1. Estimating and Predicting Ocean Currents in the U.S. coastal oceans John D. Farrara*, Yi Chao, Zhijin Li, Xiaochun Wang*, Hongchun Zhang*, Peggy Li, Quoc Vu NASA Jet Propulsion Laboratory *Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles Ocean Currents and End User Feedback Workshop, May 5-6, 2011, Atlanta 1

2. Data Assimilation Model Products Users Observations Feedback Forecasting Ocean Hindcast/Nowcast/Forecast Ocean observing is rapidly expanding, ocean models are maturing; To what extent can the regional oceans be predicted from synoptic weather (days) to climate (seasons, interannual, decadal) time scales? 2

3. A Portable, Data-Assimilative Coastal Ocean Nowcast/Forecast/Hindcast System - Based on the ROMS regional ocean model A typical configuration has a horizontal resolution of a few km or less, and extends several hundred kilometers offshore Nowcasts typically generated every 6 hours, with a daily 48 or 72 hour forecast - A multi-scale 3DVAR data assimilation scheme Large and small spatial scales separately assimilated Scale-dependent error covariances Scale-dependent dynamic balance constraints Typical data assimilated includes satellite SSTs, HF radar surface currents and glider/mooring temperature and salinity vertical profiles - Tides: Oregon State University global tidal forcing - Atmospheric Forcing: Regional atmospheric models (WRF, NAM) - Ensemble Forecasting methodology - Interactive trajectory tool, real-time execution is fully automated 3

4. U. S. Coastal Regions where the system has been applied 4

5. 15-km5-km1.5-km Multi-level Nested Regional Ocean Modeling System (ROMS) A multi-scale (or “nested”) ROMS modeling approach has been developed in order to simulate the 3D ocean at the spatial scale (e.g., 1-km) measured by satellites and coastal HF radars in a way that is computationally efficient enough to allow real-time operations. 5.

6.  x is obtained by minimizing the Cost Function J = (  x ) T B -1 (  x) + (h  x-  y) T R -1 (h  x-  y) x a = x f +  x 3DVAR Data Assimilation (3-dimensional variational) x: model (with error); x f t+1 =M(x a t ) f: forecast (model alone) a: analysis/nowcast (with data) y: observation (with error); h: map model to data References: Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008a: A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090. Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation schemefor the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002, doi:10.1029/2006JC004042. Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2011: A multi-scale three-dimensional variational data assimilation scheme and its application to coastal oceans. Quart. J. Roy. Meteorol. Soc., submitted. 6

7. 7 3DVAR Data Assimilation With Geostrophic Constraint Min[(X uv o -X uv f ) 2 ] Geostrophic Balance HF Radar Current Obs. Geostrophic vs. Non-geostrophic

8. 8 3DVAR Data Assimilation With Hydrostatic Constraint Min[(X o - X f ) 2 ] Hydrostatic Balance Satellite Altimetric Sea Level Obs. Steric vs non-steric

9. Satellite SST/SSH HF Radar LR- 3DVAR Forecast Smoothed Start HR- 3DVAR Low-Res Increment End Glider/Argo/Mooring Smoothed Multi-Scale 3DVAR Data Assimilation High-Res Increment t+1 Low-Res. Observations High-Res. Observations 9

10. Impact of Surface Current Data Assimilation on Nowcast RMS Correlation ROMS w/o HF radar data ROMS with HF radar data 10

11. Impact of Surface Current Data Assimilation on Forecast ROMS forecast w/o sfc current data Persistence ROMS forecast with sfc current data 11

12. 12 HF Radial Current Data Assimilation Quality Control: STD<10 cm/sec MapError<0.95 JPL/ROMS DAS

13. L0 10km, 40 layers L1 3.3.km, 40 layers L2 1.1km, 40 layers Atmospheric Forcing: L2: UAA, 4km WRF L1, L0: 0.5 o GFS Tides forced on lateral boundary of L0 domain by OSU global tide model output. Gulf of Alaska/Prince William Sound Configuration 13

14. ROMS vs. Independent Data: Drifter Trajectories ROMS Daily Mean Surface Currents for July 26, 2009 Observed Drifter Trajectories – July 25 – 27, 2009 Sound Predictions 2009 Field Experiment, Prince William Sound, Alaska 14

15. July 31 – Aug 3, 2009 July 20 – 26, 2009 July 27 – 30, 2009 ROMS vs. Assimilated Data HF Radar Surface Currents 15

16. ROMS vs. Assimilated Data: HF Radar Surface Currents RMS (cm/s) Spatial Correlation Number of Observations (Light Blue Bars) Julian Day 2008 16

