1. Improved NCEP SST Analysis
Xu Li
NCEP/EMC

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

3. Progress (1): Use satellite data more effectively
SST retrieval (with AVHRR Data)
Navy Retrieval  Physical Retrieval
Improved Analysis (Exp. done )
Physical retrieval code has been merged into GSI
Physical retrieval algorithm is running operationally since March 2005
SST analysis by assimilating satellite radiances directly with GSI
Use more satellite data
Add a new analysis variable in GSI: skin temperature of ocean
Errors of observation and first guess
Add SST In Situ and AVHRR observations to GSI
Experiments on SST or Skin Temperature analysis with GSI
Control: No In Situ & AVHRR, daily first guess (weekly analysis)
EXP1: With In Situ & AVHRR, daily first guess (weekly analysis)
EXP2: In Situ & AVHRR, 6-hourly first guess (previous 6-hourly analysis)

4. 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

5. 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

6. Progress (2): Resolve Diurnal Variation
Problems caused by the lack of SST diurnal cycle
Radiance bias correction
SST Analysis bias
Others: DV is an essential weather variation
Boundary condition: flux calculation precision
Evaluation of cost function in data assimilation
Feasibility to resolve diurnal variation (Diagnostics)
Observation
Buoy
Satellite retrieval
Flux (from GFS)
SST prediction in hourly time scale
6-hourly SST analysis by GSI

7. Radiance Bias Correction Amount: Day/Night dependent?
Bias = OB - BG
Solid: RMS
Dashed: Bias
Radiance Bias Correction Amount: Day/Night dependent?
NOAA-16 Passing Time: (2 pm, 2 am); NOAA-17 Passing Time: (10 am, 10 pm)

13. Impact of strong diurnal variation on the validation of SST retrieval and analysis
Physical retrieval
Analysis with
Physical retrieval
First Guess
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 10m wind > 4.5 m/s

14. Progress (3): Improved First Guess
Essential for a modern data assimilation system
SST forward model
Active ocean in GFS
Ocean model

15. Future Analysis with GSI Active ocean in GFS Retrievals Aerosol effect
Observation errors for in situ data
First guess error
Error correlation to other analysis variables
Active ocean in GFS
Retrievals
AVHRR
Other satellites?
Aerosol effect
Raw radiance (AVHRR GAC)

17. Daily Number of Satellite SST Retrievals in 1x1 Grid Cell
Monthly Mean. Feb
Day Time
Night Time

18. Daily Number of Observed Data (OP16: NOAA-16)
1 x 1 Grid Cell. Monthly Mean Feb. 2004

19. The Signal of Diurnal Cycle in Physical SST Retrieval
(Day – Night), 1 x 1, Monthly Mean, Feb. 2004
First Guess of Physical SST Retrieval: Daily Analysis without diurnal cycle?  Night Retrieval warmer than Day Retrievals!

20. Navy SST Retrieval too warm (bias) during day time for NOAA-16
The Signal of Diurnal Cycle in Navy SST Retrieval
(Day – Night), 1 x 1, Monthly Mean, Feb. 2004
Navy SST Retrieval too warm (bias) during day time for NOAA-16

21. SST definitions and data products within the GHRSST-PP
Infrared SST measurements
Skin-subskin model
Microwave SST measurements
Diurnal warming
model
Analysed SST
product