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

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

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

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

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

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

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

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

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

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

Results: Probabilistic dynamics (source of spread) 2-km PV contoured 1-h QPF shaded Ensemble mean @16Z/12 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!!!!

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!!!!

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

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

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)

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

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)

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

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