HYCOM/NCODA Variational Ocean Data Assimilation System James Cummings Naval Research Laboratory, Monterey, CA GODAE Ocean View III Meeting 14-18 November.

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Presentation transcript:

HYCOM/NCODA Variational Ocean Data Assimilation System James Cummings Naval Research Laboratory, Monterey, CA GODAE Ocean View III Meeting 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 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 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 (K T )

Observation (y) NCODA 3DVAR HYCOM / NCOM Forecast (x f ) Gradient of Cost Function J: (  J/  x f ) Background (x b ) Analysis (x a ) HYCOM / NCOM Adjoint Observation Sensitivity (  J/  y) Analysis Sensitivity (  J/  x a ) Observation Impact (  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 (x f ) Gradient of Cost Function J: (  J/  x f ) Background (x b ) Analysis (x a ) Navy NWP Adjoint Observation Sensitivity (  J/  y) Analysis Sensitivity (  J/  x a ) 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: 11  m 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 -  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 knowledge of  sst,  t,  q error statistics critical NCODA: Assimilation Satellite SST Radiances

δT SST 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 HYCOM Observation Locations: 4 September ,593 obs 263,427 obs 625,359 obs

Domain Grid SizeNumber Procs Number Obs Solver (min) Post (min) Total (min) Atlantic1751 x 1841 x , Indian1313 x 1569 x , Pacific2525 x 1841 x , Arctic Cap 4425 x 1848 x , NCODA: Global HYCOM Assimilation Timings on Cray XTE >750 million grid nodes, ~1.2 million observations, ~5 min run time

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

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

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 NCODA: First Guess at Appropriate Time Forecast Time Period Innovations 1 hour forecast interval for SST: preserves diurnal cycle Data Window (+/- 12 hours)

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 ?