Application and improvement of Ensemble Optimal Interpolation on Regional Ocean Modeling System (ROMS)

Transcription

1 Application and improvement of Ensemble Optimal Interpolation on Regional Ocean Modeling System (ROMS) Zhaoyi Wang Guokun Lyu, Hui Wang, Jiang Zhu, Guimei Liu et al. National Marine Environmental Forecasting Center (NMEFC) Key Laboratory of Research on Marine Hazards Forecasting (LoMF) State Oceanic Administration (SOA) Institute of Atmospheric Physics, Chinese Academy of Sciences(IAP/CAS)

2 Outline Background Data assimilation scheme Operational forecasting Conclusion

3 Background Ensemble Optimal Interpolation (EnOI), an approximation of Ensemble Kalman Filter (EnKF), provides a very computationally efficient method. EnOI has been used to assimilate altimetry and Argo profiles. EnOI also yields more consistent improvements, even in areas where there are large model errors, than 3DVAR during assimilation of altimetry data. EnOI has the advantage of high efficient and good performance which is suitable for operational forecasting system. Aims: (1)Develop a suitable EnOI scheme for ROMS; (2)Assimilate SSH data into operational forecasting system.

4 Background 1. NwPM 2. ECSM 3. SCSM 4. BhSM Horizontall 1/20 o 1/30 o 1/30 o 1/60 o EnOI Vertical a f ' ' T T ' ' T T T 1 Atmosphe A A H ( HA A H ) ( d GFS/CGOGS-wind/NMEFC-WRF re Forcing Tidal N/A TPOX TPOX TPOX Assimilatio n Scheme 3DVAR 1 SST J ( x xb ) 2 OB S SSH T/S 3DVAR EnOI T 1 ( x x b EnOI 1 MGDSST ) ( H( x) y) 2 N/A 1 Jason-1 & Jason-2, Cryosat, SARAL and HY2 In-situ temperature and salinity profiles T H ) ( H( x) y)

5 2. Data assimilation scheme

6 Experiment setting Resolution: 1/ o 10 1/ o 10 Grids: (1)spin-up for 10 years (2)CTRL Qscat +Oaflux (3)AGE With age error (4)NoAGE Without age error Topography of the model region and observations used :

7 Parameter Setting Slide Ensemble members : model history output (at 00:00) in an interval of five days from 2000 to 2008 as an integer ensemble libs; thirty days before and after assimilate day defined as sampling range period; select the ensemble memebers of every year in the same period from the libs as ensemble member for the present assimilation.

8 Localization radius Correlation of aviso sla, reference point(black dot), colorbar represent correlation; dash line represent circle with radius 250 km

9 Localization radius The original (a, b, c, d) and localized (e, f, g, h) ensemble-based correlation between sea surface height and velocity components at two reference locations, denoted by the black starts. Radius:250km(as the red box shows)

10 Point experiment One point data assimilation experiment,observation>simulation; (a) SLA increment ; (b) u-increment ; (c) v-increment; (d) SLA+uv increment; (e) Temperature increment; (f) salinity increment

11 Vertical performance Vertical velocity innovation after assimilation: Left top: model result Right top: assimilation result Left bottom: V component innovation

12 Temporal evolution CTRL : 10.57cm AGE : 7.01cm NoAGE: 6.70cm Temporal evolution of SLA RMSE computed with the reanalysis field for the control run (blue line), AGE run (red line), NoAGE run (black line), and observation errors for the AGE run (black dotted)

13 Comparison with surface drifters Feb 03 Feb 13 Mar 06 CTRL AGE

14 Comparison with floats The RMSE of temperature (left) and salinity (right) for the control, AGE, and NoAGE runs for the upper 1000 m. For temperature, the multivariate properties of data assimilation lead to an improvement for the upper 1000 m. For salinity, RMSE decreased for the upper 200 m,.

15 Comparison with AVISO Standard deviation of sea level (a) AVISO (b) AGE (c)ctrl (d)noage

16 3. Operational forecasting

17 Data Assimilation of SLA Method: EnOI Period: 2013/01/01 ~ now Observation data : SLA from Jason1/2, HY2, Altika, Cryosat2. Assimilation cycle: 7 Days

18 Validation at 2016/9/24 Observation Before DA After DA BDA (cm) ADA (cm) ME AE RMSE

19 4. Conclusion

20 4. Conclusion Based on EnOI data assimilation method, the altimetry data has been assimilated into operational forecasting system. Comparison with observation (buoy / section investigation) and other modeled results, the simulated horizontal and vertical structures are good agreement with these data. Ongoing work, the 3DVAR method is used to assimilated SLA, which will be used to compare with the performance of EnOI.

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