1. Coastal Ocean Observation Lab http://marine.rutgers.edu/cool John Wilkin, Hernan Arango, John Evans Naomi Fleming, Gregg Foti, Julia Levin, Javier Zavala-Garay, Gordon Zhang Regional Ocean Prediction Scott Glenn, Oscar Schofield, Bob Chant Josh Kohut, Hugh Roarty, Josh Graver Coastal Ocean Observation Lab Janice McDonnell Education and Outreach Coastal Observation and Prediction Sponsors: Regional Ocean Prediction http://marine.rutgers.edu/po Education & Outreach http://coolclassroom.org Coastal Ocean Modeling, Observation and Prediction in the Mid-Atlantic Bight

2. Coastal Ocean Observation Lab http://marine.rutgers.edu/cool/sw06/sw06.htm Integrating Ocean Observing and Modeling Systems for SW06 Analysis and Forecasting Regional Ocean Modeling and Prediction http://marine.rutgers.edu/po/sw06 gliders and CODAR satellite SST, bio-optics high-res regional WRF atmospheric forecast SW06 ship-based obs. ROMS model embedded in NCOM or climatology WRF and NCEP forcing + rivers 2-day cycle IS4DVAR assimilation Real-time data and analysis to ships via ExView and HiSeasNet glider, CODAR, satellite, WRF Daily Bulletin NCOM and ROMS/assimilation 2-day forecasts Model-based re-analysis of submesoscale ocean state ROMS/IS4DVAR assimilation: plus CODAR, Scanfish, moorings, CTDs … high-res nesting in SW06 center ensemble simulations; uncertainty instability, sensitivity analysis, optimal observations

3. 62/62: 62 moorings deployed and recovered

6. IS4DVAR assimilation SW06: Shallow Water Acoustics 2006: –ROMS model configuration –Assimilation data, IS4DVAR configuration, real-time performance –Issues: initialization, boundary conditions / nesting, background error covariance scales, unconstrained shelf/slope front transport Next steps: –SW06 reanalysis algorithmic tuning, more data, higher resolution, nesting –ensemble simulations forecast and analysis uncertainty and predictability –observing system design

7. Adjoint model integration is forced by the model-data error x b = model state at end of previous cycle, and 1 st guess for the next forecast In 4D-VAR assimilation the adjoint model computes the sensitivity of the initial conditions to mis- matches between model and data A descent algorithm uses this sensitivity to iteratively update the initial conditions, x a, to minimize J b +  (J o ) Observations minus Previous Forecast xx 0 1 2 3 4 time

8. Adjoint surface temperature states at different time during a three-day period. Initial adjoint forcing area is surrounded by the black frame. Top: southward wind. Bottom: northward wind.

9. Basic IS4DVAR * procedure * Incremental Strong Constraint 4-Dimensional Variational Assimilation (1)Choose an (2)Integrate NLROMS and save (a) Choose a (b) Integrate TLROMS and compute J (c) Integrate ADROMS to yield (d) Compute (e) Use a descent algorithm to determine a “down gradient” correction to that will yield a smaller value of J (f) Back to (b) until converged (3) Compute new and back to (2) until converged The Devil is in the Details

10. Harvard Box (100kmx100km) ROMS LATTE outer boundary ROMS SW06 outer boundary SW06 Model Domains

11. ROMS SW06 5-km grid (coarse) for IS4DVAR testing Forcing: NCEP-NAM and WRF USGS Hudson River OTPS tides Open boundaries NCOM and L&G climatology 2-day assimilation cycle length scales for background error covariance: 20-km horizontal 5-m vertical Data: gliders, CTDs, Scanfish, XBTs, ship thermo-salinograph, daily best-SST composite, AVISO SSH

12. ROMS SW06 real-time observations SW06 ExView Google Earth movie

16. Salt 5mSalt 30mTemp 30m

19. Forecast skill in 2-day interval when initial conditions are adjusted using IS4DVAR Simple forecast: No data assimilation

20. Forecast Skill Observations: Glider data Lag=0: Comparison with data used for assimilation Lag=2: Comparison of 2 day forecast with data Lag=8 Comparison of 8 day “forecast” with data

21. Day = 215 Distribution of errors in forecast for lag = 2 days at day 215

22. What can be done to improve the initial conditions bias?

23. IS4DVAR for initial conditions estimation SW06 initial conditions (climatology) were clearly biased in summer 2006 –extreme Hudson discharge in July Assume early glider observations are indicative of shelf-wide conditions –make an implicit long length scale correlation assumption –Introduce ‘bogus’ data to assimilation data set

26. Now what ? SW06 reanalysis of sub-mesoscale ocean state –IS4DVAR algorithmic tuning forecast cycle length; background error covariance –More data CODAR, moorings (u,T,S), shipboard ADCP, drifters … –Higher resolution / nesting –Ensemble simulations forecast skill; quantify predictability; analysis uncertainty Mid-Atlantic Bight wide COMOP –Address open boundary and nesting issues –Deep ocean / shelf sea coupling –Observing system design –Physics information in the transport of optics fields

27. Mixing of the Hudson and Raritan Rivers Phytoplankton Absorption Detritus Absorption SeaWiFS chlorophyll Visible RGB SST