Real-Time Oregon Coastal Ocean Forecast System Alexander Kurapov, S. Erofeeva, P. Yu, G. D. Egbert, J. S. Allen, P. T. Strub, P. M. Kosro, D. Foley Everyday updates of 3-day forecasts of u,v,T,S NOAA-CIOSS NOAA-IOOS ONR NSF

Requests/interest: - NOAA Hazmat, - EPA, - fishermen

Model details: Regional Ocean Modeling System (ROMS) 3-km horizontal resolution, 40 vertical layers Atmospheric fluxes: - NOAA –NAM forecasts Boundary conditions: -Until 8/2/2009: NCOM-CCS forecasts (Shulman et al., NRL) -Since 8/2/2009: NCOM-CCS climatology - Since 8/22/2010: assimilation of HF radar surface current observations, hourly GOES SST. - Ongoing: assimilation of alongtrack SSH (shown: forecast SST & SSH, Sept. 20, 2010) 128W 126W

Data assimilation Model + Data = Improved Starting Conditions for Forecasts Assim (TL&ADJ AVRORA) forecast (NL ROMS) forecast from previous window analysis forecast 08/22 08/25 08/28 08/31 09/02 09/05 09/08 09/11 09/14 09/17 Variational Assimilation: -Tangent linear & Adjoint AVRORA system [DAO, 2009, JGR 2010 in review] : Improve initial conditions in the recent past (beginning of the 3 day window); 6-km res. AVRORA: flexibility in the choice of model error covar., data functionals - Run nonlinear ROMS using improved conditions; 3-km res.

Hindcast research Impact of HF radar surface current assimilation: - geometry of the SST front is improved Free-run Analysis Forecast (SST is not assimilated in this case) (HF radar data: P M Kosro)

Assimilation of radial component data could be more preferable than mapped data: - Radial data are of more uniform quality than mapped data ( e.g., because the problem of geometric dilution of precision near coast is avoided ) - In areas where data coverage is provided by only one radar, the radial component data will still provide a useful constraint on the model - The DA model is used as a tool to produce (u,v) maps (including forecasts) Data coverage from 4 long-range HF radars

Assimilation of radial component data, directly in the model, has yielded (u,v) maps that are close to the original maps, obtained using traditional objective mapping Free-run model DA w/ mapped HF DA w/ radial HF Assimilation of radial component data: similar effect on velocity and SST GOES SST, HF radar map, 7/23/08

Assimilate SSH slope, not SSH (and thus exclude alongtrack SSH mean from assimilation) The cost functional (minimized in each window): assumed covariance of errors in the initial conditions Operator matching model to data “background” initial conditions (the best prior estimate)

Impact of alongtrack SSH assimilation: -geometry of the SST front is improved [Kurapov et al., 2010, JGR - in review] SST maps: Sept 25, 2005 Free-run 6-km ROMSDA (analysis) SST daily composite free-run AVISO

SSH DA: Improvement in the area-averaged SST model-data correlation free-run model analysis forecast

SSH assimilation: effect on subsurface temperature CTD data at 45N (Peterson) no DA DA: SSH reduced thermocline depth

 T=1 o C  v=5 cm/s Temp. Meridional Velocity Extend the cross-shore section farther west: an outer front is apparent in the DA solution at N free-run model DA: SSH DA SSH&SST outer front (emerged in part due to SSH assim.) subsurface temperature front (in thermal wind balance) stronger temperature stratification inshore of the front Shown: cross-shore vertical sections of T and meridional vel. at 45N, 29 Aug 2005

section at 126W DA: SSH no DA DA: SSH+SST Grey: <10 0 C,  T=1 0 C SSH assimilation changes subsurface slope of isoterms in the meridional section: (need subsurface hydrographic data to verify)

A series of analyses is discontinuous:

Volume-integrated heat equation: advective flux through side boundaries atmospheric heat flux series of instantaneous DA corrections at times t k To present these terms, we average the terms over 6 day intervals, each centered on the time of correction Gray box = control volume

Time-series of volume-integrated terms in the heat equation (scaled to obtain units of W/m 2 ) free-run model DA: SSH DA: SSH + SST - Variability in tendency is dominated by advection -DA correction term is comparable in magnitude to other terms - Correction term: DA SSH – cooling DA SSH+SST closer to 0 on average

Variational assimilation: dynamically based interpolation in space and time Hourly GOES SST composites DA: Alongtrack SSH + GOES SST For verification: Multisatellite blended SST (D. Foley, CoastWatch) SST (color), SSH (contours) No SST DA:

Planned in the real-time model: assimilation of SST from several satellites: use data redundancy to remove noise in data MODIS AMSRE AVHRR0.1 AVHRR blend sat free run forecast analysis Satellite and forecast model SST 12 Sept. 2010

Process studies in the CTZ [Koch et al., JGR, 2010] Left: surf. ageostros. currents (vectors) and geostrophic vorticity (color): jets in CTZ affect convergence of currents near surface Right: max TKE in the upper 25 m (color) and SSH (contours) (increased TKE in the area of the jet stream, comparable to that in the area of intensified wind stress) 10 -3

Higher-resolution process studies: [J. Osborne – see poster] 1-km resolution ROMS modeling wind- and tidally-driven circulation in combination -intermittency of internal tide - internal tide energetics -effects of small scale, high-freq. variability on the cross-shore transport (volume, heat)

SUMMARY: -The data assimilation component has been added to the pilot real- time Oregon coastal ocean model (to be released to NANOOS users in the near future) - Observations of surface currents, satellite alongtrack SSH, SST all positively impact the solution (as verified against unassimilated data) - It is often more convenient to assimilate “original” data (HF radar radial velocity component, alongtrack SSH, hourly SST) than blended products - Real-time assimilation experiments encourage us to address new exploratory issues in coastal ocean modeling and DA ( e.g., Columbia R. effect, subsurface circulation, heat budget in the coastal ocean, b/t transports ) - Process studies reveal nontrivial dynamics over the shelf and in the CTZ: provide guidance to real-time forecasters