Transcript Improved NCEP SST Analysis Xu Li NCEP/EMC

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
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–
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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:
1
1
1
1
1
2
2
f
o 2
J  J b  J o  [ 2 (Ts ) 2 
(

T
)

(

Q
)
]

[(
T


T
)

T

a
a
b ,i
b ,i
b ,i ]
2
2
2
2 s
2 a
2 q
2 i  b ,i
Ts  Tsa  Ts f , Ta  Taa  Taf , Qa  Qaa  Qaf
Tb,i  Tba,i  Tb,fi 
Tb ,i
Ts 
Tb ,i
Ta 
Tb ,i
Qa
Ts
Ta
Qa
Tb,i , Ts , Ta ( z ), Qa ( z ) is brightness temperature (radiance), skin temperature,
atmospheric temperature vertical profile and atmospheric water vapor vertical
profile respectively. Tb ,fi is calculated with radiative transfer model.
Tb ,i
,
Tb ,i
,
Tb ,i
is the sensitivity of Tb ,i to Ts , Ta ( z ), Qa ( z ) respectively.
Initially, the Ta , Qa 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 a ,f ,o represents analysis, first guess and
observation respectively. Lower-subscription i means the channel index.
Ts
Ta
Qa
 b2,i , s2 ,  a2 ,  q2 is the error variance of Tb,i , Ts , Ta and Qa respectively
The solutions of Ts , Ta , Qaare 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
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
– 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!
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