1. Munehiko Yamaguchi, Sharanya S. Majumdar (RSMAS/U. Miami) and multiple collaborators 3 rd THORPEX International Science Symposium 14 Sep. 2009 Coordinated use of targeted observations during TCS-08/T-PARC

2. Outline of the talk 1.What we did before TCS-08/T-PARC 2.What we did during TCS-08/T-PARC 3.What we learned from TCS-08/T-PARC I would talk the above 3 from a perspective of targeted observations Pretty Simple…

3. Guideline on targeting products and timelines After the Hawaii meeting in December 2007 (8 months before TCS-08/T-PARC), our targeting group created a guideline on targeting products and timelines because it was found that many institutes could provide the sensitivity analysis guidance and the availability time of each guidance varied from one center to another. (Final version of the guideline was completed in July…) Singular vectors ECMWF NRL (based on NOGAPS) JMA U Yonsei (based on MM5) Adjoint based SAG NTU (ADSSV) NRL (based on COAMPS) EKF UKMO (ETKF based on MOGREPS) U Miami/NCEP (ETKF based on NCEP + ECMWF ensembles) U Washington (EKF based on WRF) Ensemble variance NOAA (based on NCEP ensembles) 10 kinds of SAG in total (many talks and posters about SAG and targeting during TTISS)

4. Website providing sensitivity analysis guidance 1.UCAR/EOL T-PARC/TCS-08 website 2.JMA T-PARC website 3.PREVIE system developed by ECMWF in partnership with UK Met Office. UCAR/EOLJMA ECMWF PREVIEW We mainly used the PREVIEW system for the comparison of the sensitivity analysis guidance

5. PREVIEW System Sensitive Area Predictions (SAPs) Automatic submission of 5 fixed areas Up to 5 additional areas chosen interactively Flexible choice of targeting time (t + 18 to 102 h) and verification time (t + 36 to 120 h)

6. TCDateAircraft Non-typhoon2008082600-20080909006 Falcon missions TY Nuri2008082012DOTSTAR TY Sinlaku2008091000DOTSTAR TY Sinlaku2008091100 - 2008091112DOTSTAR and Falcon TY Sinlaku2008091400Falcon TS Sinlaku2008091600DOTSTAR and Falcon TY Hagupit2008092200DOTSTAR STY Jangmi2008092700DOTSTAR TY Jangmi2008092800 - 2008092812DOTSTAR and Falcon TS Jangmi2008092906-20081001003 Falcon missions JMA activated rapid-scan mode on MTSAT2 satellite between 2008091012- 2008091306 and 2008092712-2008092812. Extra rawinsonde observations. Summary of Missions

7. Targeting Elluminate Session The targeting group hold an 1-hour Targeting Elluminate Session before the Daily Planning Meeting when targeting missions might be expected and mainly discussed about 1.Synopsis based on satellite images and analysis fields by global models such as ECMWF, UKMO, GFS and NOGAPS. 2.Available observation resources 3.Forecast uncertainty using ensemble products such as tracks and spread of Z500. 4.Sensitivity analysis guidance 5.Tentative flight plan

8. Following 3 slides are the slides we really used in the Targeting Elluminate Session starting from 21UTC 9 th September 2008 for a flight mission for Typhoon Sinlaku at 00 UTC 11 th September 2008.

9. JMA Typhoon EPS 2008.09.09 06UTC ini +132h JMA Medium-Range EPS 2008.09.08 012UTC ini +216h EPS track forecasts ECMWF EPS 2008.09.08 12UTC ini +120h GFS EPS 2008.09.09 12UTC ini +???h

10. Sensitivity Guidance: Obs. Time 09/11 00Z ECMWFNOGAPSJMA UM ETKFUK ETKFCOAMPS

11. Joint Flight Plan: 09/11 00Z DOTSTAR (Blue) and Falcon (Red) Flight Plan

12. Canceled Flight: Mission at 09/12 00Z DOTSTAR !!! Canceled !!! The latest email from Wu-san said, “Concerning the weather condition not suitable for a feasible targeting flight, the DOTSTAR mission for 00UTC 12 Sept. has just been called off.” Due to the weather condition, the planned DOTSTAR mission at 00UTC 12 th Sept. was canceled.

