1 / 17

Ensemble-based adaptive sampling and data assimilation issues in tropical cyclones

Ensemble-based adaptive sampling and data assimilation issues in tropical cyclones. Sharanya J. Majumdar (RSMAS/U.Miami) Collaborators, present and future: Carolyn Reynolds , Xuguang Wang , Sim Aberson, Craig Bishop, Roberto Buizza, Yongsheng Chen, Tom Hamill, Melinda Peng

ozzie
Download Presentation

Ensemble-based adaptive sampling and data assimilation issues in tropical cyclones

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Ensemble-based adaptive sampling and data assimilation issues in tropical cyclones Sharanya J. Majumdar (RSMAS/U.Miami) Collaborators, present and future: Carolyn Reynolds, Xuguang Wang, Sim Aberson, Craig Bishop, Roberto Buizza, Yongsheng Chen, Tom Hamill, Melinda Peng EnKF Workshop, Austin TX, 10-12 Apr 2006

  2. HURRICANE WILMA, 24th October 2005

  3. Initialization time Observing time Verification time tv ti to t 2 days 2 days 2 topics • Adaptive Sampling • ETKF tested as an alternative to uniform sampling / ensemble spread for hurricane synoptic surveillance • How do targets compare with Singular Vectors? • Data Assimilation • Limited development and application of EnKFs to tropical cyclones

  4. ETKF: Adaptive Sampling STEP 1: Error covariance matrix for ROUTINE obs network Pr(t) = Pf - Pf HrT (Hr Pf HrT + Rr)-1 Hr PfandZr = Zf Tr STEP 2: Using SERIAL ASSIMILATION theory, covariance update for q’th possible ADAPTIVE observational network Pq(t) = Pr - Pr HqT (Hq Pr HqT + Rq)-1 Hq Pr = Zr(t)ZrT(t) – Zr(t) CqGq(Gq + I)-1 CqT ZrT(t) = Pr - Sq Holds for any time t if linear dynamics are obeyed. Sq is reduction in error covariance due to adaptive obs.

  5. Examples: Ivan. Observation Time 2004090900 SV targets in vicinity of storm. ETKF targets near the storm and to the NE. Majumdar et al. 2006, MWR

  6. Examples: Ivan. Observation Time 2004091600 SV targets in vicinity and to NW of storm. ETKF targets near the storm. Majumdar et al. 2006, MWR

  7. Composites of “Far” Targets SV maxima occur to the northwest. ETKF maxima often occur to the north and east.

  8. Variance Singular Vectors • To date, the most commonly used optimals are “total energy singular vectors”. • Need to combine error growth optimization with realistic estimates of analysis error covariance. • Do SV structures and growth rates change when this is considered?

  9. Variance Singular Vectors (courtesy Carolyn Reynolds) ETKF ECMWF Analysis Error Variance NAVDAS 3d-Var Analysis Error Variance Charley 0814 NRL NAVDAS TESV Charley 0814 ETKF VAR SV Using the ECMWF ETKF error variance as initial-time constraint pushes primary target downstream. 2-day growth diminished from 54.5 to 9.0.

  10. Conclusions and Issues • ETKF and TESV targets often differ, indicating the respective constraints and limitations. • Constraining AEC optimals (SVs) using the ETKF variance can produce targets similar to ETKF regions. Perturbation growth is damped considerably. • ETKF results are sensitive to the ensemble used. • Sampling errors can lead to spurious correlations (and targets) far from region of interest. Potential solutions: • time-dependent localization techniques • larger ensembles.

  11. DA in Hurricanes • Artificial operational methods: • Bogus Vortex (NOGAPS, UKMO) • Relocation (NCEP GFS + Ensemble) • Vortex Spin-Up (GFDL) • Research methods: • Bogus / 4d-Var (Zou, Xiao, Pu etc) • EnKF assimilating position (Lawson and Hansen 2006, Chen and Snyder 2006) • EnKFs assimilating physical variables?

  12. A “spun-up” hurricane

  13. Hurricane Dynamics • External Influences: Environmental Interactions • Vertical wind shear • Interaction with trough • Entrainment of dry air • Internal Influences • Air-sea fluxes of heat and momentum • Core asymmetries • Imbalanced adjustment processes • Eyewall cycles • Does an EnKF account for these processes? • Data assimilation • AEC Optimals (Hamill et al. 2002), Synoptic Analysis (Hakim and Torn 2005)

  14. PRELIMINARY RESULTS (Xuguang Wang, NOAA/CIRES) (1) Assimilation of single v ob: 5 m/s higher than background v

  15. (2) EnKF-based covariance of decrease in central SLP with T and v

  16. Observations in Hurricanes • Satellite • GOES winds (include rapid-scan) • AIRS, AMSR-E temp. and water vapor, 15km res • Aircraft • GPS Dropwindsondes • Dual Doppler Radar (3-d wind fields and Z) • Stepped-Frequency Microwave Radiometer • UAVs

More Related