1. Near real-time predictions of salinity intrusion in a river-dominated estuary: tales and implications of a challenging cruise A.Baptista, Y. Zhang, G. Law, J. Needoba, N. Hyde, S. Frolov, P. Turner, M. Wilkin, C. Seaton, B. Howe, D. Hansen Modified from a presentation to the Unstructured Grid Workshop, Halifax, Sep 2008

2. “CMOP: Transforming Ocean Exploration” 2 Outline The “mature” observatory The “inconvenient” cruise The short term “fix” Open benchmark “retrospective “analysis Jul 2008 since 1996Jul 2008 Aug-Sep 2008 Skill metrics Sep 2008

3. “CMOP: Transforming Ocean Exploration” 3 Conclusions The river-dominated CR river-to-ocean system provides major scientific and management challenges The end-to-end observatory SATURN offers a modern and comprehensive monitoring and modeling infrastructure Under-predicted SIL in a recent cruise has challenged the SATURN modeling skill, leading to a new benchmark SELFE has met most of the benchmark challenges through added resolution. But, will other codes do better? Allied with Opendap-CF standards, an Open CR benchmark could offer a stringent snapshot of modeling skill across leading-edge models, with automated updates The goal is to unite and cross-inform (not divide) the multiple unstructured-grid model communities We invite broad participation!

4. “CMOP: Transforming Ocean Exploration” 4 SATURN: an end-to-end observatory Stakeholders Cyber- infrastructure Observation network Modeling system Daily forecasts Simulation databases Scenarios Network optimizations Researchers Educators Students Managers … …

5. “CMOP: Transforming Ocean Exploration” 5 SATURN mobile platforms CMOP cruises Observation network CORIE stations SATURN “endurance” stations SATURN “pioneer” stations Land-based remote sensing Context networks: 1 Slocum glider 2 REMUS-100

6. “CMOP: Transforming Ocean Exploration” 6 Circulation modeling system Function Support cruise planning, execution and analysis Characterize processes Characterize long-term variability Characterize and anticipate change Re-design observation network Mechanisms Daily forecasts (multiple) Multi-year simulation databases (multiple; since 1999) Scenario simulations –Climate –Human activities –Plate displacement Redundancy (models/simulations) as philosophy Codes (past): QUODDY, ADCIRC, POM Codes (current): ELCIRC, SELFE

7. “CMOP: Transforming Ocean Exploration” 7 What makes SELFE the current default model Robustness Ability to represent complex circulation processes and features, as required by CMOP research Computational efficiency  MPI SELFE v2.0g  Intel Xeon 2.3GHz cluster (canopus) with GBit connection  ~27K horizontal nodes; 24 S levels; ~30m minimum equiv. diameter  with 30s step: ~9x faster than real time  with 50s step: ~15x faster than real time ** See Joseph Zhang’s presentation, Friday afternoon **

8. “CMOP: Transforming Ocean Exploration” 8 Blind retrospective cruise analysis – estuary LMER - observations SELFE simulation psu … shows ability to represent complex and episodic features June 1999 Salinity Cruise data courtesy D. Jay

9. “CMOP: Transforming Ocean Exploration” 9 Blind retrospective cruise analysis – plume Pt Sur path (surface ) Data courtesy D. Jay (RISE project) RMSE=2.64 psu correlation = 0.80 ● Cruise data X SELFE simulation

10. “CMOP: Transforming Ocean Exploration” 10 Coarse scale cruise planning/analysis Maximum bottom salinity in the estuary over cruise period Minimum surface salinity in the plume over cruise period Total RNA content from the Aug 2007 CMOP cruise Cruise data courtesy L. Herfort and M. Smit

11. “CMOP: Transforming Ocean Exploration” 11 Forecast skill: prediction of plume location Cruise data courtesy B. Crump

12. “CMOP: Transforming Ocean Exploration” 12 Goal: validate simulation of SIL (Salinity Intrusion Length) SIL has a clear response to river discharge, and is being consider as a possible “sentinel” for CR variability and change

13. “CMOP: Transforming Ocean Exploration” 13 SIL: difficult to measure … (a) Data collected by David Jay on LMER and NOAA cruises Chawla, Jay, Baptista, Wilkin and Seaton, CSR 2008

14. “CMOP: Transforming Ocean Exploration” 14 10:0909:0009:32 … and difficult to simulate (forecast; fDB16; July 13) 07:23 Cruise data courtesy J. Needoba 08:41

15. “CMOP: Transforming Ocean Exploration” 15 Exploring options (in forecast mode, during the cruise) Data assimilation (DA) Method of Frolov et al. 2008 Model-independent Reliant on fast model surrogates (SVD decomposition, machine-learning trained Grid refinement nchannel schannel …

16. “CMOP: Transforming Ocean Exploration” 16 Grid refinement fDB16 Refined grid (nchannel) mottb cbnc3 grays

17. “CMOP: Transforming Ocean Exploration” 17 fDB16 nchannel July 17 0:30am July 17 0:30am Bottom salinity (forecasts; July 17) July 17July 16 Tide (at grays ) 1.6m -1.5m DA goes here DA trained on DB16 July 17 0:30am

