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Applying an outer-loop to the WRF-LETKF system for typhoon assimilation and prediction

Applying an outer-loop to the WRF-LETKF system for typhoon assimilation and prediction. Shu-Chih Yang 1 ,Kuan-Jen Lin 1 , Takemasa Miyoshi 2 and Eugenia Kalnay 2 1 Dept. of Atmospheric Sciences, National Central University, Taiwan;

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Applying an outer-loop to the WRF-LETKF system for typhoon assimilation and prediction

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  1. Applying an outer-loop to the WRF-LETKF system for typhoon assimilation and prediction Shu-Chih Yang1,Kuan-Jen Lin1, Takemasa Miyoshi2 and Eugenia Kalnay2 1 Dept. of Atmospheric Sciences, National Central University, Taiwan; 2 Dept. of Atmospheric and Oceanic Science, Univ. of Maryland, USA

  2. Motivation • The regional EnKF needs a spin-up period when cold-starting the ensemble with global analyses. • For typhoon prediction, the reliability of the mean state strongly affects the track prediction. • The quality of initial mean state determines the spin-up length. • During the spin-up, valuable observations (e.g. reconnaissance aircraft) can’t be effectively used. • The “Running In Place” method can serve as a generalized outer-loop for the EnKF framework to improve the nonlinear evolution of the ensemble (Kalnay and Yang, 2010, Yang et al. 2012). • The RIP method is designed to re-evolve the whole ensemble to catch up the true dynamics, represented by observations. • Both the accuracy of the mean state and structure of the ensemble-based covariance are improved.

  3. Standard LETKF framework LETKF (ti-1) Obs(ti-1) xa0 (ti-1) Nonlinearmodel M[xa(ti-1)] Time xb0 (ti) LETKF (ti) Obs(ti) xa0 (ti)

  4. Standard LETKF framework LETKF (ti-1) Obs(ti-1) xa0 (ti-1) Nonlinearmodel M[xa(ti-1)] Time Adjust dynamical evolutions at an earlier time xb0 (ti) LETKF (ti) Obs(ti) xa0 (ti)

  5. Xa0 (ti-1) “Running in place” in the LETKF framework Obs(ti-1) LETKF (ti-1) Random Pert. +  Nonlinearmodel M [xa(ti-1) ] Time  xbn (ti) no-cost Smoother  LETKF (ti)  Obs(ti) Threshold > ε xa(ti)  False Re-evolve the whole ensemble to catch up the true dynamics, represented by Obs

  6. Increase the influence of observations ☐“Hard way”: • Reduce the observation error and assimilate this observation once. • Compute the analysis increment at once “soft way” • Use the original observation error and assimilate the same observation multiple times. • The total analysis increment is achieved as the sum of multiple smaller increments (advantageous with nonlinear cases).

  7. Increase the influence of observations “soft way”: RIP/QOL areadvantageous corrections with nonlinear cases With a smaller ensemble spread, smaller corrections allow the increments to follow the nonlinear path toward the truth better than a single increment.

  8. With RIP/QOL, filter divergence is avoided!! Results from the Lorenz 3-variable model (Yang et al. 2012a) • With RIP/QOL, the LETKF analysis with nonlinear window is much improved, even better than 4D-Var! Assimilation with different obs. sets • RIP and QOL use the observations more efficiently for the under-observed cases. Performance: RIP > QOL > standard ETKF

  9. With RIP/QOL, filter divergence is avoided!! Results from the Lorenz 3-variable model (Yang et al. 2012a) • With RIP/QOL, the LETKF analysis with nonlinear window is much improved, even better than 4D-Var! Assimilation with different obs. sets • RIP and QOL use the observations more efficiently for the under-observed cases. Performance: RIP > QOL > standard ETKF How about RIP for a dynamical complex model?