13. Sensitivity region not fully covered: Mission at 09/10 00Z ECMWFNOGAPS Obs. Points cover sensitive regions proposed by guidance Area surrounded by black line is also sensitive DOTSTAR Flight Plan JMA Due to the limited aircraft resources, the sensitivity area was not fully covered.

14. Summary 1.The Guideline on targeting products and timelines were helpful in understanding when and what products were available at each meeting help in Monterey, Tokyo, Taiwan, etc. 2.The Websites were useful to compare the various kinds of sensitivity analysis guidance. 3.The targeting group hold the 1-hour Targeting Elluminate Session when targeting mission was expected. 4.We conducted targeting missions for 4 typhoons. 5.Due to the weather condition and limited aircraft resources, we had to be very flexible in the decision-making process.

15. Future Challenges 1.Observing System Experiment (OSE): Evaluate the impact of targeted observations based on sensitivity areas. 2.Understand the role of the additional observations in NWP models. It might happen that some observations lead to the deterioration of model performance. 3.Evaluate fidelity of sensitivity analysis guidance (long targeting and verification time).

16. Recurving Point Uncertain! Comparison of EPS spread of Z500. VT: 09/13 12Z ECMWF EPS Z500 JMA EPS Z500 GFS EPS Z500

17. Enough room to improve TC track forecasts Position error (km) average Position errors of each TC Track Forecast by JMA/GSM in 2007 Forecast time: 72 hours Total number of forecast events: 102 Position errors are sorted in ascending order

18. Various approach to improve forecasts Observation Data assimilation Numerical weather prediction User Current system Reducing the errors of deterministic track forecasts is not only the approach. Providing confidence information based on ensemble forecasts is also one way to improve TC track forecasts. Yamaguchi et. al (2009) developed the Typhoon Ensemble Prediction System (EPS) at the Japan Meteorological Agency and demonstrated that the ensemble spread is an indicator of confidence of TC track forecasts.

19. Ensemble spread of TC positions 2 (ensemble spread accumulated from 0 to 120 hour forecasts every 6 hours) Position Errors of Ensemble Mean at 5-day forecasts (km) Number of sample 1 ： 149 Strong relationship between ensemble spread and position error of ensemble mean track forecasts 1.The TC strength of L is included in this verification 2. Ensemble mean tracks are defined using more than 1 ensemble member Confidence information provided by ensemble forecasts

20. How to bring out forecast uncertainties T=t0 T=t1 Analysis field Deterministic forecast Ensemble member Uncertainty of Analysis field Forecast Uncertainty Dramatis Personae Ensemble spread is a variability (standard deviation) between the members in the ensemble forecast. Ensemble spread can be used as an indicator of confidence of forecast. Ensemble spread is a variability (standard deviation) between the members in the ensemble forecast. Ensemble spread can be used as an indicator of confidence of forecast.

21. ECMWFNCEP Sinlaku initiated at 12UTC 10 Sep. 2008 Dolphin initiated at 00UTC 13 Dec. 2008 Some contradictions as seen among various EPSs The grey lines are ensemble track predictions. The black line is the best track. The black triangles are the forecast positions at 120-h. Japan Philippines Taiwan The recently established The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database makes it possible to conduct a systematic inter-comparison of global model ensembles and investigate the reason why the ensemble spread changes from one EPS to another.

22. Specifications of ECMWF, NCEP and JMA EPS Note that The verifications in this study are based on initial time 0000 UTC unless otherwise noted because the most of the airborne observations were conducted centered on 0000 UTC. Verification results of JMA are based on 1200 UTC because JMA’s EPS is initiated only at 1200 UTC. JMA’s EPS is the Medium-range EPS, not the Typhoon EPS.

