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Overview of Navy Operational and Research SST Activities
James Cummings Naval Research Laboratory,
Monterey, CA
Doug May and Bruce McKenzie Naval Oceanographic Office,
Stennis Space Center, MS
Sea Surface Temperature Science Team Meeting 8-10 November 2010
Seattle, WA
Talk Outline:
1.NAVOCEANO SST Activities
• SST retrievals
• SST uncertainty estimates
2.SST Analysis Capabilities and Products
3.NRL SST Activities
• aerosol contamination detection and correction
• physical SST retrievals
• diurnal skin SST model
MetOp-A AVHRR FRAC 15 million obs
GOES-West 3.2 million obs
N-19 AVHRR LAC 4.5 million obs
GOES-East 2.1 million obs
MetOp-A, N-18, N-19 AVHRR GAC 1.3 million obs
NAVOCEANO Operational SST Daily Data Counts
Total: 26.1 million
retrievals/day
ENVISAT AATSR 18 million obs AQUA AMSRE 5 million obs
MSG SEVIRI 2 million obs
GHRSST SST Data Daily Data Counts
Total: 25.0 million
retrievals/day
Data latency is determined from start time of AVHRR GAC orbit to delivery time of processed SST retrievals
NAVOCEANO AVHRR GAC SST Data Latency
Improved Daytime Equation
Bias and RMSD Errors Relative to Drifting Buoys: NAVOCEANO METOP-A
FRAC
Day Night
NAVOCEANO Satellite SST Retrieval Errors
Common Set of Drifting Buoy Match-ups used to Compute SST Retrieval Errors Across all Satellites
3DVAR – simultaneous analysis of 5 ocean variables: temperature, salinity, geopotential, u,v velocity components
Ocean Model
Ocean Data QC
3DVAR
Raw Obs
SST:NOAA (GAC, LAC), METOP (GAC, LAC), GOES, MSG, AATSR, AMSR-E, Ship/Buoy Profile Temp/Salt: XBT, CTD, Argo Float, Fixed/Drifting BuoyAltimeter SSH: Jason-1, Jason-2 Sea Ice: SSM/I, SSMIS, AMSR-EOcean Gliders:T/S profilesVelocity: HF Radar, ADCP, Argo Trajectories, Surface Drifters, Gliders
Innovations
Increments
Forecast Fields Prediction Errors
First GuessAdaptive Sampling Guidance
Sensors NCODA: QC + 3DVAR HYCOM or NCOM
Navy Coupled Ocean Data Assimilation: operational at Navy centers (NAVO, FNMOC)
Automated QC w/condition flags
Data Flow through NCODA System
Variational Analysis System Components
• 3DVAR
• Analysis Error
• Ensemble Transform
• Assimilation Adjoint
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Sea Ice
SST
Global 2DVAR Assimilation: 9 km grid, 6 hr cycle
Analysis Increments Updated Field
Navy Contribution to GHRSST
http://www.usgodae.org/ftp/outgoing/fnmoc/models/ghrsst/
Sea Ice and SST analysis fields and analysis errors since 2005
Variational Assimilation: Adaptive Data Thinning• high density SST data averaged within spatially varying bins
• bins defined by background covariances – more (less) data thinning where length scales are long (short)
• takes into account observation error and SST water mass of origin
Satellite & In Situ SST Thinned SST
10 km
200 km
10 km
Scales
Global 2DVAR GHRSST Analysis
6 hr update cycle
SST Covariance Options:
• flow dependence: correlations stretched and rotated along SST gradients
• distance from land: correlations spread along, not across, land boundaries
Flow Dependent
Land Distance
Variational Assimilation: Covariances
Aerosol Plumes Obscure the Ocean
Yellow Sea, Sea of Japan
West Pacific OceanAtlantic OceanTropical Atlantic Ocean
Dust is optically active in the IR: elevated plumes appear cold
Need to first detect and then correct aerosol contamination of SST retrievals
Navy Aerosol Analysis Prediction System (NAAPS)
NAAPS February 2007 Optical Depth
Sulfate Dust Smoke
• global semi-lagrangian aerosol transport model
• driven by global NWP model
• variational assimilation MODIS and MISR AOD
• multiple aerosol types: dust, smoke, sulfate, sea spray
• physical processes:a) emission from the
surfaceb) boundary layer mixing
and diffusionc) wind dispersion and
advectiond) atmospheric removal by
wet and dry deposition
Aerosol plume events are episodic, varying in strength, frequency, composition, altitude
NAAPS provides time-dependent, spatially varying analyses to track aerosol plumes
• discriminate among groups of SST retrievals contaminated by aerosols and SST retrievals free from aerosols
B = between group and W = within group covariance matrices. Eigenvalues of W-1B and eigenvectors () are the canonical variates.
