-1h
late (related to spinup?)
• Active development of BL eddies observed before
C.I. in convergence zone (50-100km wide) and in
the moist air (underneath CAP)
Conclusions – continued
• A number of successfully penetration by B.L. parcels
through the CAP before sustained convection is achieved
• Sustained convection occurred on the moist side near but
not in the well mixed convergence zone or at the location
of strong Td gradient
• Sloping terrain and surface heating played key roles
• Cold frontal convection started earlier, due to lifting
• Cold front or the triple point did not directly trigger dryline
convection – dryline did it on its own and initiated
convection further south
• Cold air surge to the N.W. of dryline did not directly
trigger convection (was in dry air, no CAPE)
Conclusions - continued
• Role of surface wind convergence induced by mixing
unclear to C.I. – perhaps indirectly by destabilizing the
zone
• B.L. depth increases with time, so does its temperature,
due to surface heating and eddy mixing)
• Any role of played by gravity waves or their interaction
with B.L. eddies – not clear.
• Eddies organize into rolls/cloud streets near the edges of
the convergence zone, where background wind is present
• Fine westward-propagating echoes observed by KLBB
radar at the low levels are associated with the rearward
propagating outflow boundary
Comments very welcome!
Like to compare with fine-scale
observations
Posters
• Dawson and Xue: Data sensitivity study on June 16 MCS
case
• Liu and Xue: 3DVAR assimilation GPS slant path water
vapor data with an isotropic spatial filter. Tested on
simulated data from June 18, 2002 dryline case.
• Tanamachi: June 12, 2002 boundary layer water vapor
oscillation case - undular bore? Observation and simulation
study
• Sensitivity Study – Title Slide
TANGENT-LINEAR AND ADJOINT MODELS
VERSUS PERTURBED FORWARD MODEL
RUNS
William J. Martin
Ming Xue
Center for Analysis and Prediction of Storms and the
University of Oklahoma, Norman, Oklahoma
6 hr. forecast
total
accumulated
precipitation
• The sensitivity of a defined response function, J, such
as the total rainfall in some area, is sought as a
function of the initial fields.
• This is done by making a large number of forward
model runs, each with a perturbation in a different
location. The location of the perturbation is varied so
as to tile a 2-D slice of the model domain.
• dx=dy=9km, no Cb parameterization
Example of an
initial
perturbation
1 g/kg qv pert
Response Function Defined As:
(q )xy

J
 xy
vi
i
i
Sensitivity Defined As:
S ( x, y)  1000  ( J ( x, y)  J control )
Sensitivity field
for the
dependence of
total PPT on
initial boundary
layer moisture
perturbations
+1 g/kg
perturbations