1. Dynamics and Predictability of Hurricane Humberto Jason Sippel and Fuqing Zhang Texas A&M / Penn. State Contributor: Yonghui Weng, TAMU

2. Intro: Motivation
Understand origin of intensity forecast error (lack of improvement)
Contribute to understanding of tropical cyclone formation and intensification
Follow-up Sippel and Zhang (2008) study of non-developing gulf low

3. Intro: Hurricane Humberto
Near-shore rapid formation, intensification
TD at 09Z 9/12
Landfall, CAT1+ at 07Z 9/13
Poorly forecasted
“BASED ON OPERATIONAL ESTIMATES… NO TROPICAL CYCLONE IN THE HISTORICAL RECORD HAS EVER REACHED THIS INTENSITY AT A FASTER RATE NEAR LANDFALL. IT WOULD BE NICE TO KNOW...SOMEDAY...WHY THIS HAPPENED.”
- James Franklin, NHC

4. Methodology: Using ensembles
Ensembles provide uncertainty estimates
Ensembles improve position/intensity forecasts for TCs
Ensembles can be used to investigate dynamics in a probabilistic sense (Hawblitzel et al 2007)
Deterministic problems well recognized
Increasing awareness of probabilistic need.
This applies for tropical cyclones as well.
Both multimodel and single-model ensembles have shown good results

5. Methodology: Ensemble correlation
Correlation between A and B denoted by (A : B)
Does not imply causality, but can be used to investigate relationships

6. Methodology: WRF/EnKF configuration
Forecast model: WRF 40.5/13.5/4.5 km with WSM 6-class microphysics and YSU PBL
ICs/BCs: NCEP FNL analysis
30-member ensemble initiated with random large-scale perturbations using MM5 3Dvar background error statistics (Barker et al. 2004)
WRF/EnKF (Meng and Zhang 2008a,b; Zhang et al 2008) assimilates hourly Vr from CRP, HGX, and LCH WSR-88D radars from 09Z/12 until 18Z/12 after a 9-h ensemble forecast

7. Results: Initial analysis
Local environment favorable
Low shear
Deep moisture
High Instability
Regional environment less so
Dry air through mid-troposphere poleward of surface front just north of genesis region
Dry 700-hPa to south over GOM
09Z/12 analysis

8. Results: Ensemble performance
No assimilation of Vr
Results: Ensemble performance
All members forecast genesis
Strength from Cat 2 to a weak TS
‘Worst’ member better than operational forecasts
Clearly DA helps, so this is largely an IC problem
Large spread highlights uncertainty
WRF/EnKF Vr assimilation
Forecast

9. Results: Ensemble performance
Select members, simulated reflectivity at landfall
Mem 1, 15Z/13
Mem 10, 09Z/13
Mem 16, 06Z/13
Mem 19, 06Z/13
KHGX reflectivity at landfall
984 hPa
07Z/13
992 hPa
993 hPa
Storm size and core structure captured by ensemble
Problems capturing stratiform precip
1000 hPa
1004 hPa

10. Results: Ensemble performance
(or colored) Individual Members
Ensemble mean
Observed track
Storm track roughly spanned by ensemble
Movement too slow, partly because of a left jog in the mean
Stronger storms are further east

11. Results: Probabilistic dynamics (source of spread)
2-km PV contoured
1-h QPF shaded
Ensemble
2-km PV within mean 2-PVU isopleth at 1600 UTC 12 June very highly correlated to SLPf (0.87)
Antecedant 1-h QPF within 6-mm isopleth highly correlated to PV and SLPf
What causes higher QPF?
TELL WHAT IS SHOWN!!!!

12. Results: Probabilistic dynamics (source of spread)
Instability, mid-level moisture, and convection:
(CAPE : QPF) and (q : QPF) significant to strong
Similar results to gulf low (SZ08)
Can we say any more?
TELL WHAT IS SHOWN!!!!

13. Results: Probabilistic dynamics (source of spread)
Frontal Interaction
(Tsfc : SLPf) and (qsfc : SLPf) strong north of frontal boundary
Weaker or further north front favors genesis
With weaker front, less cool/dry air entrains into circulation (different from gulf low)
More easterly storm movement (i.e., weaker westerlies) also results in less interaction
Differing landfall times increase spread further in same sense as interaction with front

14. Results: Probabilistic dynamics (source of spread)
Surface mixing ratio - 21Z/12
Individual members
Mems 1 and 10: stronger, farther east, weaker front
Mems 16 and 19: weaker, farther west, stronger front

15. Results: Probabilistic dynamics (source of spread)
QPF, (QPF : Tsfc)
Weakening downdrafts and transition to mature cyclone (WISHE)
Cold downdrafts present, weaken with time on 12 June
After 00Z/13, statistical relationship between convection and cold pools disappears
After 03Z/13, mature tropical cyclone dynamics active (similar to gulf low)

16. Concluding Remarks
For the first time, the source of spread in tropical cyclone forecasts has been documented
Generally, differing CAPE and deep moisture drive initial spread, which is later amplified by differing fluxes and WISHE
Deep moisture result is consistent with previous literature, but CAPE result is new... is CAPE always an important factor?
Data assimilation can reduce error due to IC deficiencies, but even when DA performs well, uncertainty can remain large
Large uncertainty highlights the need for ensemble forecasts and advanced DA

18. Gulf low: Probabilistic dynamics
CAPE, (CAPE : SLPf)
900 km
Initial correlation length scale very large due to large-scale perturbations
Length scale generally shrinks as convection results in smaller scale differences
0 h
24 h
(Sippel & Zhang 2008, JAS, in press)

20. Humberto: Ensemble results
Member 10 SLP, T - 12Z/13
Observed SLP, T - 06Z/13
Ensemble captures mesoscale interaction of front with cyclonic circulation

22. Gulf low sensitivity: VHTs and cold pools
QRTP1 and QRTP2:
Both have stronger and more numerous VHTs than other simulations
VHTs incrementally contribute to stronger storm-scale circulations
Similar convective line and cold pool forms as in QRT4, cutting off initial primary VHT
3/4
MEM6