Improved NCEP SST Analysis

Xu Li

NCEP/EMC

Project Objective:

To Improve SST Analysis

• Use satellite data more effectively

• Resolve diurnal variation

• Improve first guess

Progress (1): Use satellite data more

effectively• SST retrieval (with AVHRR Data)

– Navy Retrieval Physical Retrieval• Improved Analysis (Exp. done 2003-2004)

– 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)

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Physical/Variational SST Retrieval FormulationCost Function:

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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.

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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

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

Radiance Bias Correction Amount: Day/Night dependent? NOAA-16 Passing Time: (2 pm, 2 am); NOAA-17 Passing Time: (10 am, 10 pm)

Bias = OB - BG

Solid: RMS

Dashed: Bias

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

Physical retrieval

Analysis with

Physical retrieval

First Guess

Impact of strong diurnal variation on the validation of SST retrieval and analysis

Progress (3):Improved First Guess

• Essential for a modern data assimilation system

• SST forward model– Active ocean in GFS

– Ocean model

Future

• Analysis with GSI– 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)

Daily Number of Satellite SST Retrievals in 1x1 Grid Cell

Day Time Night TimeMonthly Mean. Feb. 2004.

Daily Number of Observed Data (OP16: NOAA-16)

1 x 1 Grid Cell. Monthly Mean Feb. 2004

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!

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

SST definitions and data products within the GHRSST-PP

Infrared SST measurements

Microwave SST measurements

Analysed SST product

Diurnal warmingmodel

Skin-subskin model