17. . ROMS Ensemble Forecasting: Surface Current Speed Spreads (cm/s) Hour 6Hour 48. 17

18. 18

19. ROMS Forecast Performance vs. Persistence (Spatial correlation / RMS Difference) Forecast FieldROMS Single FcstROMS Ensemble FcstPersistence 24 hr Sfc Currents 0.90 / 7.4 cm/s 0.91 / 7.2 cm/s 0.72 / 14.1 cm/s 48 hr Sfc Currents 0.80 / 10.6 cm/s 0.82 / 9.9 cm/s 0.57 / 17.2 cm/s PWS 19

20. Subsurface Currents in the PWS Julian Day 2009 20

21. California (3 km) and Southern California Bight (1 km) domains 21

22. ROMS Forecast Performance vs. Persistence (Spatial correlation / RMS Difference) Forecast FieldROMS Single FcstROMS Ensemble Fcst Persistence 24 hr Sfc Currents 0.78 / 12 cm/s 0.66 / 18 cm/s 48 hr Sfc Currents 0.66 / 17 cm/s 0.46 / 23 cm/s SCB 22

23. SCB ROMS Forecasts vs. HF radar observed surface currents 23

24. Comparison of Glider-Derived Currents (vertically integrated current) Black: glider Red: ROMS 24

25. RMS: 8.5 cm/s, Correlation: 0.46 Subsurface currents in California: ROMS CA-3km vs Independent Data: Vertically-averaged glider currents 25

26. 2006 MB06/ASAP/AESOP Baroclinic Tides 26

27. Summary 1) A portable ROMS-based nowcast/forecast/hindcast nested modeling system assimilating coastal HF radar surface current measurements, satellite SSTs and in-situ glider/mooring data capable of routinely producing near real-time nowcasts every 6 hours and daily 2-3 day forecasts for coastal ocean regions at a horizontal resolution of 1km was presented. 2) A key component of the system is a multi-scale 3DVAR assimilation methodology that incorporates spatially varying and scale-dependent error covariances, scale-dependent dynamic balance constraints and can simultaneously assimilate all types of ocean observations. 3) Surface circulation patterns are quantitatively reproduced (RMS differences of 5-15 cm/s). Subsurface currents are qualitatively reproduced. 4) One and two day forecasts of surface current forecasts more skillful than persistence forecasts and clearly show the positive impact of the assimilation of HF radar surface currents at the surface and below. ourocean.jpl.nasa.gov/{PWS, CA, SCB} 27

28. All slides that follow are 'extras'

29. 29 Depth-Integrated Baroclinic Tide Energy/Energy Flux (M2)

30. Barotropic Tide Energy Flux (M2) 30

31. Barotropic Tide Current (M2)

32. Data Assimilation to enable Forecasting & estimate Uncertainty Time State of Ocean (e.g., T, S, Current) T T+ 6 hoursT+ 72 hours Forecast True Ocean Observations Ensemble Forecast Error Nowcast 32

33. HF Radar Total vs Radial Current Data Assimilation Total currents data assimilation (circle) 1 st Guess (blue) Reanalysis (red) Radial current data assimilation (triangle)

34. Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008a: A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System. Journal of Atmospheric and Oceanic Technology, 25, 2074-2090. Li, Z., Y. Chao, J.C. McWilliams, and K. Ide, 2008b: A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002, doi:10.1029/2006JC004042. Li, Z., Y. Chao, J. C. McWilliams, and K. Ide, 2011: A multi-scale three-dimensional variational data assimilation scheme and its application to coastal oceans. Quart. J. Roy. Meteorol. Soc., submitted. Global to Regional & Physics to Biology Multi-scale (or “nested”) ROMS modeling approach has been developed in order to simulate the 3D ocean at the spatial scale (e.g., 1- km) measured by satellites

35. 35 Impact of HF Radar Current Data on Nowcast/Forecast Nowcast RMS ROMS w/o Current DA ROMS with Current DA 48-hour forecast

36. Surface Current Forecasting in the PWS: Two case studies

37. Mean distance between ROMS nowcast and Observed drifter trajectories Hours since Deployed Distance (km)

38. Data Assimilation Formulation  prescribed B  optimization algorithm Variational methods (3Dvar/4Dvar): Sequential methods (Kalman filter/smoother)  dynamically evolved B  analytical solution

39. Distance Offshore (km)