1. Rutgers Ocean Modeling Group ROMS 4DVar data assimilation Mid-Atlantic Bight and Gulf of Maine John Wilkin with Julia Levin, Javier Zavala-Garay, Hernan Arango, Eli Hunter, David Robertson, Naomi Fleming MARACOOS Modeling Meeting Washington DC July 22-23, 2013

2. Present ESPRESSO real-time system
New DOPPIO real-time system (with 2-way nesting)

3. Work flow for real-time ESPreSSO ROMS 4DVar
Analysis interval is 00:00 – 24:00 UTC
Input pre-processing starts 01:00 EST
Input preprocessing completes approximately 05:00 EST
4DVAR analysis completes approx 08:00 EST
24-hour analysis is followed by 72-hour forecast using NCEP NAM 0Z cycle from NOMADS GDS at 02:30 UT (10:30 pm EST)
Forecast complete and transferred to THREDDS by 09:00 EST
Effective forecast is ~ 60 hours
ESPreSSO
We overlap analysis cycles, performing a new analysis and new forecast every day
*Experimental System for Predicting Shelf and Slope Optics

4. Work flow for real-time ESPreSSO ROMS 4DVar
Data used [… real-time SOURCE]
72-hour forecast NAM-WRF 0Z cycle at 2 am EST [NCEP NOMADS]
RU regional CODAR product – hourly: 4-hour latency delay [RU TDS]
RU glider T,S when available (seldom) (~ 1 hour delay) [RU TDS]
USGS daily average flow available 11:00 EST [USGS waterdata]
AVHRR IR passes 6-8 per day (~ 2 hour delay) [MARACOOS TDS]
REMSS MW-IR blended SST daily average [PO-DAAC]
HYCOM NCODA 7-day forecast updated daily [NRL]
Jason-2 along-track SLA (4 to 16 hour delay for OGDR) [RADS]
SOOP XBT/CTD, Argo floats, NDBC buoys on GTS [OSMC ERDDAP]

5. Work flow for real-time ESPreSSO ROMS 4DVar
Input pre-processing
RU CODAR de-tided (harmonic analysis) and binned to 5 km
“re-tided” with ESPReSSO harmonics
variance within bin & OI combiner expected u_err (GDOP) used for QC
RU glider T,S averaged to ~5 km horizontal and 5 m vertical bins
GTS SOOP XBT and Argo – binned and QC
AVHRR IR individual passes 6-8 per day
U. Del cloud mask selected QC flags; bin to 5 km resolution
Jason-2 along-track 1 Hz with coastal corrections in RADS
MDT from 4DVAR on “mean model” (climatology 3D T,S, uCODAR, τwind)
USGS daily river flow is scaled to account for un-gauged watershed

6. The data …

7. Sub-surface T/S analysis and forecast skill
In situ T and S observations are not assimilated so offer independent skill assessment
There is a sizeable archive of observatory data from CTD, gliders and XBTs for 2006 (SW06) and 2007
days since 01-Jan-2006

8. Temperature Forward model Forward model after bias removal
Analysis/forecast skill with respect to subsurface OBS that are NOT assimilated
Temperature
Forward model
Forward model after bias removal
Data assimilation analysis/hindcast
2-day forecast
4-day forecast

9. Multi-model skill comparison: T/S
CRMS (normalized)
BIAS (normalized)
MARACOOS AUGV and NMFS EcoMon CTD data in 2010 and 2011

10. Multi-model skill comparison: velocity

11. Bias removal Mean Dynamic Topography
4D-Var applied to climatology of T/S, mean surface fluxes, & mean velocity obs (CODAR, moorings, vessel ADCP)

13. Summary
Rutgers ROMS 4DVAR uses all available data from a modern coastal ocean observing system
satellites, HF-radar, moorings, AUV (glider, Argo …), XBT/CTD;
IR SST individual passes work best – model dynamics create the composite
more and diverse data is better
climatology assimilation: removes OBC and MDT bias; unbiased background state in Tangent-Linear model gives correct dynamic modes and adjoint-based increments
Useful skill for real-time applications
4 days for temperature and salinity; 1-2 days for velocity
improves short-term ecosystem prediction
observing system operation … glider path planning
Variational methods for observing system design
adjoint sensitivity and representer-based observing system design (see W. Zhang et al. papers in Ocean Modelling, 2010); observation impact analysis (see A. Moore et al. papers in Prog. Oceanog. 2011)

14. Future DOPPIO model domain configuration for MAB + Gulf of Maine
Same 4DVAR methodology as ESPRESSO
Evaluate GOOS/GODAE and IOOS-NB products for real-time OBC
Nest in Curchitser group NWA 50-year simulations for reanalysis OBC
Repeat climatology 4DVAR for MDT and OBC bias removal
Include waves in forcing data and model physics
Local shelf and estuarine nests (2-way ROMS)
USECOS nitrogen/carbon cycle simulations
ROMS 4DVAR weak constraint/dual space formulation
observation space – computationally smaller than model space
W4DVAR Indirect Representer algorithm (Egbert 1994)
W4DPSAS Physical Space Statistical Analysis System (Da Silva 1995)
Adjusts time-varying forcing and boundary conditions, explicitly acknowledges model error, enables posterior analysis of observation impact/sensitivity (and more)