1. On the Relative Benefits of Multi-Center Grand Ensemble for Tropical Cyclone Track Prediction in the Western North Pacific 2 Nov 2012 (Fri) The Fourth THORPEX Asian Science Workshop Kunming, China Munehiko Yamaguchi 1, Tetsuo Nakazawa 1,2 and Shunsuke Hoshino 1 1: Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan 2: World Meteorological Organization, Geneva, Switzerland 山口 宗彦

2. Background Past Present Future Research Phase TIGGE (started 2006) TIGGE CXML (started 2008) Goal: Enhanced use of ensemble prediction for operational purposes Operational Phase Various projects to demonstrate the value of ensemble prediction have been conducted. Example 1. A WWRP-RDP “North Western Pacific Tropical Cyclone (TC) Ensemble Forecast Project (NWP-TCEFP)” Example 2. Severe Weather Forecast Demonstration Project (SWFDP)

3. NWP-TCEFP Homepage URL: http://tparc.mri-jma.go.jp/cyclone/ Main Page Send e-mail to thorpex@mri-jma.go.jp to get ID and passwordthorpex@mri-jma.go.jp The Meteorological Research Institute of the Japan Meteorological Agency (MRI/JMA) created a website for the NWP-TCEFP.

4. Ensemble TC track forecast -MCGE- Track forecasts from CMA (ensemble size is 15), ECMWF (51), JMA Typhoon EPS (11), JMA One-week EPS (51), KMA (17), MSC (21), NCEP (21), STI (9) and UKMO (24) are available as of December 2011. Ensemble TC track forecast by all ensemble member of all centers. (Ensemble TC track forecast by each center is also available)

5. Ensemble TC track forecast -Each Center-

6. Multi-Center Grand Ensemble Past Research Phase TIGGE (started 2006) TIGGE CXML (started 2008) TIGGE has made it possible to construct a new ensemble, that is Multi-Center Grand Ensemble MCGE EPS at ECMWF EPS at CMA EPS at JMA EPS at KMA EPS at XXX Relative benefits of MCGE over single model ensemble (SME), that is ensemble prediction system (EPS) at each NWP center, have yet to be verified from a perspective of typhoon track predictions. MCGE is an ensemble of ensembles of major NWP centers.

7. SME vs MCGE Using the TIGGE dataset, the relative benefits of MCGE over SME are investigated from both deterministic and probabilistic perspectives. Verified TCs: 58 TCs in the western North Pacific basin from 2008 to 2010 Verified items: 1.TC strike probability (verification of probabilistic forecasting) 2.Confidence information (verification of ensemble spread) 3.Ensemble mean track prediction (verification of deterministic forecasting)

8. TIGGE providers Verifications are conducted for the forecasts initiated at 00 and 12 UTC only. Meteo France is eliminated from the verifications since their forecasts are initiated at 06 and 18 UTC only. NCEP’s forecasts initiated at 06 and 18UTC are not included in the verifications. The verification results of BOM is based on the data in 2008 and 2009 only because the data in 2010 is not available. The number of verification samples of JMA is smaller because JMA’s forecasts are initiated at 12 UTC only.

9. TC strike probability Original idea by Van der Grijn (2002, ECMWF Tech. Memo): “A forecaster is often more interested in whether a TC will affect a certain area than when that TC will hit a specific location.” He defined the strike probability as “the probability that a TC will pass within a 65 nm radius from a given location at anytime during the next 120 hours”. Example -TC strike probability map- It allows the user to make a quick assessment of the high- risk areas regardless of the exact timing of the event. The strike probability is based on the number of members that predict the event with each member having an equal weight.

10. Verification result of TC strike probability -1- Strike prob. is computed at every 1 deg. over the responsibility area of RSMC Tokyo - Typhoon Center (0 ∘ -60 ∘ N, 100 ∘ E-180 ∘ ) based on the same definition as Van der Grijn (2002). Then the reliability of the probabilistic forecasts is verified. Reliability Diagram -Verification for ECMWF EPS- In an ideal system, the red line is equal to a line with a slope of 1 (black dot line). The number of samples (grid points) predicting the event is shown by dashed blue boxes, and the number of samples that the event actually happened is shown by dashed green boxes, corresponding to y axis on the right.

