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HYCOM/NCODA Variational Ocean Data Assimilation System

HYCOM/NCODA Variational Ocean Data Assimilation System James Cummings Naval Research Laboratory, Monterey, CA

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HYCOM/NCODA Variational Ocean Data Assimilation System

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  1. HYCOM/NCODA Variational Ocean Data Assimilation System James Cummings Naval Research Laboratory, Monterey, CA GODAE Ocean View III Meeting 14-18 November 2011 European Space Agency Headquarters Paris, France

  2. NCODA: Variational Analysis Flexible and Unified System: • global or regional applications (HYCOM, NCOM, WW3) • 2D mode: SST, Sea Ice, SSH, SWH, surface velocity • 3D mode: fully multivariate analysis (T,S,U,V) • multi-scale analyses: nested, successively higher resolution grids • cycles with forecast model or runs stand-alone Designed as Complete End-to-End Analysis System: • data quality control (QC) • variational analysis (3DVAR) • performance diagnostics (analysis residuals, Jmin, adjoint data impacts, ensemble transform)

  3. HYCOM/NCODA Data Flow Raw Obs NCODA: Navy Coupled Ocean Data Assimilation SST: NOAA (GAC, LAC), METOP (GAC, LAC), GOES, MSG, MTSAT-2, AATSR, AMSR-E, Ship/Buoy in situ Profile Temp/Salt: XBT, CTD, Argo Floats, Fixed/Drifting Buoy, Ocean Gliders Altimeter SSH: Jason-1&2, ENVISAT Sea Ice: SSM/I, SSMIS, AMSR-E Velocity: HF Radar, ADCP, Argo Trajectories, Surface Drifters, Gliders Automated QC w/condition flags Ocean Data QC 3DVAR – simultaneous analysis of 5 ocean variables: temperature, salinity, geopotential, u,v velocity components Innovations 3DVAR Increments HYCOM Adaptive Sampling Data Impacts Forecast Fields Prediction Errors First Guess Sensors NCODA: QC + 3DVAR

  4. NCODA Analysis System Components • 3DVAR • Analysis Error • Ensemble Transform • Assimilation Adjoint (KT)

  5. NCODA: Data Impacts Analysis – Forecast System Observation (y) NCODA 3DVAR Analysis (xa) HYCOM / NCOM Forecast (xf) Background (xb) Adjoint System Ob Error Sensitivity (J/ e) How to adjust the specified errors to improve the forecast ? Analysis Sensitivity (J/ xa) Gradient of Cost Function J: (J/ xf) Observation Sensitivity (J/ y) Adjoint of NCODA 3DVAR HYCOM / NCOM Adjoint Observation Impact <y-H(xb)> (J/ y) What is the impact of observations on the forecast accuracy ?

  6. NCODA: SST Data Impacts Analysis – Forecast System Observation (y) NCODA 2DVAR Analysis (xa) Navy NWP (NOGAPS) Forecast (xf) Background (xb) Adjoint System Observation Sensitivity (J/ y) Adjoint of NCODA 2DVAR Analysis Sensitivity (J/ xa) Gradient of Cost Function J: (J/ xf) Navy NWP Adjoint What is the sensitivity of the low level wind stress to the different SST data sources ?

  7. NCODA: SST Data Sources • GOES 11,13 (NAVO) • MSG (GHRSST GDAC) • METOP GAC/LAC (NAVO) • NOAA 18,19 GAC/LAC (NAVO) • Drifting/Fixed Buoys • Ship intake, hull contact, bucket temps • Coming Soon: • MTSAT-2, NPP VIIRS, WindSAT

  8. NCODA: Adaptive Data Thinning • high density surface data averaged within spatially varying bins – applied to SST, SSH, SWH, HF Radar, sea ice data • bins defined by grid mesh and background covariance structure – more (less) thinning where length scales are long (short) • takes into account observation error and SST water mass of origin Thinned SST Global NWP 37 km grid Length Scales 10 km 200 km 10 km input # obs: 28,943,383 output # obs: 152,768

  9. NCODA: Direct Assimilation Satellite SST Radiances Assume changes in TOA radiances are due to:(1) atmospheric water vapor content (2) atmospheric temperature (3) sea surface temperature Channel 3: 3.5 m Channel 4: 11m Channel 5: 12 m CRTM provides sensitivity of radiances with respect to SST, water vapor, and atm temperature for SST channels

  10. NCODA: Assimilation Satellite SST Radiances Given TOA BT innovations and RTM sensitivities, solve: Returns: (1) SST increment - Tsst (2) atmospheric temperature increment - Tatm (3) atmospheric moisture increment - Qatm • incorporates impact of real atmosphere above the SST field • removes atmospheric signals in the data • knowledge of esst, et, eqerror statistics critical

  11. NCODA: Assimilation Satellite SST Radiances • δTSST corrections for NOAA-19 and METOP-A; valid 8 June 2011 • first guess SST from NAVO empirical buoy match up regressions • atmos profiles from Navy NWP • large SST corrections associated with high water vapor regions • corrections differ between NOAA-19 and METOP-A for same NWP fields NOAA-19 METOP-A

  12. Difference between 2DVAR analysis of atmospheric corrected and uncorrected NAVO SST - 16 Aug 2011: METOP-A, NOAA-18,19 • NAVO SST data biased cold • large bias in mid-latitudes during NHEM summer • Atmosphere corrected SST being tested in Navy NWP 4DVAR • More accurate ocean surface allows use of sounder channels in 4DVAR that peak in boundary layer • Better characterization of boundary layer will improve ocean forcing

  13. NCODA: Global HYCOM • basin scale assimilation in Mercator part of grid (Atlantic, Indian, Pacific) • Arctic cap basin for irregular bi-pole part of grid (not shown) Observation Locations: 4 September 2008 369,593 obs 263,427 obs 625,359 obs

  14. NCODA: Global HYCOM Assimilation Timings on Cray XTE >750 million grid nodes, ~1.2 million observations, ~5 min run time

  15. NCODA: HYCOM Verification Temperature Atlantic Indian Pacific model errors adjust to data after ~10 cycles, remain constant over time

  16. NCODA: HYCOM Verification Salinity Atlantic Indian Pacific little model error adjustment to data, Atlantic salinity errors worse

  17. NCODA: HYCOM Verification Layer Pressure Atlantic Indian Pacific model errors adjust to data in about month slow improvement over time in Atlantic and Indian basin RMS errors

  18. NCODA: First Guess at Appropriate Time Why FGAT? Eliminates component of analysis error that occurs when comparing observations and forecasts not valid at same time -12 0 12 Data Window (+/- 12 hours) 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Forecast Time Period Innovations 1 hour forecast interval for SST: preserves diurnal cycle

  19. NCODA: First Guess at Appropriate Time -120 0 12 Data “Receipt Time” Window (-120 to + 12 hours) 24 Hour 24 Hour 24 Hour 24 Hour 24 Hour Forecast Forecast Forecast Forecast Forecast 5 days ago 4 days ago 3 days ago 2 days ago 1 day ago Innovations 24 hour forecast interval for profiles assimilating data “received” since last analysis using forecasts valid 5 days into the past

  20. Questions ?

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