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Ensemble Sensitivity Analysis Applied to Tropical Cyclones: Preliminary Results from Typhoon Nuri (2008) Rahul Mahajan & Greg Hakim University of Washington,

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Presentation on theme: "Ensemble Sensitivity Analysis Applied to Tropical Cyclones: Preliminary Results from Typhoon Nuri (2008) Rahul Mahajan & Greg Hakim University of Washington,"— Presentation transcript:

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

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 Genesis -24 hrs1230Z 16 August 2008

8 Typhoon Nuri (2008) Genesis -12 hrs0030Z 17 August 2008

9 Typhoon Nuri (2008) Genesis1230Z 17 August 2008

10 Typhoon Nuri (2008) Genesis +12 hrs0030Z 18 August 2008

11 Typhoon Nuri (2008) DB TY TS TD

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 TimeForecast 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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