Baptista, Y. Zhang, G. Law, J. Needoba, N. Hyde, S. Frolov, P. Turner, M. Wilkin, C. Seaton,

1. Near real-time predictions of salinity intrusion in a river-dominated estuary: tales and implications of a challenging cruise 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. Outline The “mature” observatory The “inconvenient” cruise The short term “fix” Jul 2008 since 1996 Jul 2008 “retrospective “analysis Open benchmark Skill metrics Aug-Sep 2008 Sep 2008 Sep 2008

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. SATURN: an end-to-end observatory Observation network Cyber-infrastructure Researchers Daily forecasts Educators Simulation databases Students Scenarios Managers Network optimizations … Stakeholders Modeling system

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

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. 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. Blind retrospective cruise analysis – estuary Salinity LMER - observations SELFE simulation Cruise data courtesy D. Jay psu psu … shows ability to represent complex and episodic features June 1999

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

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

11. Forecast skill: prediction of plume location Cruise data courtesy B. Crump

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. SIL: difficult to measure … (a) Data collected by David Jay on LMER and NOAA cruises Chawla, Jay, Baptista, Wilkin and Seaton, CSR 2008

14. … and difficult to simulate (forecast; fDB16; July 13) 07:23 08:41 09:00 09:32 10:09 Cruise data courtesy J. Needoba

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. Grid refinement Refined grid (nchannel) grays cbnc3 mottb fDB16

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

18. CMOP July 2008 cruise: Real-time forecast July 17 2008 09:59 fDB16 DA nchannel da

19. Salinity at challenging stations (forecasts, July 16-17) mottb fDB16 nchannel DA cbnc3 nchannel da nchannel DA fDB16

20. Retrospective analysis

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

22. “hires” hindcasts (eliot; Oct 2004) DB16 DB16 hires t=30sec hires t=30sec

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

24. Forecast (grays; Sep 15-16 2008) fDB16 RMSE= 5.2 psu Salinity ` fhires; t=20sec RMSE= 1.6 psu

25. Definition of Skill Assessment metrics See : http://www.ccalmr.ogi.edu/~cseaton/tmp/dec06/pub/index_page.html

26. Forecast skill assessment (fhires; Sep 15-16, 2008) Correlation skill IOA Biofouled sensor Degraded sensor Biofouled sensor Stations RMSE N Telemetry interrupts

27. Hindcast skill assessment (sandi; salinity; IOA) tide

28. Hindcast skill assessment (sandi; salinity; correlation) tide Correlation skill

29. CR context and issues 1997 Q (m3/s) 2002 2001 • Climate forcing • Pacific Decadal Oscillation & ENSO (precipitation, ocean climate) • Global climate change • (sea level rise, snow pack) Winter 01 courtesy J. Barth N W E Barnes et al. 1972 S N E W E S Summer 01

30. Selected E-GRs

31. System response to forcing: estuary Salinity (psu) Tide range (m) Q (m3/s) Salinity intrusion am169

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. Planning

34. Code registration Registration of modeling strategy Reference static benchmark ? Refine modeling strategy All static benchmarks Operational forecasts ? Simulation databases ? ? Scenario simulations ? ? Forecast benchmark