  10. Application of LETKF-RIP to typhoon assimilation/prediction • Experiment setup: • Regional Model: Weather Research and Forecasting model (WRF, 25km) • RIP is used as an generalized outer-loop to improve the ensemble evolution during the spin-up • Assimilation schemes: LETKF and LETKF-RIP with 36 ensemble members • LETKF-RIP setup • Computed the LETKF weights at analysis time (00,06,12,18Z) • Use these weight to reconstruct the ensemble (U, V) at (03,09,15,21Z) • perform the 3-hr ensemble forecasts • Re-do the LETKF analysis (only one iteration is tested) A B time 15 03 00 06 09 18 12

  11. Observations used in the OSSE experiments

  12. Results from OSSE: Impact on analyses(Improving the TY’s inner core) N-S vertical cross-section of wind speed TRUTH LETKF LETKF-RIP COV(Vc, U)LETKF vs. U error ★ Rapid intensification in 12 hours COV(Vc, U)RIP vs. U error ★ The covariance structure is strongly correlated to error pattern. Time RIP Effectively spins up the dynamical structure of the typhoon! Yang et al. 2012b

  13. Results from OSSE: Impact on typhoon prediction (Improving the TY’s environmental condition) Capture the west-ward turning direction of the typhoon track 12 hour earlier!! Φ300hPa (2-day forecast) Truth LETKF-RIP LETKF Yang et al. 2012b

  14. Application to real observations 2008 Typhoon Sinlaku Observations: Upper-air sounding, dropsonde, surface station 09/08 00Z-09/09 12Z Tropical depression 09/12 18Z-09/11 00Z Typhoon 09/11 06Z-09/12 18Z Super typhoon 09/13 00Z-09/14 06Z Typhoon

  15. Experiment settings for the real case Standard LETKF run LETKF With RIP LETKF-RIP With RIP turn-off RIP LETKF-RIPs ★ ★ ★ ★ ★ ★ ★ ★ 2008/09/08 00Z 09 00Z 10 00Z 12 00Z 11 00Z ★: Dropsondes (reconnaissance aircraft) are available • Two cases are evaluated: • Typhoon structure is well observed by the dropsondes • Typhoon is under-observed.

  16. Dynamical adjustment OBS (QuikSCAT) (Wind Speed)suf vs. w (LETKF-RIP) • The RIP scheme enhances the cyclonic flow and the vertical motion near the eyewall. • The asymmetric structure with the strong winds is captured in the LETKF-RIP analysis. Difference (RIP − LETKF) (Wind Speed)suf vs. w (LETKF) Time: 2008/09/09 12Z

  17. Observation Impact Observation impact is computed according to Li et al. (2010), Kalnay et al. (2012). Anal time:2008/09/09 06Z The first aircraft data is available for the typhoon structure LETKF-RIP LETKF The effectiveness of the dropsonde data is greatly improved by RIP and the negative impact shown in the control run can be alleviated. Reduce the 6-hr fcst error

  18. Impact on Typhoon track prediction 4-Day track prediction initialized at 09/09 06Z Anal time:2008/09/09 06Z LETKF LETKF-RIP C130 aircraft data At early developing stage of the typhoon, the aircraft data can provide more useful information with RIP for improving track prediction.

  19. Another case: the typhoon is under-observed Analysis time: 09/10 12Z LETKF LETKF-RIP No reconnaissance aircraft available near the typhoon For the under-observed case, the LETKF-RIP analysis still leads to a better track prediction.

  20. Impact on Typhoon track prediction Mean absolute track Error • With RIP, the track prediction is significantly improved during the LETKF’s spin-up period. • RIP is especially useful for improving forecasts beyond 36 hours and the typhoon landfall location is better predicted. LETKF LETKF-RIP LETKF LETKF-RIP 36hr Error of land fall location Mean cross track Error * N: not landfall at Taiwan Averaged during the spin-up period (first two days)

  21. LETKF-RIP vs. LETKF-RIPs (RIP is turned off after spin-up) Averaged for the last two days Init: 09/09 12Z Cross-track error LETKF LETKF-RIP LETKF-RIPs LETKF-RIPs LETKF LETKF-RIP Error of central Psfc When RIP is turned off after the spin-up (2-day), the performance of the track/ intensity prediction is even better than the LETKF prediction.

  22. Summary • RIP can be used as a generalized outer-loop to improve the nonlinear evolution of the ensemble during the spin-up. • The RIP scheme accelerates the spin-up of the WRF-LETKF system and provides further adjustments for improving typhoon assimilation and prediction. • The value of flight data during the developing stage of typhoon is effectively increased. • Even when the typhoon is under-observed, the RIP analysis can still provide an improved prediction. • The dynamical adjustments include both the inner core and environmental condition of the typhoon. • RIP can be turned off after the system has spun up (2 days).

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