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
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)
NCODA: Variational Analysis
3DVAR – simultaneous analysis of 5 ocean variables: temperature, salinity, geopotential, u,v velocity components
HYCOM
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 GlidersAltimeter SSH: Jason-1&2, ENVISAT Sea Ice: SSM/I, SSMIS, AMSR-EVelocity: HF Radar, ADCP, Argo Trajectories, Surface Drifters, Gliders
Innovations
Increments
Forecast Fields Prediction Errors
First GuessAdaptive Sampling Data Impacts
Sensors NCODA: QC + 3DVAR
NCODA: Navy Coupled Ocean Data Assimilation
Automated QC w/condition flags
HYCOM/NCODA Data Flow
NCODA Analysis System Components
• 3DVAR
• Analysis Error
• Ensemble Transform
• Assimilation Adjoint (KT)
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Observation(y)
NCODA 3DVAR
HYCOM / NCOM
Forecast(xf)
Gradient ofCost Function
J: (J/ xf)
Background(xb)
Analysis(xa)
HYCOM /NCOMAdjoint
ObservationSensitivity
(J/ y)
AnalysisSensitivity(J/ xa)
Observation Impact<y-H(xb)> (J/ y)
Adjoint of NCODA 3DVAR
What is the impact of observations on the forecast accuracy ?
Adjoint System
Analysis – Forecast System
NCODA: Data Impacts
Ob Error Sensitivity(J/ )
How to adjust the specified errors to improve the forecast ?
Observation(y)
NCODA 2DVAR
Navy NWP (NOGAPS)
Forecast(xf)
Gradient ofCost Function
J: (J/ xf)
Background(xb)
Analysis(xa)
Navy NWP Adjoint
ObservationSensitivity
(J/ y)
AnalysisSensitivity(J/ xa)
Adjoint of NCODA 2DVAR
What is the sensitivity of the low level wind stress to the different SST data sources ?
Adjoint System
Analysis – Forecast System
NCODA: SST Data Impacts
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
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 10 km
200 km
10 km
Length Scales
input # obs: 28,943,383 output # obs: 152,768
CRTM provides sensitivity of radiances with respect to SST, water vapor, and atm temperature for SST
channels
Channel 3: 3.5 m Channel 4: 11m Channel 5: 12 m
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
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 sst, t, q error statistics critical
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NCODA: Assimilation Satellite SST Radiances
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
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
• 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 HYCOMNCODA: Global HYCOM
Observation Locations: 4 September 2008
Observation Locations: 4 September 2008
369,593 obs 263,427 obs 625,359 obs
Domain Grid Size Number Procs
Number Obs
Solver (min)
Post (min)
Total (min)
Atlantic 1751 x 1841 x
42
104 269,593 1.3 2.6 4.9
Indian 1313 x 1569 x 42
88 263,427 1.4 2.9 4.5
Pacific 2525 x 1841 x 42
392 625,359 2.1 1.5 4.0
Arctic Cap
4425 x 1848 x 42
40 181,230 0.8 2.4 3.2
NCODA: Global HYCOM Assimilation Timings on Cray
XTE
NCODA: Global HYCOM Assimilation Timings on Cray
XTE
>750 million grid nodes, ~1.2 million observations, ~5 min run time
NCODA: HYCOM Verification Temperature
NCODA: HYCOM Verification Temperature
model errors adjust to data after ~10 cycles, remain constant over time
Atlantic Indian
Pacific
NCODA: HYCOM Verification Salinity
NCODA: HYCOM Verification Salinity
little model error adjustment to data, Atlantic salinity errors worse
Atlantic Indian
Pacific
NCODA: HYCOM Verification Layer Pressure
NCODA: HYCOM Verification Layer Pressure
model errors adjust to data in about month slow improvement over time in Atlantic and Indian
basin RMS errors
Atlantic Indian
Pacific
Why FGAT? Eliminates component of analysis error that occurs when comparing observations and forecasts not
valid at same time-12 0 12
NCODA: First Guess at Appropriate Time
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
Data Window (+/- 12 hours)
-120 0 12
NCODA: First Guess at Appropriate Time
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
Data “Receipt Time” Window (-120 to + 12 hours)
Questions ?