23. What I did Calculation procedures: 1.Download u and v of initial ensemble fields at 1000, 925, 850, 700, 500, 300, 250 and 200 hPa through ECMWF’s TIGGE site. 2.Convert the latitude-longitude coordinate into the x-y coordinate centered on the central position of Sinlaku. 3.Calculate ensemble perturbations (u’ and v’) of all ensemble members at each vertical level. 4.Calculate kinetic energy defined as u’^2+v’^2 from the results of 3. 5.Calculate grid- and ensemble-averaged kinetic energy at each vertical level over the 2000km x 2000km domain and draw the vertical profile. 6.Calculate vertically- and ensemble-averaged kinetic energy over the 2000km x 2000km domain and draw the horizontal distribution. I compared the kinetic energy of ensemble initial perturbations for Typhoon Sinlaku, using TIGGE data from ECMWF, NCEP and JMA* (Medium range EPS, not Typhoon EPS). I calculated the following two elements through 10 th to 19 th of September 2008. 1. Vertical profile of the kinetic energy 2. Horizontal distribution of the kinetic energy

24. Best track and intensity of Typhoon Sinlaku

25. Synopsis in a before-recurvature stage 500 hPa250 hPa Geopotential height (solid line) and stream function (dash line) Sinlaku was located west of the Pacific High. The High is not strong enough to interact with the typhoon, so the steering caused by the Pacific High is weak. Sinlaku moved very slowly at that time; less than 10 km h -1.

26. Synopsis in a during-recurvature stage 500 hPa250 hPa Geopotential height (solid line) and stream function (dash line) Sinlaku was located in a confluent area induced by the westerly jet and the southerly flow at the west edge of the Pacific

27. Synopsis in a after-recurvature stage 500 hPa250 hPa Geopotential height (solid line) and stream function (dash line) Sinlaku was sandwiched by both features; it was located north of the Pacific High and south of the westerly jet, being advected by the confluent westerlies.

28. Vertical profile of KE in a before-recurvature stage

29. Note that the scale of NCEP is 10 times as large as that for ECMWF and JMA Vertically averaged horizontal distribution of KE in a before-recurvature stage ECMWFNCEPJMA Storm-relative coordinate with the domain of 2000 km x 2000km 2000km

30. ECMWFNCEPJMA Vertically averaged kinetic energy of ensemble initial perturbations Vertical profile of kinetic energy of ensemble initial perturbations 2000km JMA ECMWF NCEP Comparison of kinetic energy of ensemble initial perturbations for Typhoon Sinlaku (2008) Storm-relative coordinate 2000km 10 Sep. 00Z (before-recurv.) 15 Sep. 00Z (during-recurv.) 18 Sep. 00Z (after-recurv.)

31. Temperature and specific humidity perturbation I did the same verifications for temperature and specific humidity perturbations. Total energy = ½ {(u’ 2 + v’ 2 ) + c p T’T’/T r + L c L c q’ q’/c p /T r } c p is the specific heat of dry air at constant pressure, T’ is a temperature perturbation about the control analysis, and Tr = 300 K is a reference temperature. Similarly, Lc is the latent heat of condensation and q’ is a specific humidity perturbation. ! Kinetic energy ! Available Potential energy ! Specific humidity energy

32. ECMWFNCEPJMA Vertically averaged APE of ensemble initial perturbations Vertical profile of APE of ensemble initial perturbations 2000km JMA ECMWF NCEP Comparison of APE of ensemble initial perturbations for Typhoon Sinlaku (2008) Storm-relative coordinate 2000km 10 Sep. 00Z (before-recurv.) 15 Sep. 00Z (during-recurv.) 18 Sep. 00Z (after-recurv.)

33. NCEPJMA Vertically averaged SHE of ensemble initial perturbations Vertical profile of SHE of ensemble initial perturbations 2000km JMA NCEP Comparison of SHE of ensemble initial perturbations for Typhoon Sinlaku (2008) Storm-relative coordinate 2000km 10 Sep. 00Z (before-recurv.) 15 Sep. 00Z (during-recurv.) 18 Sep. 00Z (after-recurv.)

