1. Xu Li, John Derber NCEP/EMC
Improved SST Analysis
Xu Li, John Derber
NCEP/EMC

2. Project Objective: To Improve SST Analysis
Use satellite data more effectively
Resolve SST diurnal variation

3. Progress (1) SST Retrieval
Develop a physical (variational) SST retrieval algorithm
Demonstrate the potential of variational assimilation of satellite radiance to derive SST
Done with AVHRR and used in NCEP operational 1/12 RTG daily SST analysis
Other satellites?

4. OPERATIONS:
New daily Real-time global SST (RTG_SST_HR) analysis (1/12o latitude, longitude resolution) is generated every 24-h (22:30 UTC) using latest 24 h of real-time data. (implemented – September 27, 2005)
Original daily Real-time global SST (RTG_SST) analysis (1/2º latitude, longitude resolution) is generated every h (22:30 UTC) using latest 24 h of real-time data. (implemented – January 30, 2001). Still running in parallel.
SST derived as physical retrievals from AVHRR data (JCSDA)
.Used as the lower boundary condition over the oceans for the Eta/WRF regional forecast model.
Areal maps and time series of validation statistics are available immediately from the MMAB WEB page:
Under evaluation by international forecast centers (ECMWF, UK Met Office)

5. Daily Analysis Difference
RTG_SST-HR
Operational
Reduced daily noise

6. Smoother anomalies
(less noise)
Smoother anomalies
(less noise)

7. Comparison between Two SST Retrieval Algorithms
Item
Navy/NESDIS
NCEP
Algorithm
Empirical Regression:
For example, NOAA-17, day time:
NL(4/5) = .9404T Tf(T4-T5) (T4-T5)(sec(0)-1) –
Physical/Variational
is solved by minimizing a cost function
Priori Information
Yes: 1 x 1 field SST (Tf, analysis based on 36-hour retrieval)
Yes: Previous Analysis
Diurnal variation resolving
Yes, but limited to the range of buoys diurnal signal
Yes, but requires analysis and first guess be able to resolve diurnal cycle
Radiative Transfer Model (RTM)
No,
but based on Simplified RTM
Yes:
Full RTM + Jacobi
Product
Temperature at buoy depths.
The regression equation is calibrated to buoys
Skin temperature (Infrared) or subskin temperature (Microwave). Tuned with buoys, but physically, not buoy temperature
Quality Control
CLAVR (flag = 0)
CLAVR (flag = 0) + BG check , , ,
Here
For AVHRR

8. Progress (2) Direct Assimilation of Satellite Radiance
Analyze SST by assimilating satellite radiances directly with GSI
6-hourly skin temperature analysis (Exps. Done)
Impact of the errors of the first guess and in situ observations on SST analysis (Exps. Done)
The use of AVHRR GAC 1-b data (Done)
Aerosol Effect
Radiance increment dependency on Navy aerosol optical depth (Done)
Bias correction?
Incorporation of oceanic components in GSI
Flux files (done)
Diurnal warming and sublayer cooling model (in development)
Oceanic model in GFS and coupling?

11. buoy
SST
FG error variance
Obs. Error
RTG
E (lon,lat)
(1.33,1.33,4.00)E
Exp 7
0.6E
(0.50,1.00,50.0)
EXP 9
(0.50,10.0,1.20)
Exp 22
1.0E
(0.10,0.20,0.50)
Exp 23
(0.25,0.35,1.0)

12. The use of AVHRR GAC 1-b data in GSI
(For GAC)
No thinning for Navy

13. AVHRR dTb (obs – rtm) histogram

15. CLAVR Cloud Flags

16. Satellite Radiance dependency on Aerosol Optical Depth (Not significant, the same to HIRS, AMSU)

17. Progress (3): Resolve Diurnal Variation
Active ocean to improve the first guess
Ideally:
3-dimensional OGCM (resolving diurnal variation?)
Near future:
High resolution 1-dimensional model (PWP, turbulence)
At present (in development, Ilya Rivin, Carlos Lozano):
Analysis Variable: Foundation SST (converted into skin and sub-skin SST)
Low resolution mixed layer model (2 layers)
+ Diurnal Warming model (Fairall et al, 1996)
+ Skin layer cooling model (Fairall et al, 1996)
Inventory on the depths of buoys and ships (Done)

18. SST definitions and data products within the GHRSST-PP

19. Impact of strong diurnal variation (weak winds)
on the validation of SST retrieval and analysis
All: All match-up.
Hwind: Match-up with
10m wind > 4.5 m/s
Nall: Number of all match-up
NHwind: Number of match-up with

