1. NRL RESEARCH FROM TPARC/TCS-08 C. Reynolds, J. Doyle, R. Langland, J. Goerss, J. McLay and E. Serra Marine Meteorology Division, Naval Research Laboratory, Monterey, CA A. Snyder and Z. Pu, University of Utah, Salt Lake City, Utah C. Velden and H. Berger, CIMSS, University of Wisconsin, Madison, WI. Thanks Very Much to JMA and OPRF OUTLINE: Real-time Products Nuri Data Denial Experiments NOGAPS Ensembles Experiments COAMPS Adjoint Experiments

2. NRL REAL-TIME TARGETED OBSERVING PRODUCTS T-PARC/TCS-08: Observe TCs and their environment from genesis to extratropical transition. Aug-Oct 2008; 9 nations; 4 aircraft (lidar, Eldora radar, dropsondes), driftsondes, rapid-scan satellite obs, off-time radiosondes, buoys. Targeted Observing Objective: Take additional observations in regions where they are most likely to improve forecasts Ensemble-based and adjoint-based guidance provided from operational, research, and academic centers around the world NRL real-time products: - Navy Operational Global Atmospheric Prediction System (NOGAPS) Singular Vectors and Ensembles - Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS ® ) Forecasts and Adjoint sensitivity. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint COAMPS ® is a registered trademark of NRL

3. NOGAPS SVS FOR TARGETING SVs (shading): Fastest growing (linear) perturbation to a given forecast 500-hPa streamlines (blue) help relate sensitivity to steering dynamics SVs related to TC dynamics. Associated with weakness in the ridge on the north side of storm and peripheral high to southeast of storm. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint Jangmi 2008092800

4. Strongest sensitivity often to low- and mid-level  and q v. C130 often sampled key portions of the sensitivity. More details on COAMPS sensitivity later in talk. 24-h adjoint sensitivity 36-h lead time Valid at 12Z 10 Sep 2008 2-km vorticity sensitivityTotal energy sensitivity C130 Flight Track C130 Flight Track Dropsondes Best Track COAMPS Real-Time Adjoint Typhoon Sinlaku Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

5. DROPSONDE and DRIFTSONDE OBS: September 2008 TPARC/TCS08 Observations Source: Fleet Numerical Meteorology and Oceanography Center. NOAA Hurricane Observations DOTSTAR Falcon C130, P3 Driftsondes Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint Evaluate impact of additional observations, including Atmospheric Motion Vectors (AMVs), and Dropsondes.

6. Recently (Sept. 2009) the operational global DA system has been upgraded from 3DVAR (NAVDAS) to 4DVAR (NAVDAS-AR). Performing data denial experiments with and without atmospheric motion vectors (AMVS) and dropsondes with NOGAPS and NAVDAS-AR Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint NURI DATA DENIAL EXPERIMENTS Atmospheric motion vectors (AMVs) improve general forecast skill measures, but no systematic improvement on TC track. No systematic improvement from Dropsondes. Ensemble results (shown later) also indicate erroneous recurvature is a robust feature in the forecast. All data No AMV No AMV No Drop 2008081900 All data No AMV No AMV No Drop 2008081912

7. Verifying Analysis With Drops No AMVs With Drops With AMVs No Drops No AMVs Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint 00 hr: NURI south of subtropical high. Black contours: Forecast TC Blue Contours: SV sensitivity Red contours: Analyzed TC (850-vort).

8. Verifying Analysis With Drops No AMVs With Drops With AMVs No Drops No AMVs Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint 24h: Anticylone weaker in forecast than in analysis. Initial sensitivity highlights region between TC and anticyclone

9. Verifying Analysis With Drops No AMVs With Drops With AMVs No Drops No AMVs Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint 48h: Anticylone weaker in forecast than in analysis. Forecast storm moves westward too slowly. Evolved sensitivity shows east-west dipole.

10. Verifying Analysis With Drops No AMVs With Drops With AMVs No Drops No AMVs Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint 72h: Anticylone weaker in forecast than in analysis. Forecast storm moves westward too slowly. Evolved sensitivity shows east-west dipole.

