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Ensemble Sensitivity Analysis of Tropical Cyclones: Preliminary Results from Typhoon Nuri (2008)

This study presents an analysis of Typhoon Nuri's genesis using ensemble sensitivity to understand key dynamical processes controlling tropical cyclogenesis. The research explores modifying initial conditions diagnostically to influence the genesis process. Sensitivity analysis techniques, including adjoint and ensemble methods, are applied to investigate the impact of perturbations on forecast metrics. Results highlight the relationship between initial condition perturbations and forecast outcomes. The study aims to enhance understanding of tropical cyclone genesis through comprehensive sensitivity analysis.

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Ensemble Sensitivity Analysis of Tropical Cyclones: Preliminary Results from Typhoon Nuri (2008)

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  1. Ensemble Sensitivity Analysis Applied to Tropical Cyclones: Preliminary Results from Typhoon Nuri (2008) Outline • Motivation • Ensemble Sensitivity • Typhoon Nuri (2008) • Experiment Design • Sensitivity Results • Summary and Future Work Rahul Mahajan & Greg Hakim University of Washington, Seattle Third THORPEX International Science Symposium (TTISS) Monterey California, 14 - 18 September, 2009

  2. Motivation and Goal • Key issues regarding Tropical Cyclones: • Dynamical understanding • Genesis and Structure • Predictability • Track and Intensity • Apply ensemble sensitivity analysis to identify dynamical processes that control genesis • Diagnostically modify initial conditions to stop or speed-up genesis processes

  3. Sensitivity Analysis • relates the change to an input (state: x) to changes in the output (metric: ) adjoint sensitivity to IC adjoint or transpose of TLM

  4. Sensitivity Analysis • adjoint approach is deterministic • consider and as random variables

  5. Ensemble Sensitivity • compute sensitivity from ensemble output • linear regression • dependent variable is forecast metric • independent variable is element of state vector • can apply confidence testing using ensemble statistics • no adjoint or tangent linear model required • generate an ensemble by cycling an ensemble Kalman filter

  6. Typhoon Nuri (2008)

  7. Typhoon Nuri (2008) Genesis -24 hrs 1230Z 16 August 2008

  8. Typhoon Nuri (2008) Genesis -12 hrs 0030Z 17 August 2008

  9. Typhoon Nuri (2008) Genesis 1230Z 17 August 2008

  10. Typhoon Nuri (2008) Genesis +12 hrs 0030Z 18 August 2008

  11. Typhoon Nuri (2008) TD DB TY TS

  12. Experiment Setup • Model • Weather Research and Forecasting (WRF) • 27km horizontal resolution domain over west Pacific • 38 vertical levels, 10 hPa model top • Data Assimilation • Ensemble Kalman square-root filter • 90 ensemble members • assimilate observations every 6 hrs • Conventional observations: • rawinsondes, ACARS, surface stations, buoys, ships, cloud track winds • Field observations from T-PARC / TCS08: • dropsondes, flight-level data, driftsondes

  13. Procedure • Cycle data assimilation from pre-depression to maximum intensity of typhoon Nuri • Generate ensemble forecast out to 24 hours from pre-depression to tropical storm strength • Compute sensitivity of metrics at forecast time to analysis state at initialization time

  14. Nuri 00hr forecast

  15. Nuri 06hr forecast

  16. Nuri 12hr forecast

  17. Nuri 18hr forecast

  18. Nuri 24hr forecast

  19. Sensitivity Results

  20. Forecast Sensitivity to 850 hPa height

  21. Initial Condition Perturbations • Test statistical prediction with model response • Perturb initial state via • independent variable is the forecast metric • dependent variable is the analysis • apply for a range of scales

  22. Nuri 24hr forecast – Perturbed IC Initialization Time Forecast Time

  23. Perturbed Forecasts

  24. Summary and Future Directions • Applied ensemble sensitivity analysis to explore tropical cyclogenesis of typhoon Nuri • Sensitivity associated with the incipient storm and axis from the North to the West of the storm • Modified initial conditions with perturbations: • small: model response comparable to ensemble predictions • large: model response less than the ensemble predictions suggesting non-linear interaction • Iterate over different choices of forecast metrics and analysis state variables and increased resolution • Assess the impact of T-PARC / TCS08 field observations

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