20. Warming model

21. Cooling model

22. Depths of Buoys and Ships
Mooring Buoys
TAO: 1.0 m.
Station ID list and status:
TRITON: 1.5 m
PIRATA: 1.0 m. Station ID list and status:
Indian Ocean: 1.0m. Station ID list and status:
NDBC: 0.6 m (3 meter discus buoy) or 1.0 m (others). Station ID list and status:
Canadian: 0.6 m (3-meter discus buoy) or 1.0 m (6, 10 or 12-meter discus buoy), unknown yet (WKB, , 02 02). Station ID list and status:
COMPS: 1.2m. Station ID list and status:
GoMOOS: 1.0m. Station ID list and status:
Irish: 1.0m. Station ID list and status:

23. MBARI: 0. 6m. Station ID list and status: http://seaboard. ndbc. noaa
Meteo France: 1.0m. Station ID list and status:
MySound: 1.0m. Station ID list and status:
Scripps: 0.45 m. Station ID list and status:
UK: 1.0m. Station ID list and status:
Drifting Buoys
In a still wind condition, the sea water temperature at 12.5 cm ~ 17.5 cm depth is observed. The drifter may go below the water more than one meter when there is large wave, which is related to strong surface wind.
The drifting buoy station ID:

24. Ships:
The ships information: including the record layout, code table of data file and the list of VOS.
The methods of obtaining SST
BTT: Bait tanks thermometer
BU: Bucket thermometer (1.0 m)
C: thermometer in condenser intake on steam ships, or inlet engine cooling system on motor ships (2 ~ 14.5 m)
HC: Hull contact sensor (1.4 ~ 7.3m)
HT: “Through hull” sensor
RAD: Radiation thermometer
TT: Trailing thermistor
OT: Other
There may be two methods of measuring sea temperature on a ship

25. Future Analysis with GSI Active ocean in GFS/GSI Couple Analysis
More satellite data
AVHRR, HIRS, AIRS, AMSRE, GOES and other geostationary satellites, others
Observation errors for in situ data
First guess error
The sensitivity of skin and sub-skin temperature to foundation temperature (related by heat flux)
Active ocean in GFS/GSI
The impact of diurnal warming and sub-layer cooling on the satellite radiance simulation
A low resolution mixed layer prediction model
Improvements to Fairall warming model
A high resolution one-dimensional oceanic model?!
Couple Analysis

26. Cool skin/warm layer component of the COARE 3
Cool skin/warm layer component of the COARE 3.0 bulk flux algorithm (Fairall et al., 1996)
Based on Price, Weller, and Pinkel
2nd moment closure turbulent mixed layer model with added skin layer (Wick, 1995)
Based on Kantha and Clayson (1994)

27. Assumed vertical temperature profile: Linear or Exponent?z
Strong warming z 
Weak warming  
Integrate this T equation along t and z
when is assumed, under the condition of positive downward surface heat flux

28. Warming when wind vanishes
The diurnal warming (trapping) depth:
When wind is zero, Therefore,
The scaling must change over to a different form, governed by free convection and radiation absorptin. The mixing depth is then the convection depth C (Dalu and Purini, 1981, and J Price et al , 1986). The warming depth is deeper than the mixing depth in this situation.

29. Air-sea mass exchange and Warming Depth
The notion behind PWP model is that the wind mixing occurs primarily to relieve shear flow instability. The stability limit is given by a Richardson number criterion as follows:
Assuming a relation this is true between the density and velocity anomalies and the length scale, then,
In Fairall et al, the mass exchange, caused by precipitation (P) and evaporation (E) between air and sea is not included, therefore
When salinity is accounted, then
This gives a slightly different warming depth:

30. Physical/Variational SST Retrieval Formulation
Cost Function:
is brightness temperature (radiance), skin temperature, atmospheric temperature vertical profile and atmospheric water vapor vertical profile respectively is calculated with radiative transfer model.
is the sensitivity of to respectively.
Initially, the and are assumed not varying with height (z). Therefore,
The sum of these sensitivities with height is used in the scheme for AVHRR data. Upper-subscription represents analysis, first guess and observation respectively. Lower-subscription means the channel index.
is the error variance of and respectively
The solutions of are solved by minimizing cost function J

31. Bias & RMS of SST retrievals and analysis to buoy
RTPH: Physical Retrieval; RTNV: Navy Retrieval; ANPH: Analysis with RTPH;
ANNV: Analysis with RTNV; NOBS: Number of match-up in 6-hour time window
Solid: RMS; Dashed: Bias