11. Verifying Analysis With Drops No AMVs With Drops With AMVs No Drops No AMVs Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint 96h: Anticylone weaker in forecast than in analysis. Forecast storm starting to move northward.

12. Verifying Analysis With Drops No AMVs With Drops With AMVs No Drops No AMVs Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint 120: Forecast storm moves northward. Trough deeper in forecasts.

13. SVs indicate larger potential growth for Sinlaku and Jangmi than for Nuri. Sinlaku and Jangmi data denial experiments underway. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint SV AMPLIFICATION FACTORS FOR TAIWAN REGION

14. Forecasts NOGAPS global ensemble forecasts during August-September 2008 in West Pacific Basin 32 ensemble members plus control at 00Z, T119L30 CTL: Ensemble Transform (ET, McLay et al 2008) initial perturbations STO: Stochastic convection perturbations (Teixeira and Reynolds 2008, Reynolds et al 2008) and ET perturbations Forecast tracks compared to the Joint Typhoon Warning Center (JTWC) warning data Objectives Qualitative assessment of ensemble’s ability to capture TC genesis Assess spread-skill relationship for ensemble mean TC track errors Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint NOGAPS ENSEMBLE FORECASTS

15. TRACKING METHOD AND GENESIS CRITERIA Each ensemble forecast was tracked manually on a plan-view map, both before and after genesis. Three 850-hPa variables used for tracking: geopotential height, vorticity, and wind vectors. Using all three variables best represents the features of the storm and helps identify weaker systems. Genesis Criteria (examined analysis when system was declared TD) -Multiple closed height lines (at 10 m interval) within 5 o of center -Closed circulation in the wind field -Vorticity greater than 1 x 10-4 s-1 If only one or two of the above criteria were met, the system was labeled “vortex-like” Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

16. EXAMPLE: JANGMI 66-h BEFORE TROPICAL DEPRESSION “X” marks spot where ensemble disturbance becomes TD. Purple: Ensemble tracks of feature starting 66-h before TD. Black: Observed track after TD. For pre-genesis cases, black and purple lines will not start at same point. Light Blue: Member 0 – No Initial Perturbation Dashed Red: Ensemble mean Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

17. 14 Aug. 00UTC (72h before TD) Reasonable tracks, but no TD forecasts Reasonable tracks, more spread, some TDs. 19 Aug. 00UTC (48h after TD) Small spread, all recurve. More spread, but all still recurve. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint CTL (LEFT) and STOC (RIGHT) for NURI Similar to the data denial experiments, all ensembles recurve for Nuri.

18. 21 Sept. 00UTC (66h before TD) Reasonable tracks, but no TD forecasts. Reasonable tracks, more spread, TDs. 26 Sept. 00UTC (54h after TD) Small spread, few recurve. More spread, more members recurve. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint CTL (LEFT) and STOC (RIGHT) for JANGMI In contrast to Nuri, NOGAPS error for Jangmi is to NOT recurve storm. In ensembles, some members do recurve.

19. PREDICTION OF GENSIS (%) Lead Time GenesisGenesis + vortex Non dev -3 d142476 -2 d144654 -1 d315941 Lead Time GenesisGenesis + vortex Non dev -3 d263466 -2 d334852 -1 d506733 % of forecasts predicting genesis increases as lead time decreases % of forecasts predicting genesis is higher when stochastic convection included % of forecasts predicting genesis is smaller for the non-developing cases than for the TC cases Number of cases too small for probabilistic verification. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint 5 TC Cases 2 Non-developing cases (TCS03 and TCS017, 4-day periods) GenesisGenesis + vortex Non dev 12080 GenesisGenesis + vortex Non dev 92575

20. ENSEMBLE MEAN VS SPREAD (1-5 DAY AVERAGE) R 2 =0.443 R 2 =0.545 R 2 =0.525R 2 =0.796 Mean Error (km) Standard Deviation (km) Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint Inclusion of Stochastic Convection improves spread-skill relationship