18. “CMOP: Transforming Ocean Exploration” 18 CMOP July 2008 cruise: Real-time forecast da nchannelfDB16 July 17 2008 09:59 DA

19. “CMOP: Transforming Ocean Exploration” 19 Salinity at challenging stations (forecasts, July 16-17) nchannelda fDB16nchannelDA nchannelDA mottb cbnc3 fDB16

20. “CMOP: Transforming Ocean Exploration” 20 Retrospective analysis PeriodTarget features/processesObservations SI-01Sep16-Oct13 2004 Salinity intrusion: two consecutive spring- neap sequences show distinct salinity patterns at eliot (modest salt penetration in the first sequence, extensive in the second) Extensive fixed-station data SI-02Jun-Jul 2008 Salinity intrusion: 5-day with > half a tidal cycle each in one of the two channels; mostly flood spring tide. High-quality CMOP cruise data VS-01Jun11-25 1999 Vertical salinity structureHigh-quality LMER profiling data RV-01Apr-Nov 2002 Residual velocities and salinity structure. Not a big spring freshet year, however. ADCP data at am012, am169, tansy, red26, and coaof, as well as fairly extensive S-T data (including 3 level at red26 abnd am169, some eliot, and some sveni with salt)

21. “CMOP: Transforming Ocean Exploration” 21 Grid refinement fDB16 eliot Refined grid (“hires”) # nodes: 27416 # elements: 53314 #  levels 24 min element area: 942 m^2 max element area: 89834 m^2 grays

22. “CMOP: Transforming Ocean Exploration” 22 “hires” hindcasts (eliot; Oct 2004) hires  t=30sec DB16 hires  t=30sec DB16

23. “CMOP: Transforming Ocean Exploration” 23 “hires” hindcasts (eliot; Oct 2004) hires  t=30sec hires  t=50sec DB16 hires  t=75sec

24. “CMOP: Transforming Ocean Exploration” 24 Forecast (grays; Sep 15-16 2008) RMSE= 5.2 psu RMSE= 1.6 psu Salinity ` fhires;  t=20sec fDB16

25. “CMOP: Transforming Ocean Exploration” 25 Definition of Skill Assessment metrics NameDefinitionNotes Index of agreement IOA ranges from 0 to 1 (1 is perfect skill; 0 is no skill) Mean square error MSE  0 (0 is perfect skill) Root mean square error RMSE  0 (0 is perfect skill) Correlation skill score Perfect skill:  mo =1 Normalized standard deviation Perfect skill: Nstdev=1 Model biasPerfect skill: MB=0 See : http://www.ccalmr.ogi.edu/~cseaton/tmp/dec06/pub/index_page.html See : http://www.ccalmr.ogi.edu/~cseaton/tmp/dec06/pub/index_page.html

26. “CMOP: Transforming Ocean Exploration” 26 Forecast skill assessment (fhires; Sep 15-16, 2008) Correlation skillIOA RMSE N Biofouled sensor Degraded sensor Telemetry interrupts Stations

27. “CMOP: Transforming Ocean Exploration” 27 Hindcast skill assessment (sandi; salinity; IOA) tide

28. “CMOP: Transforming Ocean Exploration” 28 Hindcast skill assessment (sandi; salinity; correlation) tide Correlation skill

29. “CMOP: Transforming Ocean Exploration” 29 CR context and issues Climate forcing Pacific Decadal Oscillation & ENSO (precipitation, ocean climate) Global climate change (sea level rise, snow pack) Q (m 3 /s) 1997 2001 2002 W E S N Winter 01 EW N S Summer 01 E courtesy J. Barth Barnes et al. 1972

30. “CMOP: Transforming Ocean Exploration” 30 Selected E-GRs

31. “CMOP: Transforming Ocean Exploration” 31 System response to forcing: estuary am169 Salinity (psu) Tide range (m) Q (m 3 /s) Salinity intrusion

32. “CMOP: Transforming Ocean Exploration” 32 CR open benchmark Similar to NOAA’s Delaware Bay “model evaluation environment”, in that it enables cross-model comparisons Distinct in estuary type (river-dominated estuary) and philosophy Enable continuous enhancement of multiple models and exploration of diverse modeling strategies Maximize value-added expertise of model developers/expert users, while minimizing their time investment Dynamic timeframes (blending controlled hindcasts with continuous blind forecasts) Focus on unstructured grid models Implementation phases –CMOP-driven SELFE pilot (on-going) –CMOP-assisted pilots for other lead models with by-invitation participation of the respective developers / expert users (a ~12 month effort) –Open to community (early 2010) and consider exporting (2011) Enablers –CMOP’s SATURN modeling system & Rapid Deployment Forecasting System –OpenDAP-CF standards for unstructured grid models (synergistic effort led by Rich Signell, with participation of at least the FVCOM, ADCIRC, SELFE, ELCIRC communities)

33. “CMOP: Transforming Ocean Exploration” 33 Planning

34. “CMOP: Transforming Ocean Exploration” 34 Code registration Code registration Registration of modeling strategy Reference static benchmark Refine modeling strategy All static benchmarks ? ? ? ? Forecast benchmark ? ? Simulation databases Operational forecasts Scenario simulations Scenario simulations ? ? ? ? ? ?