11. Verification result of TC strike probability -2- All SMEs are over-confident (forecasted probability is larger than observed frequency), especially in the high-probability range.

12. Benefit of MCGE over SME -1- Combine 3 SMEs Reliability is improved, especially in the high-probability range. MCGE reduces the missing area (see green dash box at a probability of 0 %).

13. Best SME (ECMWF) MCGE-3 (ECMWF+JMA+UKMO) MCGE-6 (CMA+CMC+ECMWF+JMA+NCEP+UKMO) MCGE-9 (All 9 SMEs) Benefit of MCGE over SME -2- MCGEs reduce the missing area! The area is reduced by about 1/10 compared with the best SME. Thus the MCGEs would be more beneficial than the SMEs for those who need to avert missing TCs and/or assume the worst-case scenario.

14. Verification of ensemble spread Verification at 3 day predictions x axis: ensemble spread y axis: position error of ensemble mean track prediction Verification at 3 day predictions x axis: ensemble spread y axis: position error of ensemble mean track prediction

15. Verification of confidence information Position errors (km) of 1 to 5 day ensemble mean TC track predictions with small (blue), medium (orange) and large (red) ensemble spread. Each color has five filled bars, corresponding to the position errors of 1 to 5 day predictions from left to right. If a SME is successful in extracting the TC track confidence information, the average position error of small-spread cases would be smaller than that of medium-spread cases, and the average position error of large-spread cases would be larger than that of medium-spread cases. The frequency of each category is set to 40%, 40% and 20%, respectively (Yamaguchi et al. 2009).

16. Relative benefit of MCFE over SME 3 days 4 days 5 days Position errors (km) of the ECMWF EPS (blue) and the MCGE-3 (green) for extremely small ensemble spread cases (20% of the total number of prediction cases). The position error of MCGE is generally smaller than that of the best SME, indicating that when multiple SMEs simultaneously predict the low uncertainty, the confidence level increases and a chance to have a large position error decreases.

17. Verification of ensemble mean track prediction Position errors (km) of 1- to 5-day TC track predictions by the unperturbed control member (unfilled bars) and ensemble mean (filled bars) of each SME. The circle (hyphen) mark means that the difference in the errors between the control member and ensemble mean is (not) statistically significant at the 95 % significance level. The ensemble mean has better performance than the control prediction in general and the improvement rate is relatively large for the longer prediction times.

18. Position errors (km) of 1- to 5-day TC track predictions by the ensemble mean (EC MEAN) and high-resolution deterministic model of ECMWF (EC DTM) and the ensemble mean of the MCGEs and MCEs. Verification of ensemble mean track prediction The position error of 5-day predictions by the MCGE-3 is slightly smaller than that of the ensemble mean of the best SME, which is the ECMWF EPS, though the difference is not statistically significant at the 90 % significance level. The TC track prediction by the high-resolution deterministic model of ECMWF has better performance than the ensemble mean of the ECMWF EPS up to 3 days.

19. Summarize the points so far… The relative benefits of MCGE over SME are investigated from both deterministic and probabilistic perspectives. 58 TCs in the western North Pacific from 2008 to 2010 are verified. 1.TC strike probability Reliability is improved in MCGE, especially in the high-probability range. MCGE reduces the missing area by about 10 %. 2.Confidence information When multiple SMEs simultaneously predict the low uncertainty, the confidence level increases and a chance to have a large position error decreases. 3.Ensemble mean track prediction The position error of 5-day predictions by the MCGE-3 is slightly smaller than that of the ensemble mean of the best SME though the difference is not statistically significant.

