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)

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

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? 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)

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

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 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)

Daily Number of Satellite SST Retrievals in 1x1 Grid Cell Monthly Mean. Feb. 2004. Day Time Night Time

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!

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

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