ROMS California modeling system Horizontal resolution: 3.3 km

Vertical resolution: 40 sigma layers

Atmospheric forcing: Daily 5-km NAM

00 UTC forecasts (NOAA/NCEP)

Data Assimilation: multi-scale 3DVAR (satellite SST and SSH, SIO glider/Argo T/S profiles,

HF radar surface currents, ship SSTs, M1 mooring T/S)

Real-time Nowcasts: every 6 hours -

03, 09,15, 21 UTC: Jan 2009 - present.

With CoSINE biogeochemistry:

Sept 2013 – present.

Forecasts: Daily 72 hour forecast

from 03 UTC.

CeNCOOS

SCCOOS

Assimilation Impact of Physical Data on the California Coastal Ocean Circulation and Biogeochemistry

Yi Chao, Remote Sensing Solutions (RSS)/UCLA; John D. Farrara, RSS; Fei Chai, University of Maine; Hongchun Zhang, UCLA

The Multi-Scale Three-Dimensional Variational Data Assimilation Scheme

• Sparse Vertical Profile sampling

• High Resolution Remote Sensing

• High Resolution Coastal Ocean Model

Li, Z., J. C. McWilliams, K. Ide, J. D. Farrara (2015), A Multiscale Variational Data Assimilation Scheme: Formulation and Illustration. Mon. Wea. Rev., 143, 3804–3822. doi: http://dx.doi.org/10.1175/MWR-D-14-00384.1. Li, Z., J. C. McWilliams, K. Ide and J. D. Farrara (2015), Coastal Ocean data assimilation using a multi-scale three-dimensional variational scheme. Ocean Dynamics, 65, 1001-1015. doi: 10.1007/s10236-015-0850-x.

• Multi-scale 3DVAR scheme uses partitioned cost functions for two scales, which are

solved sequentially (large scale first)

• Uses multi-decorrelation length scales (of approximately 65 and 10 km) to

construct background error covariances for the two scales

• Effectiveness of the assimilation of both sparse and high resolution observations is

improved compared to single-scale 3DVAR

Validation of model performance: Assimilated Data ROMS vs. IR satellite monthly mean sea surface temperatures: Mean seasonal cycle

http://west.rssoffice.com/ca_roms_valid_other?variable=IRsst

Daily means

Spatial Correlation

RMS

Monthly means

RMS

Validation of model performance: Assimilated Data

M1 mooring interannual variability (temperature)

Observed

ROMS

Impact of Data Assimilation: Temperature Profiles Assimilated Data Independent Data

Bias: -0.01oC RMS: 0.51oC Corr: 0.98

Bias: +0.03oC RMS: 0.92oC Corr: 0.96

Impact of Data Assimilation: Salinity Profiles

Independent Data

Bias: +0.001 PSU RMS: 0.086 PSU Corr: 0.97

Assimilated Data

Bias: -0.05 PSU RMS: 0.17 PSU Corr: 0.90

Assimilated Data, HF Radar Surface Currents

Daily means

Monthly means

Spatial Correlation

RMS

RMS

No DA

RMS

Impact of DA: Monthly Mean Sea Surface Temperatures, ROMS - Satellite IR SSTs

July No DA

July DA

Impact of DA: Monthly Means, July, ROMS DA – ROMS No DA

July 100m

July Surface

July 100m

July Surface

Impact of DA: Monthly Mean Meridional Velocities, ROMS DA – ROMS No DA

July 100m

July Surface

OBSV ROMS 3km

ROMS 300m

ROMS 1km

Impact of Model Resolution on the assimilation of SST

5 Apr 2016

NO3 SiO4 S1 S2

1 October 2013: Nutrients and Phytoplankton Surface

Cross-Section (0-500m)

Summary A real-time, data-assimilating regional ocean modeling system for the California coastal ocean has been developed, deployed and validated.

Performance validation revealed very good agreement of model nowcasts with assimilated data (SST, SSH, surface currents, T / S profiles)

and good agreement with independent data.

RMS differences in T / S versus independent observations are about twice the RMS differences versus assimilated observations

Upwelling signatures in T, S and meridional currents improved with data assimilation

Impact of Data Assimilation

End

Validation of model performance: Independent Data

Glider-derived depth-average (~0-500m) currents

DJF JJA

A schematic diagram of the 13-component CoSiNE biogeochemistry model.

CA ROMS

Summary

A real-time, data-assimilating regional ocean modeling system for the California coastal ocean has been developed, deployed and validated.

Performance validation revealed excellent agreement of model nowcasts with assimilated data (SST, SSH, surface currents, T / S profiles)

and very good agreement with independent data.

Interannual variability at the M1 mooring (Monterey Bay), including the 2014-15 warming, is realistically reproduced.

Comparison of independent (SIO glider) depth-average currents with model currents showed that the flow patterns associated with

the California current and undercurrent/Davidson current systems are qualitatively reproduced by the model.

ROMS California modeling system

1

9

Lateral Boundary Forcing: global 1/12o HYCOM forecast

Tidal forcing: TPXO.6, 0.25o resolution, 8 major diurnal and semi-

diurnal constituents

Atmospheric Forcing: NCEP/WRF 5km, surface air temp / relative

humidity plus bulk formula to obtain surface latent and sensible heat

fluxes, 10 m winds, net surface solar / terrestrial radiation,

precipitation – evaporation for freshwater flux. Wind stress from 10m

winds using Large and Pond (1982).

Data Availability: OpenDAP, ftp (only most recent data)

Website: http://west.rssoffice.com/ca_roms

Computing: In-house 128-processor cluster / Google cloud backup

Nowcast/Forecast data normally available 8 to 10 hours behind real-time.

Validation of model performance: Assimilated Data, Real-time

http://west.rssoffice.com/ca_roms_valid_prof?variable=tscat

21

Linking California coastal ocean model with San Francisco Bay/Estuary and the lower Sacramento River

Offline 3-km 1-km………………..10-m

Golden Gate

ROMS Unstructured grid SCHISM

HI34A-1803 Towards a real-time forecasting system for the San Francisco bay/estuary and river delta Wednesday, February 24, 2016 04:00 PM - 06:00 PM

Please see our poster:

Impact of DA: Monthly Mean Sea Surface Temperatures, ROMS - Satellite IR SSTs

July 2012 No DA

July 2012 DA

Impact of DA: Monthly Mean Sea Surface Temperatures, ROMS - Satellite IR SSTs

March 2013 No DA

July 2012 No DA

March 2013 DA

July 2012 DA

Impact of DA: Monthly Mean Salinities, ROMS DA – ROMS No DA

July 2012 100m

July 2012 Surface