21. NOGAPS ENSEMBLE MEAN TRACK ERROR: 2008 NH SEASON 331258200 Number of Forecasts 149105 T159 9-member ensembles have lower mean track error than T119 17-member ensembles. T239 experiments and experiments with model uncertainty underway. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

22. NOGAPS 2009 Western North Pacific (17W-23W) 9/24 to 11/1: Homogeneous TC Forecast Error (nm) 978371 Number of Forecasts 5547 4DVAR 3DVAR Consensus JTWC 4DVAR better than 3DVAR at 2-5 days Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

23. Tropical Cyclogenesis Theories Multi-scale aspects (waves, monsoon dynamics, gyres, ET lows, troughs) “Bottom up” and “Top down” development theories  Vortical hot towers & upscale growth (Hendricks et al. 2004; Montgomery et al. 2006…)  Mid-level MCVs, thermodynamics (Ritchie & Holland 1997; Bister and Emanuel 1997…) Predictability of TC Genesis has Yet to be Quantified “No facet of the study of tropical cyclones have proven more vexing than understanding and predicting their genesis”. (Emanuel 2003) COAMPS ADJOINT: TROPICAL CYCLONGENESIS Goals Quantify TC genesis forecast sensitivity characteristics using an adjoint system. THORPEX Pacific Asian Regional Campaign (T-PARC) Tropical Cyclone Structure ‘08 (TCS08) Provided real-time targeting guidance for genesis using moist COAMPS adjoint. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

24. COAMPS ® Moist Adjoint Model Setup Dynamics: nonhydrostatic, nested Physics: PBL, surface fluxes, microphysics (no ice), Kuo Response Function, J: kinetic energy in a box (lowest 1 km) (tested w, , p, TE..) Resolution:  x=40 km, 18 h (18 h lead time)  x=40 km / 13 km, 9 h (27 h lead time) COAMPS Adjoint Model Lead Time Adjoint Model Tangent Linear Model Nonlinear Forward Model Adjoint Model Test TL Approx. Using Optimal Perturbations ~1 m s -1, ~1 K correlations 0.5-0.9 Adjoint allows for the mathematically rigorous calculation of forecast sensitivity of a response function to changes in initial state Sensitivity of response function (J) at time t n to the state at time t 0 COAMPS ® is a registered trademark of NRL Adjoint Optimal Perturbations Basic State titi tftf Nonlinear Forward Model Adjoint allows for the mathematically rigorous calculation of forecast sensitivity of a response function to changes in initial state Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

25. TCS??: Experts Predict “Non-Developer” Which of These Will Develop? 2 Events from T-PARC/TCS08 08Z 18 Aug 2008 Typhoon Nuri ~75 kts 03Z 3 Sep 2008 TCS??: Experts Predict “Developer” TCS025 ~30 kts Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

26. Nuri Sensitivity 0600 UTC 17 Aug 2008 (18-h lead time),  x=40 km 850 hPa  J/  q v and winds 850 hPa  J/  u and streamlines U and V sensitivity large near the storm center (increase  ) and along ridge axis and inverted trough. Largest sensitivity to low- and mid- level  and q v near the storm. 850 hPa  J/  and winds WE L 1009 Response Function (KE) Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

27. Deep layer of water vapor sensitivity near the storm center. Vorticity sensitivity is a maximum in the low-levels. Nuri Sensitivity: Vertical Structure 0600 UTC 17 Aug 2008 (18-h lead time),  x=40 km vorticity sensitivitywater vapor sensitivity trajectory water vapor & sensitivity trajectory vorticity & sensitivity WEWE 56x10 -5 s -1 + Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

28. Evolved Perturbations (Nuri) 0000 UTC 18 Aug 2008,  x=13.3 km 31 m s -1 -12 hPa Evolved Perturbations (in TLM) (9 h) 10-m Wind Perturbations Pressure Perturbations (and SLP) Extreme perturbation growth of 30x over 9 h; coincides with cyclone. Opposite sign perturbations kill Nuri. Rapid perturbation growth highlights difficulty in genesis/intensity forecasting Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

