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

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SST Anomaly

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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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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