34. ECMWF’s perturbations 1.ECMWF perturbs wind and temperature and does not perturb specific humidity. 2.In the before-recurvature stage, the ECMWF wind perturbation has a peak at 700- hPa on average and is largest in the near environment of the typhoon. Looking at each ensemble member, the maximum amplitude is found to be 4.4 m s −1, appearing about 700 km away from the typhoon center while the amplitude within 100 km from the typhoon center is only 1.6 m s −1 at most. 3.As the typhoon moves northward, the amplitude above 500-hPa becomes larger, corresponding to the change in the area of highest amplitude from the typhoon surroundings to the synoptic features north of the typhoon. 4.As with the wind perturbation, the temperature perturbation also has a peak in the mid-troposphere (e.g., the maximum amplitude in the before-recurvature stage is 2.6 K at 500-hPa and about 500 km away from the typhoon center, implying that the perturbation has little influence on the warm core structure in the inner region). 5.The vertical profiles of the wind and temperature perturbations are quite similar to those of perturbations seen in TEPS at JMA, that also uses singular vectors targeted for TCs (Yamaguchi et al 2009).

35. NCEP’s perturbations 1.NCEP perturbs all components; wind, temperature and specific humidity. 2.The amplitude of the wind perturbation is larger than ECMWF, especially in the upper troposphere. For example, it is 9.2 times as large as ECMWF at 200-hPa in the before-recurvature stage; the amplitude averaged over the 2000 km × 2000 km domain about the typhoon center is 3.4 m s −1. This trend is common in the other stages. 3.In the before-recurvature stage, there are large amplitudes in the temperature and specific humidity perturbations within about 300 km from the typhoon center. Looking at each ensemble member, the maximum amplitude of temperature (specific humidity) perturbation is found to be 2.1 K (1.8 g kg −1 ), which appear at 250-hPa (700-hPa). Considering that the temperature anomaly due to the warm core structure in the non-perturbed field (not shown) is about 4.0 K at 250 hPa, the temperature perturbation strengthens the warm core structure by about 50 %. The specific humidity perturbation increases the moisture by 16 % at 700-hPa with respect to the non-perturbed field.

36. JMA’s perturbations 1.JMA also perturbs all components; wind, temperature and specific humidity. 2.JMA’s perturbations are characterized by the large amplitude of the specific humidity perturbation. For example, it is 3.7 times as large as NCEP at 925- hPa in the before-recurvature stage; the amplitude averaged over the 2000 km × 2000 km domain about the typhoon center is 1.25 g kg −1. 3.The perturbation area is not in the typhoon surroundings but mainly south of the typhoon. This is because JMA uses moist singular vectors for creating the perturbations and they are not targeted for each TC, but for the entire tropics. That is why the amplitude south of the typhoon becomes smaller as the typhoon moves north. 4.On the other hand, the amplitude of the wind perturbation is small. For example, it is a quarter of ECMWF at 700-hPa in the before-recurvature stage; the amplitude averaged over 2000 km × 2000 km domain about the typhoon center is 0.24 m s −1. This trend is common in the other stages.

37. How do the perturbations modify the symmetric and asymmetric wind field of Typhoon?

38. Symmetric wind field ECMWFNCEP Tangential wind at 850-hPa (before recurvature stage) Black: CTL Grey: Ensemble member 1.The size of the typhoon (radial profile of the symmetric component of tangential wind) is similar among the ensemble members in each EPS; 2.The range of maximum tangential wind is less than 1 m s −1 3.The radius of the maximum tangential wind does not change significantly; 4.The differences between ECMWF and NCEP are much larger than the differences caused by the initial perturbations in each ensemble. 5.These trends are common in other stages.