• predictors are AVHRR channel BTs and wavelength dependent NAAPS AOD components (x) projected onto r canonical variates
• SST is classified as contaminated if the Euclidean distance is closest to the contaminated group mean (μj)
• group assignment (k) is probabilistic (2) – allows for uncertainties in NAAPS model predictions and satellite IR BTs
Detection Aerosol Contamination: Canonical
Variate Analysis
2
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WB
Group Dust Contaminati
on
SST Anomaly
N
1 None 0.0 3468
2 Weak -0.2 4238
3 Moderate -0.8 1016
4 Strong -3.2 566
Canonical Variate Analysis
• applied to NAVO match-up data base for 1-10 July 2010 in tropical Atlantic
• four groups defined with different levels of dust loading
• first two canonical variates explain 98% of the variance
• strong contamination group shows cold SST anomalies relative to buoys
NAAPS Dust AOT at 500 nm (0.56 g/m2 extinction)
CRTM top-of-atmosphere BTs with and without NAAPS dust
Correction Aerosol Contamination: CRTM
Aerosol Module
AVHRR/METOP-A Ch5 dust minus clear sky TOA BTs. Nadir
view using Navy global NWP model (idealized case).
Forward modeling results only, correction algorithm work in progress
Physical Satellite Skin SST Retrievals
Two Step Process• CRTM forward modeling: innovations of AVHRR BTs wrt NWP
model BTs
• CRTM inverse modeling: sensitivities of SST BTs to model state vector and SST BT response to state perturbations
• incorporates impact of real atmosphere above the SST field
• removes atmospheric signals in the data
• assumes observed changes in SST BTs are due to 3 atmospheric model state variables:
• atmospheric water vapor content
• atmospheric temperature
• sea surface temperature
3
4
5
Forward Modeling with CRTM
AVHRR Infrared Channels • converts NOGAPS state vector to
top of the atmosphere brightness temperatures (TOA BTs )
• predicted AVHRR channels 3-5 TOA BTs from NOGAPS (left)
• METOP-A observed channel 5 BTs minus NOGAPS predicted TOA BTs (below)
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Given BT innovations and sensitivities, solve 3x3 matrix problem:
Inverse Modeling with CRTM
Returns: (1) SST increment - Tsst
(2) atmospheric temperature increment - Tatm
(3) atmospheric moisture increment - Qatm
Navy NWP Requirements for Physical Skin SST
Skin vs. Bulk• empirical SST algorithms compute bulk SST from drifting buoys
• skill limited to latitude / longitude range of buoy observations• unknown sampling depth of drifting buoys (cm to m)
• daily averaged bulk SST analysis inadequate for NWP• Navy atmospheric 4D-VAR rejects data from satellite sounding channels that peak at or near the surface
Diurnal SST Cycle• need to resolve ocean diurnal cycle
• essential weather variation• required for physical SST assimilation (6-hr update cycle)
• diurnal SST influences NWP convection and mixing• affects clouds, low level humidity, visibility, EM/EO propagation
• NWP model improvements lead directly to improvements in ocean circulation and wave models
*Zeng, X. and A. Beljaars (2005). Geophys. Res. Lett. 32.*Takaya, Y., J. Bidlot, A. Beljaars, and P. Janssen (2010). J. Geophys. Res. 115.
• forced by NOGAPS heat fluxes, solar radiation, surface stress
• called every model time step integrating NWP forcing over time
• compared skin SST with bulk SST control
• large regional differences found: 4K instantaneous, 1K on average
• skin-bulk SST differences persist in warm layers in some locations
Skin SST Model* Embedded in NOGAPS
Link to movie
Summary and ConclusionsNavy Operations:
• NOAA/METOP/GOES SST data provider
• consistent SSES for all satellite SST observing systems
• range of SST assimilation activities:
• global, regional, coastal
• analysis-only, model based forecasting systems
Navy Research and Development:
• physical SST retrieval algorithms
• aerosol contamination detection and (eventually) correction
• diurnal SST modeling, direct SST radiance assimilation
Navy activities encompass many Science Team tasks
END
NAVOCEANO AVHRR Retrieval Process Overview
AVHRR and HIRS 1b Input
AVHRR and HIRS 1b Input
Day/Night TestSolar Zenith Angle
Day/Night TestSolar Zenith Angle
Satellite Zenith Angle Test
Satellite Zenith Angle Test
Gross Cloud TestGross Cloud Test Land TestLand Test
Create Unit ArrayCreate Unit Array
Visible Cloud Threshold Test (daytime only)
Visible Cloud Threshold Test (daytime only)
Uniformity TestsUniformity Tests
Thin Cirrus TestThin Cirrus Test Low Stratus TestLow Stratus Test CH4 – CH5 TestCH4 – CH5 Test SST Intercomparison
Test
SST Intercomparison
Test
Unreasonable SST Test
Unreasonable SST Test Climatology TestClimatology Test HIRS/Field Test
(nighttime only)HIRS/Field Test (nighttime only)
Aerosol Test (nighttime only)
Aerosol Test (nighttime only)
Create SSTCreate SST