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Munehiko Yamaguchi Typhoon Research Department, PowerPoint Presentation
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Munehiko Yamaguchi Typhoon Research Department,

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  1. Topic No. 2 Initial Condition Sensitivity of Typhoon Track Prediction in the Western North Pacific Tropical Cyclone Ensemble Forecast Nanjing, China 9:00 – 12:00 2011.12.15 (Thr) Munehiko YamaguchiTyphoon Research Department, Meteorological Research Institute of the Japan Meteorological Agency

  2. The position error of 5-day forecasts in 2007 is smaller than that of 3-day forecasts in 1997. Performance of typhoon track predictions by NWP model Time series of 3-yr running mean of position errors by JMA’s Global Spectral Model from 1997 to 2007 (Yamaguchi et al. 2009, MWR) The accuracy of typhoon track predictions has improved steadily over the last few decades

  3. Verification for individual cases Position errors of 3-day predictions by JMA/GSM. Verification period: 3 years from 2008 to 2010. Distance between Beijing and Shanghai Position error (km) Sample Number

  4. Some issues to be addressed • The accuracy of typhoon track forecasts has steadily improved. • Chan (2010, GPTC) Since Chan et al. (2002) paper, research on the physics ofgeneral TC motion has been almost non-existent, which suggests that most scientists are quite content with the current theories of TC motion. Present • In reality, however, significant errors still exist and there are prediction cases where the position error exceeds 1000 km at 3 days. • There are few studies focusing on the cause of prediction errors. (e.g. Carr and Elsberry 2000a, 2000b). Issues Flow of typhoon forecasting Data assimilation NWP Obs. User Forecaster Approach Various sources of prediction errors Any approach to separate them to some extent?

  5. Design of numerical experiments JMA’s global spectral model (JMA/GSM) is run from the ECMWF’s initial conditions, which are available through the YOTC (Year of Tropical Convection) dataset, to distinguish TC track prediction errors attributed to initial conditions from those attributed to the NWP model. Black: Observed track Blue: JMA’s model + JMA’s initial condition Green: ECMWF’s model + ECMWF’s initial condition Red: JMA’s model + ECMWF initial condition

  6. Results Experiment period: 2009.07.22 to 2009.11.30 (4 months) Verified TCs: 16 TCs in the west Pac. over the 4 months JM-JI: JMA’s model + JMA’s initial condition EM-EI: EC’s model + EC’s initial condition JM-EI: JMA’s model + EC’s initial condition Position error (km) Number of samples Forecast time (hours)

  7. Comparison with verification results over 3 years The difference between JM-JI and EM-EI is similar to that seen in the verification for TCs over 3 years (see figure on the right), that is, EM-EI is better than JM-JI by a lead time of one day.

  8. Results Replacing the original initial condition of JMA/GSM with the ECMWF analysis reduces the TC track prediction errors by 5 %, 11 %, 9 %, 11 % and 15 % at 1 to 5 days, respectively, and explains 20 %, 29 %, 29 %, 38 % and 68 % of the difference in the errors between JMA and ECMWF at 1 to 5 days, respectively. Position error (km) Number of samples Forecast time (hours)

  9. Individual cases There are prediction cases where the replacement of the initial condition significantly improves the track prediction. Typhoon Dujuan initiated at 12 UTC 5th Sep. 2009 Typhoon Lupit initiated at 12 UTC 21st Oct. 2009 Error reduction at 3 day: 595 km to 122 km Error reduction at 3 day: 720 km to 280 km Black: Observed track Blue: JMA’s model + JMA’s initial condition (JM-JI) Green: ECMWF’s model + ECMWF’s initial condition (EM-EI) Red: JMA’s model + ECMWF initial condition (JM-EI) Orange: JMA’s model + low wavenumber component (≤ T42) of ECMWF initial condition + high wavenumber component (≥ T42) of JMA initial condition (JM-EI2)

  10. Ensemble prediction for those cases Ensemble track prediction by the JMA Typhoon EPS (TEPS) that deals with initial condition uncertainties based on singular vectors. Typhoon Dujuan initiated at 12 UTC 5th Sep. 2009 Typhoon Lupit initiated at 12 UTC 21st Oct. 2009 TEPS captures the scenario of the observed track. It implies that TEPS is successful in expressing the uncertainties of TC track predictions when they are sensitive to initial conditions.

  11. Another individual cases There are prediction cases where the replacement of the initial condition does not help improve the prediction while the ECMWF’s prediction is accurate. Typhoon Morakot initiated at 12 UTC 4th Aug. 2009 Typhoon Parma initiated at 12 UTC 30th Sep. 2009 Black: Observed track Blue: JMA’s model + JMA’s initial condition (JM-JI) Green: ECMWF’s model + ECMWF’s initial condition (EM-EI) Red: JMA’s model + ECMWF initial condition (JM-EI)

  12. Ensemble prediction for those cases TEPS cannot capture the observed track, either, implying need for modifications of JMA/GSM and/or dealing with model uncertainties in TEPS. Typhoon Morakot initiated at 12 UTC 4th Aug. 2009 Typhoon Parma initiated at 12 UTC 30th Sep. 2009

  13. Northward bias –Typhoon Conson (2010) - Northward bias is not a problem only in JMA but also in other major NWP centers. It is noteworthy that such northward bias tends to appear in the east of Philippines. JMA ECMWF CMC UKMO

  14. Northward bias –Typhoon Nanmadol (2011) - It would be of great importance to identify the cause of the bias and modify the NWP systems including EPSs for better deterministic and probabilistic forecasts. JMA ECMWF CMC NCEP

  15. Summary The representation of the steering flow formed by the synoptic environment around the TCs is important for accurate TC track predictions as demonstrated by various previous studies (e.g. Chan and Gray 1982). Ensemble prediction, which deals with initial condition uncertainties, is successful in expressing the uncertainties of TC track predictions when they are sensitive to initial conditions. There are systematic errors in NWP models. The northward bias that tends to appear to the east of the Philippines would be common systematic errors among many NWP models.