29. SUMMARY Real-time global and mesoscale products produced in support of targeting objective. Data denial experiments for Nuri show little systematic impact on track skill. Ensembles and SVs hint at larger initial sensitivity for Sinlaku and Jangmi. Data denial experiments planned for Sinlaku and Jangmi. Ensembles show increased detection of genesis and better spread-skill with inclusion of model uncertainty. Preliminary results indicate improved TC track forecasts from 4DVAR over 3DVAR. COAMPS Adjoint highlights strong sensitivity to temperature and moisture fields, and rapid perturbation growth. Data denial experiments planned. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

30. Questions?

31. Jangmi: 26 Sept. 00UTC (54h after TD) Small spread, few recurve. More spread, more members recurve. Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint CTL (LEFT) and STOC (RIGHT) for SINLAKU AND JANGMI Sinlaku: 10 Sept. 00UTC (36h after TD) Most tracks miss Taiwan landfall. Taiwan landfall more probable.

32. NOGAPS SVs: 5 Fixed Regions, Twice Daily T79L30 adjoint/TLM resolution T239L30 (operational) trajectory Dry Total Energy norm 1 2 3 45 Details: 48-h lead-time off 00Z run (available 09 UTC, 39-h prior to target time) 60-h lead-time off 12Z run (available 21 UTC, 51-h prior to target time) 48-h opt times for all regions except 72-h opt time for North Pacific Region During high-interest periods: 24-h lead time and 36-h lead time products Flow-dependent verification regions 1)Centered on Guam 2)Storms affecting Taiwan 3)Storms affecting Japan 4)ET Region 5)Central North Pacific

33. NOGAPS SVs for Jangmi (2008092800) Sensitivity dominated by wind field WIND COMPONENTTEMPERATURE COMPONENT Sensitivity to wind field max at 500-hPa Sensitivity to temp field max in mid and upper troposphere.

34. NOGAPS SVs for Jangmi (2008092800) Sensitivity dominated by wind field 500-hPa Vorticity 500-hPa Temperature Elongated vorticity structures extend to southeast and northwest of storm Temperature dipole about storm center

35. Final Time Initial Time NOGAPS SVS and COAMPS Sensitivity (shaded) with SLP (contour) for TC Fitow (Sep 6-8 ‘07) 48-h Forecast NOGAPS COAMPS NOGAPS and COAMPS sensitivity similar for coarse-resolution, dry simulations Complete (microphysics) adjoint of COAMPS provides unprecedented opportunities to study small spatial scales and short time scales Reynolds, Doyle NOGAPS/COAMPS COMPARISON

36.  KE/  q v 500 m  KE/  q v  KE/  Low-Level Moisture Sensitivity Mid-Level  Sensitivity  e Perturbation WEWEWE Low-Level  e Maximum (Destabilization) Preferred regions of large sensitivity to low-level moisture and . Low-level  e optimal perturbations: destabilize & saturate core. 36-h KE sensitivity to 18-h state COAMPS TC Sensitivity COAMPS Adjoint sensitivity of TC Fitow during early development stage highlights strong sensitivity to thermal and moisture fields Difficulty in predicting intensity may be reflected in rapid perturbation growth. Doyle COAMPS TC SENSITIVITY

37. Sinlaku 7 Sept. 00UTC (36h before TD) 10 Sept. 00UTC (36h after TD) Erratic tracks, little development Tracks still erratic, more spread. Most tracks miss Taiwan landfall. Taiwan landfall more probable.

38. SUMMARY: Targeted Observing Products NOGAPS SVs for real-time targeted observing guidance: Five fixed region SVs provided twice daily Having many products available proved useful. Discussions led to targeting consensus. Often possible to relate position of sensitivity to general dynamic understanding of steering mechanisms For current configuration, sensitivity to wind field stronger than sensitivity to temperature field Data Denial Experiments Ongoing. Not much impact for Nuri. COAMPS Adjoint sensitivity for Developing and Mature Storms 24-h optimization time with variety of lead times Storm-centered Verification Regions Fine-scale products complementary to large-scale SVs Rapid perturbation growth associated with moist processes

