Coastal Ocean Observation Lab marine.rutgers/cool

1. Coastal Ocean Modeling, Observation and Prediction in the Mid-Atlantic Bight 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 Regional Ocean Prediction http://marine.rutgers.edu/po Coastal Ocean Observation Lab http://marine.rutgers.edu/cool Education & Outreach http://coolclassroom.org Coastal Observation and Prediction Sponsors:

2. Real-time data and analysis to ships via ExView and HiSeasNet • glider, CODAR, satellite, WRF Daily Bulletin • NCOM and ROMS/assimilation 2-day forecasts Integrating Ocean Observing and Modeling Systems for SW06 Analysis and Forecasting Coastal Ocean Observation Labhttp://marine.rutgers.edu/cool/sw06/sw06.htm Regional Ocean Modeling and Predictionhttp://marine.rutgers.edu/po/sw06 • ROMS model embedded in NCOM or climatology • WRF and NCEP forcing + rivers • 2-day cycle IS4DVAR assimilation • gliders and CODAR • satellite SST, bio-optics • high-res regional WRF atmospheric forecast • SW06 ship-based obs. • 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. xb= model state at end of previous cycle, and 1st 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, xa, to minimize Jb+ S(Jo) 0 1 2 3 4 time Observations minus Previous Forecast Adjoint model integration is forced by the model-data error dx

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. The Devil is in the Details Basic IS4DVAR* procedure*Incremental Strong Constraint 4-Dimensional Variational Assimilation • Choose an • 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

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

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 5m Salt 30m Temp 30m

19. 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

20. Distribution of errors in forecast for lag = 2 daysat day 215 Day = 215

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

22. 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

25. 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

26. SST Mixing of the Hudson and Raritan Rivers Visible RGB Detritus Absorption PhytoplanktonAbsorption SeaWiFS chlorophyll