39. Asymmetric wind field Steering flow at 500-hPa (before recurvature stage) Black: CTL Grey: Ensemble member ECMWFNCEP The steering flow is defined here as the asymmetric flow at 500-hPa averaged over 300 km from the typhoon center. 1.The ensemble members are dispersed around the non-perturbed member in both EPSs. 2.The change in the steering flow of NCEP is larger than ECMWF; In the before-recurvature stage, it is 0.67 m s −1 for NCEP and 0.49 m s −1 for ECMWF on average. 3.These trend are common in other stages, probably due to the relatively large amplitude of initial perturbations

40. Perturbation evolution for Sinlaku

41. Growth of kinetic energy of asymmetric wind component of ensemble perturbations A 2-day time series of the kinetic energy of the storm-relative asymmetric component of each wind perturbation at 500-hPa. The kinetic energy is calculated as the difference between asymmetric wind components of the non-perturbed member (control analysis and forecast) and each ensemble member to investigate how the steering flow of the ensemble members is different from that of the non-perturbed member. ECMWFNCEP Before recurvature stage (00Z 10 th Sep. 2008)

42. Definition of kinetic energy of asymmetric wind component (u asym_i − u asym_c ) 2 + (v asym_i − v asym_c ) 2, where (u asym_i, v asym_i ) and (u asym_c, v asym_c ) are the asymmetric horizontal wind fields of the i’th ensemble member and the control, respectively.

43. Ensemble member with the largest growth (ECMWF Ensemble member 44) Before recurvature stage (00Z 10 th Sep. 2008) 3000km

44. Track of EPS member with the largest growth (ECMWF) Ensemble member 43Ensemble member 44 Westernmost (left) and easternmost (right) course among all EPS members

46. Definition of kinetic energy of ensemble initial perturbations (u i − u c ) 2 + (v i − v c ) 2, where (u i, v i ) and (u c, v c ) are the horizontal wind fields of the i’th ensemble member and the control analysis about which the ensemble is constructed, respectively.

47. ECMWFNCEP Sinlaku 00UTC 13 Sep. Sinlaku 00UTC 10 Sep. Growth of kinetic energy of asymmetric wind component of ensemble perturbations A 2-day time series of the kinetic energy of the storm-relative asymmetric component of each wind perturbation at 500-hPa. The kinetic energy is calculated as the difference between asymmetric wind components of the non- perturbed member (control analysis and forecast) and each ensemble member to investigate how the steering flow of the ensemble members is different from that of the non-perturbed member.

48. Definition of kinetic energy of asymmetric wind component (u asym_i − u asym_c ) 2 + (v asym_i − v asym_c ) 2, where (u asym_i, v asym_i ) and (u asym_c, v asym_c ) are the asymmetric horizontal wind fields of the i’th ensemble member and the control, respectively.

49. 20072008 1 day forecasts 3 day forecasts Relationship of spread of ensemble track forecasts between ECMWF and NCEP r = 0.27 r = 0.21r = 0.56 r = 0.26

50. Summary 1.Ensemble perturbations and their growth around a tropical cyclone are investigated using the THORPEX Interactive Grand Global Ensemble (TIGGE). 2.Vertical and horizontal distributions of initial perturbations produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP) and the Japan Meteorological Agency (JMA) are compared for Typhoon Sinlaku 3.The amplitudes and distributions of the perturbations are found to be different among the 3 centers: before, during and after recurvature. 4.The growth rate of the asymmetric component of wind perturbations (that control the steering flow) is much higher in the ECMWF ensemble than that of NCEP, usually leading to a relatively large ensemble spread of tracks in ECMWF for forecasts beyond 3 days. Due to the relatively large amplitudes of their initial perturbations, NCEP generally possesses a larger ensemble spread at forecast times of order 1 day.

51. Interactive forecast system Observation Data assimilation Numerical weather prediction User Current system Interactive forecast system adaptive observations sensitivity analysis Observation Data assimilation User A sensitive analysis technique is needed to maximize the effect on a numerical prediction and to minimize the cost of the observations. sensitive area Adaptive observations ©Vaisala © JAXA © ＮＡＳＡ Numerical weather prediction

52. 2 nd year in RSMAS On singular vector based sensitivity analysis for tropical cyclones in a non-divergent barotropic framework Title: Motivation: How sensitive are the fast-growing perturbations to the intensity, size and asymmetry of the initial TC-like vortex? How does the perturbations (sensitivity region) affect track forecasts? Methodology: Using the SPECTRAL ELEMENT OCEAN MODEL (M. Iskandarani et al. 1995), singular vectors are computed for various initial conditions.