39. SUMMARY: Ensemble Products NOGAPS ensemble products (time-longitude diagrams), useful for downstream impact problem Real-time 55-km 8-member Ensemble Tests ongoing Tests for 2008 season indicate Impact of initial perturbation formulation relatively small Impact of resolution significant

40. NOGAPS ET Ensemble 200-hPa V: black contours- control; shading – ens. spread, 35-60N Squares shows longitude of TC Sinlaku NOGAPS ET Ensembles with Stochastic Convection (T119L30, 32-member + control, 240 h, once daily) NOGAPS Ensemble Products Time-longitude diagrams for depicting energy propagation, forecast uncertainty Large ensemble spread downstream from Sinlaku indicating uncertainty in ET

41. NOGAPS ET Ensemble 200-hPa V ensemble spread normalized by September average Squares shows longitude of TC Sinlaku NOGAPS Ensemble Products Large anomalous ensemble spread downstream from Sinlaku indicating uncertainty in ET NOGAPS ET Ensembles (T119L30, 32-member + control, 240 h, once daily)

42. NOGAPS ET Ensemble 500-hPa Z: black contours- control; shading – ens. spread, 35-60N Arrow shows longitude of North Pacific SVs. NOGAPS Ensemble Products Time-longitude diagrams useful for depicting energy propagation, forecast uncertainty. Arrow indicates location of North Pacific SVs.

43. Initial SVs After 24h After 48h Final SVs Evolution of NOGAPS North Pacific 72-h SVs from 2008092600 Illustrates rapid downstream propagation Useful for winter TPARC? Try 96-h SVs? DOWNSTREAM PROPAGATION OF SIGNAL

44. Real-time 8-member 55-km NOGAPS Ensemble tests for 2009

45. James D. Doyle Clark Amerault, Carolyn Reynolds, Hao Jin, Jon Moskaitis 1 Naval Research Laboratory, Monterey, CA 1 National Research Council, Monterey, CA James D. Doyle Clark Amerault, Carolyn Reynolds, Hao Jin, Jon Moskaitis 1 Naval Research Laboratory, Monterey, CA 1 National Research Council, Monterey, CA Acknowledgements: ONR, TCS08 Team Aspects of Tropical Cyclogenesis Predictability during TCS08 Typhoon Saomai (08W) and Tropical Storm Bopha (10W) 02Z 8 Aug 2006 (NASA MODIS)

46. Vertically-integrated total energy, C130 Track and Drops COAMPS 24-h Adjoint sensitivity calculated for storms of interest Complete (microphysics) adjoint of COAMPS provides unprecedented opportunities to study small spatial scales and short time scales COAMPS ADJOINT SENSITIVITY FOR TARGETING SINLAKU, 2008091012 800-hPa Vorticity Sensitivity with background wind and height Real-time Products Nuri Data Denial NOGAPS Ensembles COAMPS Adjoint

47. Accuracy of Tangent Linear Approximation U’ field [18-h integration (  x=40 km)] Adjoint Accuracy Excellent agreement between perturbations evolved in the nonlinear and tangent linear models (dry & moist) for  x= 40 km. Dry NonlinearTangent Linear Moist

48. Accuracy of Tangent Linear Approximation U’ field [9-h integration (  x=13.3 km)] Nonlinear Tangent Linear Excellent agreement between perturbations evolved in the nonlinear and tangent linear models with moisture for  x= 13 km. Adjoint Accuracy

49. P3 LIDAR Winds at 500 m Nuri Sensitivity 1500 UTC 17 Aug 2008 (27-h lead time),  x=13.3 km 500-m  J/  q v 500-m  J/  Strongest sensitivity on NE flank (strongest winds – observed and simulated). Spiral bands of q v and  sensitivities similar to stochastic optimals for swirling flows (e.g., Nolan and Farrell 1999). VHTs may have a bigger impact in these sensitive regions. Nuri D. Emmitt 500-m Winds (m s -1 )

50. TCS025 Sensitivity 0600 UTC 28 Aug 2008 (18-h lead time),  x=40 km U and V sensitivity large near the storm center and upstream along 850- hPa trough (  J/  large). Largest sensitivity to low- and mid- level  and q v near the storm, maxima along flanks of the trough. 850 hPa  J/  u and SLP 850 hPa  J/  q v and winds 850 hPa  J/  and winds NW SE TCS025 850 hPa  J/  u and streamlines

