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

Transcription

1 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 Horizontal resolution: 3.3 km Vertical resolution: 40 sigma layers Atmospheric forcing: Daily 5-km NAM 00 UTC forecasts (NOAA/NCEP) ROMS California modeling system 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 present. With CoSINE biogeochemistry: Sept 2013 present. CeNCOOS SCCOOS Forecasts: Daily 72 hour forecast from 03 UTC.

2 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, doi: 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, doi: /s 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

3 Validation of model performance: Assimilated Data ROMS vs. IR satellite monthly mean sea surface temperatures: Mean seasonal cycle Monthly means Spatial Correlation RMS Daily means RMS

4 Validation of model performance: Assimilated Data M1 mooring interannual variability (temperature) Observed ROMS

5 Impact of Data Assimilation: Temperature Profiles Assimilated Data Independent Data Bias: o C RMS: 0.51 o C Corr: 0.98 Bias: o C RMS: 0.92 o C Corr: 0.96

6 Impact of Data Assimilation: Salinity Profiles Assimilated Data Independent Data Bias: PSU RMS: PSU Corr: 0.97 Bias: PSU RMS: 0.17 PSU Corr: 0.90

7 Assimilated Data, HF Radar Surface Currents Monthly means RMS Spatial Correlation Daily means RMS No DA RMS

8 Impact of DA: Monthly Mean Sea Surface Temperatures, ROMS - Satellite IR SSTs July DA July No DA

9 Impact of DA: Monthly Means, July, ROMS DA ROMS No DA July Surface July 100m July Surface July 100m

10 Impact of DA: Monthly Mean Meridional Velocities, ROMS DA ROMS No DA July Surface July 100m

11 Impact of Model Resolution on the assimilation of SST OBSV ROMS 3km 5 Apr 2016 ROMS 1km ROMS 300m

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13 1 October 2013: Nutrients and Phytoplankton Surface NO3 SiO4 S1 S2 Cross-Section (0-500m)

14 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. Impact of Data Assimilation 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

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16 Validation of model performance: Independent Data Glider-derived depth-average (~0-500m) currents DJF JJA

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

18 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 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.

19 ROMS California modeling system Lateral Boundary Forcing: global 1/12 o HYCOM forecast Tidal forcing: TPXO.6, 0.25 o resolution, 8 major diurnal and semidiurnal 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: Computing: In-house 128-processor cluster / Google cloud backup Nowcast/Forecast data normally available 8 to 10 hours behind real-time. 1

20 Validation of model performance: Assimilated Data, Real-time

21 Linking California coastal ocean model with San Francisco Bay/Estuary and the lower Sacramento River Golden Gate ROMS Unstructured grid SCHISM 3-km Offline 1-km..10-m 21 Please see our poster: HI34A-1803 Towards a real-time forecasting system for the San Francisco bay/estuary and river delta Wednesday, February 24, :00 PM - 06:00 PM

22 Impact of DA: Monthly Mean Sea Surface Temperatures, ROMS - Satellite IR SSTs July 2012 DA July 2012 No DA

23 Impact of DA: Monthly Mean Sea Surface Temperatures, ROMS - Satellite IR SSTs March 2013 DA March 2013 No DA July 2012 DA July 2012 No DA

24 Impact of DA: Monthly Mean Salinities, ROMS DA ROMS No DA July 2012 Surface July m