53. Sharanya S. Majumdar 1 Melinda S. Peng 2 Carolyn A. Reynolds 2 David S. Nolan 1 1. Rosenstiel School of Marine and Atmospheric Science, University of Miami 2. Marine Meteorology Division, Naval Research Laboratory Acknowledgments Thank you for listening

54. Present status of tropical cyclone track forecasts The Japan Meteorological Agency (JMA) provides tropical cyclone track forecasts in the form of a probability circle, which is a circular range into which a tropical cyclone is expected to move with a 70% probability at each valid time. The radius is determined statistically from the recent verification results of track forecasts. Forecast time (hours) Direction of movement (deg.)Speed of movement (V) V = < 15 kt15kt < V = < 30 kt V > 30 kt 1260 NM 100 NM 24 Before recurvature (180-310)80 NM 100 NM150 NM During recurvature (320-000)90 NM After recurvature (010-170)100 NM 48 Before recurvature (180-310)150 NM 170 NM190 NM During recurvature (320-000)150 NM After recurvature (010-170)160 NM 72 Before recurvature (180-310)220 NM 270 NM400 NM During recurvature (320-000)220 NM After recurvature (010-170)290 NM Probability circle Radius of Probability Circle

55. Best track Typhoon CHABA Initial: 2004.08.28 12 UTC Typhoon CHABA Initial: 2004.08.28 12 UTC Typhoon MARIA Initial: 2006.08.06 12 UTC Typhoon MARIA Initial: 2006.08.06 12 UTC Deterministic forecasting is as good a guess as any Another day, the deterministic forecast by JMA/GSM is wrong… One day, the deterministic forecast by JMA/GSM is perfect! Red line: JMA/GSM Black line: Best track

56. Why is a probabilistic approach needed ? Position error (km) average Position errors of each TC Track Forecast by JMA/GSM so far in 2007 Forecast time: 72 hours Total number of forecast events: 102 Position errors are sorted in ascending order

57. JMA begins operation of the Typhoon EPS  The 20 km GSM, which will become operational from 21 st Nov, will support both TC track and intensity forecasting. The Japan Meteorological Agency (JMA) has developed a new ensemble prediction system (EPS) known as the Typhoon EPS, aiming to further improve both deterministic and probabilistic forecasting of TC movements. We will start operation of the Typhoon EPS no later than the beginning of the typhoon season in 2008 following preliminary operation since May 2007.

58. Position error (km) Number of Samples Forecast time (hours) Compared with control forecasts, ensemble mean forecasts statistically have smaller errors, especially after four-day forecasts. Black: Control Run Red: Ensemble Mean Black: Control Run Red: Ensemble Mean The Typhoon EPS provides better deterministic forecasts

59. Forecast uncertainty changes day by day (1) Best track Example of probabilistic forecast. There is only one conceivable scenario! Example of probabilistic forecast. There is only one conceivable scenario! Each ensemble member having a common track scenario means that the scenario is highly likely. People can act accordingly, e.g. those in areas where predictions show no possibility of the typhoon striking can avoid taking unnecessary action against it. Deterministic forecast (red line) Initial: 2004.08.28 12 UTC Deterministic forecast (red line) Initial: 2004.08.28 12 UTC Typhoon EPS (11 members: red to orange) (blue: control forecast) Deterministic forecast

60. Forecast uncertainty changes day by day (2) Each ensemble member having a common track scenario means that the scenario is highly likely. People can act accordingly with full confidence, e.g. they can prepare for possible damage well in advance. Deterministic forecast (red line) Initial: 2006.12.02 12 UTC Deterministic forecast (red line) Initial: 2006.12.02 12 UTC Medium-range EPS (51 members: red to orange) (green: ensemble mean forecast) Deterministic forecast Example of probabilistic forecast. There is only one conceivable scenario! Example of probabilistic forecast. There is only one conceivable scenario! Best track

