NCODA Variational Ocean Data Assimilation System (NCODA v3.5) James Cummings Naval Research Laboratory, Monterey, CA GODAE Ocean View / CLIVAR GSOP Workshop June 2011 University California Santa Cruz

Flexible and Unified System: global or regional applications (HYCOM, NCOM, Wavewatch) 2D mode: SST, Sea Ice, SSH, SWH, surface velocity 3D mode: fully multivariate analysis of five ocean variables: temperature, salinity, geopotential, and u,v velocity multi-scale analyses: nested, successively higher resolution grids cycles with forecast model or runs stand-alone oceanographic implementation of NAVDAS: Roger Daley and Ed Barker (2001) MWR 129: Designed as Complete End-to-End Analysis System: data quality control (QC) variational analysis performance diagnostics (residuals, Jmin, data impacts, etc) NCODA Variational Analysis

3DVAR – simultaneous analysis of 5 ocean variables: temperature, salinity, geopotential, u,v velocity components Ocean/Wave Model Ocean Data QC 3DVAR Raw Obs 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 Innovations Increments Forecast Fields Prediction Errors First Guess Adaptive Sampling Data Impacts SensorsNCODA: QC + 3DVAR HYCOM, NCOM, WW3 Navy Coupled Ocean Data Assimilation Automated QC w/condition flags NCODA Data Flow

NCODA Analysis System Components 3DVAR Analysis Error Ensemble Transform Assimilation Adjoint (K T )

fully multivariate length scales: proportional to Rossby radius deformation flow dependent: correlations stretched along front/eddy boundaries land distance: correlations spread along, not across, land/sea boundaries Flow Dependent Increments NCODA: Horizontal Correlations Land Distance Correlations Multivariate Correlations: ,u,v Rossby Radius

vertical density gradients: co-vary with stratification isopycnal: separation based on density surfaces adaptive, evolve with time Isopycnal Surfaces NCODA: Vertical Correlations HYCOM: Isopycnal Increments Vertical Density Gradient Length Scales: 2 Sep Z Cross Section Vertical Density Gradient Length Scales: 55S to 45N along 160E 0 M 200 M 400 M

Temp Salt Velocity Surface 150 m NCODA: Background Error Variances vary with location and depth, evolve with time adaptive: computed from time history of model variability and model-data errors at update cycle interval HYCOM/3DVAR Background Errors Gulf of Mexico Valid 27 Jan 2004

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 background covariance structures – more (less) thinning where length scales are long (short) takes into account observation error and SST water mass of origin 6 hrs Satellite & In Situ SST DataThinned SST 10 km 200 km 10 km Scales FNMOC GHRSST SST Analysis

Raw Data: note variable data resolutions - 0.5, 2.0, 6.0 km Thinned Data: spatial averaging to 6-km grid NCODA: Velocity Data Assimilation Analyzed Velocity: vectors over speed (cm/s) HF Radar Surface Current Mapping: July 2010 velocity data types: HF Radar, Acoustic Doppler Current Profilers, Surface Drifters, Ocean Gliders, Argo Trajectories 2D surface current mapping or 3D circulation model updates NOAA/IOOS formed team in US to assess HF Radar data impacts

NCODA: Ensemble Transform transforms forecast perturbations into analysis perturbations transformation computed for the entire state: 3D, multi-variable (T,S,U,V) supports COAMPS coupled model and Wavewatch ensemble systems NCODA ET Perturbations: Coupled Model Ensemble, 20 Aug 2005 Temp Member 3 39 m Depth U Velocity Member m Depth V Velocity Member 9 26 m Depth Salt Member m Depth

Observation (y) Data Assimilation System Forecast Model Forecast (x f ) Gradient of Cost Function J: (  J/  x f ) Background (x b ) Analysis (x a ) Adjoint of the Forecast Model Tangent Propagator Observation Sensitivity (  J/  y) Analysis Sensitivity (  J/  x a ) Observation Impact (  J/  y) Adjoint of the Data Assimilation System What is the impact of observations on measures of forecast error (J) ? Adjoint System Analysis – Forecast System NCODA: Data Impacts

CRTM provides sensitivity of radiances with respect to these variables for AVHRR channels 3, 4, and 5 (water vapor shown above) Strong water vapor absorption for channels 4 and 5, channel 3 more transparent, best channel for estimating SST Channel 3: 3.5  m Channel 4: 11  m Channel 5: 12  m NCODA: Assimilation Satellite SST Radiances Assume changes in TOA satellite SST radiances are due to: (1) atmospheric water vapor content (2) atmospheric temperature (3) sea surface temperature

Given TOA BT innovations and RTM sensitivities, solve 3x3 matrix problem: Returns: (1) SST increment -  T sst (2) atmospheric temperature increment -  T atm (3) atmospheric moisture increment -  Q atm incorporates impact of real atmosphere above the SST field removes atmospheric signals in the data easily expanded to include aerosols (dust, smoke, sea spray) important component of coupled model data assimilation system NCODA: Assimilation Satellite SST Radiances

δT SST corrections for NOAA-19 and METOP-A; valid 8 June 2011 large SST corrections associated with high water vapor regions corrections differ between NOAA-19 and METOP-A for same NWP fields requires access to cloud cleared radiances from all satellites radiance data NOT available from GHRSST NOAA-19 METOP-A

basin scale assimilation in Mercator part of grid (Atlantic, Indian, Pacific) Arctic cap basin for irregular bi-pole part of grid (not shown) NCODA: Global HYCOM Observation Locations: 27 July 2008

Domain Grid SizeNumber Procs Number Obs Solver (min) Post (min) Total (min) Atlantic1751 x 1841 x , Indian1313 x 1569 x , Pacific2525 x 1841 x ,301, Arctic Cap 4425 x 1848 x , NCODA: Global HYCOM Assimilation Timings on Cray XTE >750 million grid nodes, ~3 million observations, ~22 min run time

NCODA: Global HYCOM Verification Atlantic Basin initialized from free running model 1 July 2008, 24-hr update cycle T,S model errors adjust after ~3 cycles, remain constant over time

NCODA: Global HYCOM Verification Atlantic Basin layer pressure RMS errors adjust after ~16 cycles model bias slowly adjusts over ~1 month time period

NCODA: Global HYCOM Verification forecast innovations (blue) vs. analysis residuals (red) Atlantic basin: 27 July 2008 innovation vector files can be used in intercomparison project Argo SST

analysis QC based on iterative variational solution variational bias correction to maintain model T/S relationships at depth analysis QC based on iterative variational solution variational bias correction to maintain model T/S relationships at depth NCODA v3.x: Planned Upgrades NCODA v4.0: generalized NAVDAS-AR framework more efficient for large amounts of data compatible with planned ensemble, coupled, and hybrid DA developments interest in joint development by JCSDA partners new ONR funding FY11-FY13 to prototype system (with Craig Bishop) NCODA v4.0: generalized NAVDAS-AR framework more efficient for large amounts of data compatible with planned ensemble, coupled, and hybrid DA developments interest in joint development by JCSDA partners new ONR funding FY11-FY13 to prototype system (with Craig Bishop)

CICE Questions? NCODA NOGAPS COAMPS ® EFS COAMPS-OS ® COAMPS ® -TC Coupled Ens. NAAPS WW3 Ensemble WW3 Ensemble NCOM HYCOM Data Correction Ocean Model Initialization Atmospheric Model BC NCEP, NASA INTEREST 6.2 ONR Funding for Development of Coupled 4DVAR/ Ensemble Hybrid DA System based on NAVDAS-AR, FY10-- Advanced 4DDA R&D Ensemble Transform