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Local Ensemble Transform Kalman Filter for ROMS in Indian Ocean

Local Ensemble Transform Kalman Filter for ROMS in Indian Ocean. Arya Paul Indian National Centre for Ocean Information Services ( INCOIS ) India. Acknowledgements:. Steve Penny ( Univ. of Maryland ) Eugenia Kalnay ( Univ. of Maryland ) Siva Reddy ( INCOIS ) Biswamoy Paul ( INCOIS )

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Local Ensemble Transform Kalman Filter for ROMS in Indian Ocean

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  1. Local Ensemble Transform Kalman Filter for ROMS in Indian Ocean Arya Paul Indian National Centre for Ocean Information Services ( INCOIS ) India Acknowledgements: Steve Penny ( Univ. of Maryland ) Eugenia Kalnay ( Univ. of Maryland ) Siva Reddy ( INCOIS ) Biswamoy Paul ( INCOIS ) Balaji B. ( INCOIS ) D. S. Banerjee ( INCOIS ) ROMS ASIA-PACIFIC WORKSHOP, OCT 19, 2016

  2. Local Ensemble Transform Kalman Filter ( LETKF ) Observations Initial background ensemble • No model dependency • Each model grid point is • simultaneously assimilated. • Very easily parallelized. • No requirement of adjoint. • Can be easily extended to 4D. Ensemble “observations” LETKF Observation Operator Ensemble analysis

  3. Observation based localization • Data Assimilation is done in a • local volume • All the observation within the • circle is assimilated. • State is updated at the red dot.

  4. Local Ensemble Transform Kalman Filter ( LETKF ) Forecast: Analysis: Localization : Choose the observations to be used for each grid point. Estimate the local error covariance and perturbations in ensemble space. Analysis Mean in the ensemble space : Add to each of the columns of The new ensemble analysis in model space are stored in the columns of

  5. NUMERICAL MODEL Domain : 30E – 120E, 30S – 30N Model Resolution : 1x12 Vertical Levels : 40 Open Boundaries in East & South. Boundary Conditions from INCOIS-GODAS No Tides and River Runoff Forcing : 20th Century NCEP R2 200km 32 ensembles in wind, precipitation rate and short wave radiation. SSS relaxed to climatology INDIAN OCEAN

  6. OBSERVATIONS In-situ T & S (Argo + Buoy) SLA TRACKS Not many T & S observations in Indian Ocean in 2010!!!

  7. System Comparison

  8. SST COMPARISONS

  9. STANDARD DEVIATION OBSERVATION FREE RUN INCOIS-GODAS LETKF-ROMS

  10. Free LETKF-ROMS INCOIS-GODAS RMSE of SST

  11. SST CORRELATION WITH REYNOLDS SST FREE ROMS INCOIS-GODAS LETKF-ROMS

  12. SLA COMPARISONS

  13. STANDARD DEVIATION FREE ROMS OBSERVATION INCOIS-GODAS LETKF-ROMS

  14. Free ROMS LETKF-ROMS INCOIS-GODAS RMSE of SLA In meters

  15. SPATIAL RMSE OF SLA ( in meters ) FREE ROMS INCOIS-GODAS LETKF-ROMS

  16. SLA CORRELATION WITH AVISO FREE ROMS INCOIS-GODAS LETKF-ROMS

  17. VERTICAL STRUCTURES

  18. @ 12 N, 90 E D20 Obs INCOIS-GODAS FREELETKF-ROMS MLD

  19. @ 8S, 95 E D20 Obs INCOIS-GODAS FREELETKF-ROMS MLD Obs INCOIS-GODAS FREELETKF-ROMS

  20. WHY IS LETKF-ROMS INFERIOR TO INCOIS-GODAS ?

  21. SPREAD IN SSS SPREAD IN SST SPREAD IN TEMP at 50m SPREAD IN SALT at 50m

  22. Conclusions • In-situ observational coverage in Indian Ocean is sparse compared to Pacific. • The FREE-ROMS itself is unable to capture the features • LETKF-ROMS performance is inferior to INCOIS-GODAS. • It may be due to • (a) Ensemble Collapse • (b) Bad Initial Condition • (c) Forcings • (d) SST relaxation in INCOIS-GODAS • Possible remedy lies in training the initial condition and using better inflation • schemes. WE STILL HAVE A LONG WAY TO GO !!! Comments, Suggestions Please !!!

  23. AND THEN THERE WERE SHOCKS !!! • Virtual assimilation System: • NEMO-LETKF • OGCM: NEMO • Assimilation scheme: LETKF • Observations assimilated: Simulated T&S profiles from a Nature run using SPEEDY-NEMO • Forcing: Simulated imperfect ensemble forcing from SPEEDY-NEMO • Real assimilation System: • INCOIS-GODAS • OGCM: MOM4.0 • Assimilation scheme: 3DVAR • Observations assimilated: Real T&S profiles • Forcing: NCEP-R2

  24. Observation coverage over a typical month Real Virtual

  25. Comparisons within moored buoy coverage area (10S-10N) Real Virtual Real System (INCOIS-GODAS): Reference is Reynolds for SST and for SSS and SSH the reference is from REF experiment where Argo+Mbuoy are assimilated Virtual System (NEMO-LETKF): Reference is the outputs from Nature run SST SSS Assim is the experiment where only Moored buoy observations are assimilated SSH

  26. RMSE difference between free and assimilation experiment. +ve indicates degradation due to assimilation and vice versa (Sq.Error in MBuoy exp) – (Sq.Error in Free exp) Real Virtual REF Only Argo Only MBUOY Free SSH averaged just above moored buoy coverage area from REAL DA system These assimilations shocks propagate westward at ~0.25 m/s REAL: SSHA Assimilation shocks around moored buoy coverage area (Sq. Error in Mbuoy exp) – (Sq.Error in Free exp) 40E-160E & 10N-15N

  27. Real Virtual (Mbuoy+Argo) – Free (Mbuoy+Argo) – Free (Mbuoy+Argo) – Argo (Mbuoy+Argo) – Argo Shocks introduced from real moored buoy assimilation is not fully suppressed by Argo

  28. Why worry about assimilation shocks? • Ocean re-analysis in the pre-Argo era may be seriously affected by these spurious shocks. Indian Ocean Dipole index Mbuoy exp wrongly interpreted neutral IOD as strong –ve IOD years. Importantly, the simulation is worse than Free exp.

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