61. Forecast uncertainty changes day by day (3) Deterministic forecast (red line) Initial: 2006.08.07 00 UTC Deterministic forecast (red line) Initial: 2006.08.07 00 UTC Example of probabilistic forecast Forecast uncertainty is quite large. Example of probabilistic forecast Forecast uncertainty is quite large. Even if the most likely solution (or deterministic forecast) is wrong, with several other scenarios presented, people can act accordingly, e.g. they can prepare for possible damage well in advance. Best track Deterministic forecastTyphoon EPS (11 members: red to orange) (blue: control forecast)

62. 1. Reliability index position error (km) average ? Current Application A  Based on ensemble spread or a cluster analysis technique, we can optimize the size of the probability circle.  In addition, a reliability index such as A, B and C, where A is the highest reliability, might be easy to understand even for the general public. Future Application

63. Current Application In the stage where a typhoon is going along with a subtropical jet, forecast uncertainty is relatively large in the direction of movement compared with that in the crosswise direction. How good an indicator are circles? Uncertainty in a forecast track is represented with a round shape, whose radius is decided based on a statistical method. Orange: JMA Typhoon EPS, Green: JMA Medium-range EPS Pink: ECMWF Medium-range EPS, Blue: JMA TYM

64. Current ApplicationFuture Application The area representing forecast uncertainty could be optimized by using ensemble spread, which changes day by day and typhoon by typhoon. 2. Practical representation of TC-threatened areas Uncertainty in a forecast track is represented with a round shape, whose radius is decided based on a statistical method.

65. 3. Typhoon Strike Probability Map % The figure represents the probability that typhoon DURIAN will pass within a 120-km radius during a given 24-hour period. Black line: Best track during the given 24-hour period Grey line: Best track from the initial time to the starting time of the given 24-hour period. SunMonTueWedThuFriSatSun 2627282930123 Typhoon DURIAN Initial: 2006.11.28 12 UTC Typhoon DURIAN Initial: 2006.11.28 12 UTC

66. JMA plans to start five-day forecasts TC track forecasts covering five days will be introduced thanks to both the development of NWP systems and implementation of the Typhoon EPS.

67. Future Issues 2. Further understanding as to what causes forecast uncertainties in TC track forecasts  T-PARC 1. Further discussion on how to use uncertainty information associated with TC track forecasts Future issues to be addressed include the following two points:

68. At T-PARC, we aim to reduce forecast uncertainties in TC track forecasts by performing airborne adaptive observations. T-PARC

69. Summary  JMA will start the Typhoon EPS no later than the beginning of the typhoon season in 2008.  TC track forecasts covering five days will be introduced thanks to both the development of NWP systems and implementation of the Typhoon EPS.  Uncertainty information associated with TC track forecasts will be provided using the Typhoon EPS.  T-PARC will help us to further address TC predictability and improve NWP systems.  We would like to enhance our relationships more in order to consider more beneficial use of probabilistic TC forecasts.

70. Reference 申し訳ありません。 適当な参考文献がありません。

71. 謝謝

72. Typhoon Strike Probability Map for Typhoon MARIA The figure represents the probability that typhoon MARIA will pass within a 120-km radius during the period from the initial time, 00 UTC on 7 Aug 2006, to three days ahead, 00 UTC on 10 Aug 2006. Black line: Best track Typhoon MARIA Initial: 2006.08.07 00 UTC Typhoon MARIA Initial: 2006.08.07 00 UTC

73. Why is a probabilistic approach needed ? Position error (km) average Position errors of each TC Track Forecast by JMA/GSM so far in 2007 Forecast time: 72 hours Total number of forecast events: 102 Position errors are sorted in ascending order

74. Various approach to improve TC track forecasts Observation Data assimilation Numerical weather prediction User Current system Interactive forecast system adaptive observations sensitivity analysis observation Data assimilation User A sensitive analysis technique is needed to maximize the effect on a numerical prediction and to minimize the cost of the observations. sensitive area Adaptive observations ©Vaisala © JAXA © ＮＡＳＡ Numerical weather prediction