51. vorticity sensitivity TCS025 Sensitivity: Vertical Structure 0600 UTC 28 Aug 2008 (18-h lead time),  x=40 km Water vapor sensitivity is largest in the low-levels. Vorticity sensitivity is a maximum in the mid-levels and to the SE of the vortex (less shear?). water vapor sensitivity trajectory water vapor & sensitivity trajectory vorticity & sensitivity 51x10 -5 s -1 + NWSENWSE TCS025

52. TSC025 Verification 0300 UTC 29 Aug 2008 (27-h lead time),  x=13.3 km 500-m Winds (m s -1 ) GM6 (0030 UTC 29 Aug) ASCAT (2300 UTC 28 Aug) 25 kts TCS025

53. TCS025 Sensitivity 0300 UTC 29 Aug 2008 (27-h lead time),  x=13.3 km 500-m winds 500-m  J/  q v 500-m PV’ Prominent asymmetry in the low-level winds (agrees w/ QuikSCAT). Large sensitivity to water vapor and PV along low-level jet. TCS025

54. Evolved Perturbations (TCS025) 1200 UTC 29 Aug 2008 (27-h lead time),  x=13.3 km 15 m s -1 -5 hPa Evolved Perturbations (in TLM) (9 h) 10-m Wind Perturbations Pressure Perturbations (and SLP) Rapid growth (15x growth from initial perturbations of 1 K, 1 m s -1 ) in 9 h. Perturbation growth is confined to small areas, SE of the broad low center. Slower growth than Nuri. L L TCS025

55. Initial Perturbation Domain Average of Adjoint Optimal Perturbations Evolved in TLM Final Perturbation (18 h) Upper-Level Max. Consistent With Background Deep Response Function Box is Consistent Perturbation Characteristics 16 Cases (13 Storms)  x=40 km Initial total energy maximum in low-levels. Deep perturbation growth throughout troposphere. Non-developers show weakest growth. Height (m) Summary

56. Moist adjoint was successfully applied (real time) during T-PARC/TCS08 -Targeted observations and forecasting -Dropsondes from P3 & C130 often sampled sensitive regions Moist adjoint provides physically meaningful sensitivities. -Characteristics of both “bottom-up” and “top-down” development -Moisture and temperature show greater sensitivity than winds -Preferred locations of deep convection for intensification Significant differences in adjoint sensitivities for 2 genesis cases -Nuri:convectively dominated, vorticity sensitivity bands -TCS025:multi-scale aspects, baroclinic signatures Challenges for TC genesis predictability. -Convection introduces inherent uncertainty - motivates need for ensembles -Rapid growth rates: 50% cases show > 10x growth 18 h -1 for 500-m winds Conclusions Summary

57. Evolved Perturbations TCS025: 0000 UTC 29 Aug 2008 (18-h lead time),  x=40 km Nuri: 0000 UTC 18 Aug 2008 (18-h lead time),  x=40 km 14 m s -1 9 m s -1 Both cases exhibit fairly rapid adjoint perturbation growth. Faster p’ growth for Nuri (-5 h Pa) vs. TCS025 (-3 h Pa) leading to more organized development. TCS025 Evolved Perturbation Pressure (in TLM) and SLP (18 h) Nuri

58. Response Function Sensitivity 0600 UTC 17 Aug 2008 (18-h lead time),  x=40 km KE in lowest 1 km  in lowest 1 km w in lowest 10 km Response functions evaluated for KE, TE, , w, u over various depths The KE, TE,  response function results yield similar sensitivities. Vertical velocity averaged over the troposphere produces a similar pattern, but weaker sensitivity.

59. Sinlaku 7 Sept. 00UTC (36h before TD) 10 Sept. 00UTC (36h after TD) Erratic tracks, little development Tracks still erratic, more spread. Most tracks miss Taiwan landfall. Taiwan landfall more probable.