Transcript Improved SST Analysis Xu Li, John Derber NCEP/EMC

Improved SST Analysis
Xu Li, John Derber
NCEP/EMC
Project Objective:
To Improve SST Analysis
• Use satellite data more effectively
• Resolve SST diurnal variation
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?
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 24-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)
Daily Analysis Difference
RTG_SST-HR
Operational
Reduced daily noise
Smoother anomalies
(less noise)
Smoother anomalies
(less noise)
Comparison between Two SST Retrieval Algorithms
Item
Navy/NESDIS
Algorithm
Empirical Regression:
For example, NOAA-17, day time:
NL(4/5) = .9404T4 + .0838Tf(T4-T5) +
1.1098(T4-T5)(sec(0)-1) – 255.1277
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
,
NCEP
Physical/Variational
Tsa  Ts f  Ts
Ts is solved by minimizing a cost
function
  ws wa wq  wa wq ( w4 S 42  w5 S 52 )  ws wq ( w4 A42  w5 A52 )  ws wa ( w4 Q42  w5 Q52 )
 ws w4 w5 ( A4 Q5  A5 Q4 ) 2  wa w4 w5 ( S 4 Q5  S 5 Q4 ) 2  wq w4 w5 ( S 4 A5  S 5 A4 ) 2
1  wa wq ( w4 S 4T4  w5 S 5T5 )
 wa w4 w5 ( S 4 Q5  S 5Q4 )(Q5T4  Q4T5 )  wq w4 w5 ( S 4 A5  S 5 A4 )( A5T4  A4T5 )
1
Ts 

Here S  Tb,c
c
Ts
Ac 
Tb ,c
Ta
Qc 
Tb,c
Qa
Tc  Tbo,c  Tb,fc
c  3,4,5 For AVHRR
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?
SST Analysis with GSI: Diurnal Variation signal and comparison with RTG Analysis
04/07/2005 (7th day of GSI SST analysis)
NCEP Operational RTG Daily Analysis
GSI Analysis (Daily Mean of 4 6-h analysis.)
GSI: (00Z – Daily Mean)
GSI: (12Z – Daily Mean)
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
0.6E
(0.50,10.0,1.20)
Exp 22
1.0E
(0.10,0.20,0.50)
Exp 23
1.0E
(0.25,0.35,1.0)
The use of AVHRR GAC 1-b data in GSI
(For GAC)
No thinning for Navy
AVHRR dTb (obs – rtm) histogram
CLAVR Cloud Flags
Satellite Radiance dependency on Aerosol Optical Depth
(Not significant, the same to HIRS, AMSU)
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)
SST definitions and data products within the GHRSST-PP
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
10m wind > 4.5 m/s
Warming model
Cooling model
Depths of Buoys and Ships
• Mooring Buoys
– TAO: 1.0 m.
– Station ID list and status:
http://www.pmel.noaa.gov/tao/proj_over/wmo.html
– TRITON: 1.5 m
– Station ID list and status:
http://www.pmel.noaa.gov/tao/proj_over/wmo.html
– PIRATA: 1.0 m. Station ID list and status:
http://www.pmel.noaa.gov/tao/proj_over/wmo.html
– Indian Ocean: 1.0m. Station ID list and status:
http://www.pmel.noaa.gov/tao/proj_over/wmo.html
– NDBC: 0.6 m (3 meter discus buoy) or 1.0 m (others). Station ID list and
status: http://seaboard.ndbc.noaa.gov/stndesc.shtml
– Canadian: 0.6 m (3-meter discus buoy) or 1.0 m (6, 10 or 12-meter
discus buoy), unknown yet (WKB, 0100-7, 02 02). Station ID list and
status: http://shylock.pyr.ec.gc.ca/~wbs/bplatstat.html
– COMPS: 1.2m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
– GoMOOS: 1.0m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
– Irish: 1.0m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
– MBARI: 0.6m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
– Meteo France: 1.0m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
– MySound: 1.0m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
– Scripps: 0.45 m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
– UK: 1.0m. Station ID list and status:
http://seaboard.ndbc.noaa.gov/to_station.html
• 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:
http://www.aoml.noaa.gov/phod/dac/deployed.html
• Ships:
– The ships information:
http://www.wmo.ch/web/www/ois/pub47/pub47-home.htm,
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
Future
• Analysis with GSI
– 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
• 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)
Assumed vertical temperature profile: Linear or Exponent?
z
Strong warming
Tw
Weak warming
z
I    u *2w dt
I s   [ S w (0)  S w ( DT )  Q(0)]dt
I
DT  (2 Ric )1 / 2
(gI s / c p )1 / 2
Tw ( z )  (1 
z
)  Tw (0)
DT
z
z
Tw ( z )  (1 
)  e DT Tw (0)
DT
z

z
DT
Tw ( z )  (1 
)  e Tw (0)
DT
T
1 F

t
 0 c z
Tw

Tw (0) 
Tw (0) 

 Tw (0) 
Is
Is

c p ( DT / 2) c p (0.5DT )
Is
Is

c p (e  2) DT c p (0.718DT )
Is
Is

c p e 1 DT c p (0.367 DT )
Integrate this T equation along t and z
when Tw (z ) is assumed, under the
condition of positive downward surface
heat flux
Warming when wind vanishes
The diurnal warming (trapping) depth:
DT  (2 Ric )1/ 2
I
(gI s / c p )1/ 2
When wind is zero, I  0, DT  0 . Therefore, Tw (0)  
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.
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:
gh
 0.65
2
 0 (V )
Assuming a relation this is true between the density and velocity anomalies and
the length scale, then,
DT  (2 Ric )
1/ 2
I
(gI s / c p )1 / 2
In Fairall et al, the mass exchange, caused by precipitation (P) and
evaporation (E) between air and sea is not included, therefore   T
When salinity is accounted, then   T  S
This gives a slightly different warming depth:
DT  ( 2 Ric )1 / 2
I
(gI s /  c p   gI sa )1 / 2
I sa   [ S 0 ( P  E )]dt
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