1. 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

2. 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

3. 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

4. ENVISAT AATSR 18 million obsAQUA AMSRE 5 million obs MSG SEVIRI 2 million obs GHRSST SST Data Daily Data Counts Total: 25.0 million retrievals/day

5. Data latency is determined from start time of AVHRR GAC orbit to delivery time of processed SST retrievals NAVOCEANO AVHRR GAC SST Data Latency

6. Improved Daytime Equation Bias and RMSD Errors Relative to Drifting Buoys: NAVOCEANO METOP-A FRAC DayNight

7. NAVOCEANO Satellite SST Retrieval Errors Common Set of Drifting Buoy Match-ups used to Compute SST Retrieval Errors Across all Satellites

8. 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 Buoy Altimeter SSH: Jason-1, Jason-2 Sea Ice: SSM/I, SSMIS, AMSR-E Ocean Gliders: T/S profiles Velocity: HF Radar, ADCP, Argo Trajectories, Surface Drifters, Gliders Innovations Increments Forecast Fields Prediction Errors First Guess Adaptive Sampling Guidance SensorsNCODA: 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

9. Variational Analysis System Components 3DVAR Analysis Error Ensemble Transform Assimilation Adjoint

10. 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

11. 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 SSTThinned SST 10 km 200 km 10 km Scales Global 2DVAR GHRSST Analysis 6 hr update cycle

12. 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

13. Aerosol Plumes Obscure the Ocean Yellow Sea, Sea of Japan West Pacific Ocean Atlantic Ocean Tropical 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

14. 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 surface b) boundary layer mixing and diffusion c) wind dispersion and advection d) 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

15. 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 -1 B 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 (μ j ) group assignment (k) is probabilistic (  2 ) – allows for uncertainties in NAAPS model predictions and satellite IR BTs Detection Aerosol Contamination: Canonical Variate Analysis

16. GroupDust Contamination SST Anomaly N 1None0.03468 2Weak-0.24238 3Moderate-0.81016 4Strong-3.2566 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

17. NAAPS Dust AOT at 500 nm (0.56 g/m 2 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

18. 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

19. 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)

20. Given BT innovations and sensitivities, solve 3x3 matrix problem: Inverse Modeling with CRTM Returns: (1) SST increment -  T sst (2) atmospheric temperature increment -  T atm (3) atmospheric moisture increment -  Q atm

21. 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

22. *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: 4  K instantaneous, 1  K on average skin-bulk SST differences persist in warm layers in some locations Skin SST Model* Embedded in NOGAPS Link to movie

23. Summary and Conclusions Navy 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

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25. NAVOCEANO AVHRR Retrieval Process Overview AVHRR and HIRS 1b Input Day/Night Test Solar Zenith Angle Day/Night Test Solar Zenith Angle Satellite Zenith Angle Test Gross Cloud Test Land Test Create Unit Array Visible Cloud Threshold Test (daytime only) Uniformity Tests Thin Cirrus Test Low Stratus Test CH4 – CH5 Test SST Intercomparison Test Unreasonable SST Test Climatology Test HIRS/Field Test (nighttime only) Aerosol Test (nighttime only) Create SST