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Does mesoscale instability control sting jet variability?. Neil Hart, Suzanne Gray and Peter Clark. Martínez-Alvarado et al 2014, MWR. Instability and Predictability. Baroclinic Instability. Convective Instability. Symmetric Instability. ~1km ~30mins. ~100km ~6hours. ~1000km ~day. - PowerPoint PPT Presentation
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Does mesoscale instability control sting jet variability?
Neil Hart, Suzanne Gray and Peter Clark
Martínez-Alvarado et al 2014, MWR
Instability and Predictability
Convective Instability
SymmetricInstability
BaroclinicInstability
~100km~6hours
~1000km~day
~1km~30mins
Courtesy: ECMWF
St Jude Forecast:Global Ensemble
Courtesy: MetOffice
St Jude Forecast: MetOffice 4km
IR satellite image at 0600 UTC
Courtesy: MetOffice & EUMETSAT
St Jude Forecast: MetOffice UKV
Fig. 14 Browning 2004, QJRMS
Why mesoscale instability?
• Browning 2004 hypothesized that Conditional Symmetric Instability (CSI) in the cloud head cloud is an explanation for the fingering seen in satellite imagery at the tip of some cloud heads
• The resulting slantwise circulation would see ascent into the cloud head with descent near the cloud head tip
• This hypothesizes a mechanism for sting jet descents, as seen in 1987 Great Storm
Moist Baroclinic LC1 experimentFig. 7 Baker et al 2013, QJRMS
Why mesoscale instability?
Shading is number of pressure levels between 800hPa and 600hPa, that have CSI (MPVS*<0)
Blue circle indicates position of air parcels manually identified as part of the sting jet descent
T -10hrs
Why mesoscale instability?
T -6hrs
Moist Baroclinic LC1 experimentFig. 7 Baker et al 2013, QJRMS
Shading is number of pressure levels between 800hPa and 600hPa, that have CSI (MPVS*<0)
Blue circle indicates position of air parcels manually identified as part of the sting jet descent
Why mesoscale instability?
T -2hrs
Moist Baroclinic LC1 experimentFig. 7 Baker et al 2013, QJRMS
Shading is number of pressure levels between 800hPa and 600hPa, that have CSI (MPVS*<0)
Blue circle indicates position of air parcels manually identified as part of the sting jet descent
Friedhelm, Robert and Ulli
Martínez-Alvarado et al 2014, MWR
Smart & Browning 2013
Friedhelm 8 Dec ‘11 Robert 27 Dec ’11 Ulli 3 Jan ‘12
Identified with DSCAPE diagnostic applied to ERA-Interim (after Martínez-Alvarado 2012)
Courtesy: EUMETSAT, Sat24.com
Cyclone Robert
MethodologyFriedhelm 8 Dec ‘11 Robert 27 Dec ’11 Ulli 3 Jan ‘12
1. Produce 24 member ensemble simulations of each storm
2. Compute back trajectories from low-level jet region of each member
3. Cluster analysis to classify trajs. to identify descending airstreams
4. Explore link between these descents and CSI across ensemble
• MetOffice Unified Model vn8.2
• MOGREPS-Global ETKF
24 Init. Pert. Members
(Bowler et al, 2008)
• MOGREPS-Regional
• N. Atl. & Europe Domain
• 12km, 70 Levels
• All storms initialised at 18 UTC
the day before maximum intensity
• Results analysed further are T+10 to T+24 forecasts
Model Setup
Synoptic Overview
Synoptic Overview
Small spread in synoptic scale evolution between ensemble members:
Good, since can now focus on mesoscale differences
Compute Back Trajectories
Control Run from Cyclone Robert ensemble
Compute Back Trajectories
Cloud Top Temperature
Control Run from Cyclone Robert ensemble
850hPa 45m/s Isotach
Compute Back Trajectories
Control Run from Cyclone Robert ensemble
Trajectories Computed with Lagranto (Wernli & Davies, 1997)
Classification of Airstreams
Ward’s Hierarchical Clustering Algorthim
Use Relative Humidity to remove descents that started outside cloud head
Identify class means that descend Cluster Class Mean
Trajectories: Each trajectory described by x,y, P, θw for 5 hours preceding arrival in low-level jet
Classification of Airstreams
Ward’s Hierarchical Clustering Algorthim
Use Relative Humidity to remove descents that started outside cloud head
Identify class means that descend
Classification of Airstreams
Use Relative Humidity to remove descents that started outside cloud head
Identify class means that descend
Classification of Airstreams
Use Relative Humidity to remove descents that started outside cloud head
Classification of Airstreams
Classification of Airstreams
Each Class contains a population of individual trajectories that arrive at given time.
Next slideSize of these populations are gathered for all descent classes at all times for each ensemble member
# of Traj. Arriving in LLJ
160
0
# of Traj. Arriving in LLJ
160
0
Majority of of ensemble members have peak in # trajs at 12UTC
Ensemble Sensitivity
Control run cloud head Control run 281K θw 850hpa
Interpret as change in # trajs for 1 s.d change in CSI metric
X
Ensemble Sensitivity
Methodology after Torn & Hakim 2008
X
Ensemble Sensitivity
Methodology after Torn & Hakim 2008
X
Ensemble Sensitivity
Methodology after Torn & Hakim 2008
X
Ensemble Sensitivity
Methodology after Torn & Hakim 2008
X
Consistent synoptic development across ensemble
Considerable variability in mesoscale wind features
Demonstrated method to classify descending airstreams
Large variability in number of descending trajectories across
ensemble
Does mesoscale instability control sting jet variability?
Strength of sting jet descent is associated
with CSI in the cloud head
(in Robert as simulated with MetUM)
Conclusions
Cyclone Friedhelm
Cyclone Friedhelm Comparison toMartinez- Alvarado et al 2014 manual
classification
Ensemble Sensitivity
CSI across ensemble
# tr
ajs
If correlation > threshold(0.5 used here), good!
Ensemble Sensitivity
CSI across ensemble
# tr
ajs
∆x
∆y
Calculate Gradient
Ensemble Sensitivity
CSI across ensemble
# tr
ajs
∆x
∆y
Ens. Sensitivity = ∆y (∆x = 1 s.d.)