21. Typhoon track prediction by MCGE-9 Good example Bad example There are prediction cases where any SMEs cannot capture the observed track. => It would be of great importance to identify the cause of these events and modify the NWP systems including the EPSs for better probabilistic forecasts. Typhoon Megi initiated at 1200 UTC 25 th Oct. 2010 Observed track Typhoon Conson initiated at 1200 UTC 12 th Jul. 2010

22.  Most of the published studies using TIGGE data are focused on predictability & dynamical processes  Examples:  Prediction of SH cyclones (Froude, 2011)  Prediction of MJO (Matsueda & Endo, 2011)  Impact of ET transition of cyclones (Keller et al, 2011)  Planned paper reviewing TIGGE research, in conjunction with PDP WG. A range of papers based on TIGGE forecasts have demonstrated benefits of multi-model grand ensembles, in particular for surface air temperature, rainfall, tropical cyclone tracks, e.g.: –Matsueda & Tanaka (2008), Johnson & Swinbank (2009), Yamaguchi (2012) Other papers have also compared multi-model ensembles with use of reforecasts to correct systematic errors – with similar benefits, e.g.: –Hagedorn et al (2012), Hamill (2012)

23. ECMWF (50 members) Sinlaku initiated at 12UTC 10 Sep. 2008 Dolphin initiated at 00UTC 13 Dec. 2008 Some contradictions of ensemble spread among EPSs Japan Philippines Taiwan NCEP (20 members) Black line: Best track Grey lines: Ensemble member

25. What controls the ensemble spread of tracks? 1.Methods: Different methods of creating initial perturbations may control it, resulting in different growth of the perturbations. 2.Amplitudes: Initial amplitudes of the perturbations may affect the size of the ensemble spread, especially in the early forecast stage. ECMWFNCEPJMA MethodSV methodEnsemble Transform SV method Amplitudes are determined in each NWP center in a statistical way

26. Best track and intensity of Typhoon Sinlaku by RSMC Tokyo-Typhoon Center Best track Intensity MSLP (hPa)

27. Vertical profile of perturbation Kinetic Energy Perturbation kinetic energy at each vertical level is averaged over the all ensemble members over a 2000 km x 2000 km domain centered on Sinlaku JMA ECMWF NCEP

28. 20072008 1 day forecasts 3 day forecasts Relationship of spread of ensemble track forecasts between ECMWF and NCEP

29. Baroclinic energy conversion within a vortex The dynamics of the baroclinic instability in the mid-latitude waves can be applied to a TC-like vortex in a cylindrical coordinate system. Certain latitude North pole Mid-latitude Certain radius TC center Tropical cyclone Streamfunction perturbation Temperature perturbation C C W W

30. ECMWF ensemble member 21 before recurvature Shade: Streamfunction perturbation Contour: Temperature perturbation ECMWF ensemble member 21 (500hPa) Schematic L L H +T -T

31. Wind perturbation at 500 hPa in storm relative coordinate Radial heat flux at r=500km T = 0 hour (initial time) 3000 km N W E S NWESN

32. Wind perturbation at 500 hPa in storm relative coordinate Radial heat flux at r=500km T = 6 hours 3000 km N W E S NWESN

33. Wind perturbation at 500 hPa in storm relative coordinate Radial heat flux at r=500km T = 12 hours 3000 km N W E S NWESN

34. Wind perturbation at 500 hPa in storm relative coordinate Radial heat flux at r=500km T = 18 hours 3000 km N W E S NWESN

35. 1. Perturbation growth as well as the initial amplitude of the perturbations control the spread of ensemble track forecasts. 2. Baroclinic energy conversion plays a role in the modification of the track Summary Though the ECMWF initial perturbation amplitudes are small, the growth of the perturbations helps to obtain an appropriately large ensemble spread of tracks. Meanwhile, the relatively large amplitudes of initial perturbations seem to play a role in obtaining the ensemble spread of tracks in NCEP.

37. Thank you for your attention

38.  Most of the published studies using TIGGE data are focused on predictability & dynamical processes  Examples:  Prediction of SH cyclones (Froude, 2011)  Prediction of MJO (Matsueda & Endo, 2011)  Impact of ET transition of cyclones (Keller et al, 2011)  Planned paper reviewing TIGGE research, in conjunction with PDP WG.

40. Recent studies by Hamill et al. (2011a, 2011b, MWR) ECMWF vs EnKF + GFS