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 ?