Data Assimilation of Argo Profiles in Northwest Pacific Yun LI National Marine Environmental Forecasting Center, Beijing

Transcription

1 Data Assimilation of Argo Profiles in Northwest Pacific Yun LI National Marine Environmental Forecasting Center, Beijing

2 National Marine Environmental Forecasting Center Established in 1965 Receiving data by radio wave

3 NMEFC issues marine hazards and environmental forecast products, including storm surge, huge wave, sea Ice, El-Nino, tsunami, sea temperature, current, salinity, Oil Spill, Red(Green) Tide, search and rescue Forecasting products Warnings Environmental Forecast Marine weather Marine emergency Storm surge Wave Tsuna mi Sea ice SST Current Red tide Green tide ENSO &Climate Leakage Search and rescue Oil spill Storm surge Tsunami Sea Ice Tourist Destination Red tide Oil spill Current Seasonal ENSO forecast

4 Contents Background Model configuration &data assimilation Model Validation Discussion Conclusion

5 Chinese Operational Hydrological Forecasting System 1. Northwest Pacific 2. South China Sea 3. East China Sea 4. Bohai Sea 1 OGCM Horizontal Res. ROMS 1/20 o 1/30 o 1/30 o 1/60 o 4 2 Vertical Res. 30 levels 36 levels 30 levels 30 levels Atmosphere Forcing Assimilation Scheme 3D- VAR GFS/CGOGS-wind/NMEFC-WRF Nudging/EnOI Nudging SST MGDSST 3 OB S SSH Other s Jason-1 & Jason-2, Cryosat, SARAL and HY2 In-situ temperature and salinity profiles, e.g. Argos, XBTs Products Forecast Range Update Frequency Temperature, Salinity, Currents 5 days Daily

6 02 Model configuration & data assimilation

7 2.1 Model configuration (NwPM) Region Resolution Time Steps Boundary Condition 8 o S~52 o N;99 o E~160 o E 1/20 o 1/20 o 30 σ-levels Internal 180s;External 6s MOM4 global ocean forecasting system Hindcast Forcing Clim Data Topo Data CFSR 4 daily data SODA climatology data GEBCO(0.5' 0.5'), with observation data corrected in East China Sea

8 2.2 Data for assimilation All these datasets are quality-controlled and disseminated by Ifremer. From January 2010 to December 2010, there are 9101 ( ) T/S profiles (observations) in our model region. maximum thresholds for misfits are set up, equal to 5.0 degc and 0.5 for temperature and salinity observations, respectively.

9 2.3 Assimilation algorithms ) ) ( ( ) ) ( ( 2 1 ) ( ) ( y x H y x H x x x x J T b T b d) ( d) ( ' 1 ' 1 x H x H x x J T T d) V ( d) V ( ' -1 ' v H v H v v J J J T o b linearized x Vv T B VV

10 2.4 Background-error covariance matrix V V h V v Vh Assumes Gaussian shape for the correlation V v E 1/2 Columns of E contain multivariate eigenvectors and is a diagonal matrix containing the associated eigenvalues. (a) refers to sea level error (b) to temperature at surface (c) to salinity at surface (d) represents the yearly mean eigenvalues of all EOFs, wherein, the first and second EOFs account for 33.7% and 15.0% of the background-error matrix, respectively.

11 03 Model Validation

12 3.1 Comparison with profiles Vertical distribution of T/S errors The AF run significantly reduce the simulation bias from more than 0.70 C (0.02) to less than 0.2 C (0.01) at depths below 300 m; For temperature, improvement rate (RMSE decrease) after assimilation can reach up to 50% at the level of ~10m and larger than 30% at water shallower than 600m. For salinity, RMSE decreased by ~0.040 for the upper 1000 m, but slight deviation of ~0.002 appears from 1000 m to 1500 m.

13 3.2 Comparison with profiles Temporal evolution of T/S RMSE AF SF AF SF For temperature, the RMSE decreases from C in the SF run to C in the AF run, i.e. a 37.21% reduction occurs. For salinity, the RMSE decreases from in the SF run to in the AF run, i.e. a 27.98% reductions.

14 3.3 Comparison with satellite data (OISST) Sea Surface Temperature the SST bias from 1.21 C to 1.07 C in most of Northwest Pacific, especially tropical zone and south and east of Japan Island.

15 3.4 Comparison with reprocessed datasets (EN4.0) Spatial distribution T/S bias Temperature Salinity For temperature, the reduction of SST bias is significantly and, on average, from 1.23 C to 1.02 C For salinity, the reduction of SSS bias is significantly and, on average, from to 0.308

16 3.5 Comparison with reprocessed datasets Section distribution T/S at 137 E Temperature Salinity For temperature (left), the vertical distribution of AF run has a structure similar to the reprocessed dataset. The isotherm of AF has the same depth compared with EN For salinity (right), the AF run has a good perform slightly with the reprocessed dataset.

17 04 Discussion

18 Potential of assimilation system Discussion AF run CTRL run New scheme : a) b) c) : a) b) : a) b) c) b) For temperature, the RMSE for temperature is 0.58 C for AF, 0.60 C for bda, 0.40 C for ada and 0.68 C for CTRL. For salinity, the RMSE for salinity is for AF, for bda, for ada and for CTRL. On December , the RMSE decrease from 0.97 C (0.095) in the CTRL run to 0.73 C (0.081) in the AF run, 0.75 C (0.077) in the bda run and 0.46 C (0.063) in the ada run.

19 Discussion Operational forecast experiment For temperature, on average, the RMSE is C, C, C, C and C for 1st day, 2nd day, 3rd day, 4th day and 5th day forecast, respectively, i.e. always smaller than the RMSE of CTRL run, equal to C; For salinity, on average, the RMSE is , , , and for for the five forecast days, respectively, i.e. performing better than the CTRL run (with a RMSE of ).

20 05 Conclusion

21 Conclusion After assimilating the Argo profiles, the daily averaged temperature (salinity) RMSE decreases from C (0.098) to C (0.071) in the assimilation experiment throughout the Northwest Pacific Ocean The data assimilation can provide a beneficial effect for the temperature and salinity at the surface in the model region. the assimilation system can improve~0.46 C for temperate and ~0.06 for salinity. 03 Through 5-day forecast experiment, 3DVAR initialization proves effective in improving the shortterm predictability in the North-West Pacific Ocean. The 3DVAR assimilation system for ROMS can be used as a suitable assimilation scheme for operational forecasting.

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