Powerpoint template for scientific poster

1
Prototyping SST Retrievals from GOES-R ABI with
MSG SEVIRI Data Nikolay V. Shabanov1,2,
Nikolay.Shabanov_at_noaa.gov, [phone]
154 Alexander Ignatov1, Boris Petrenko1,2, Yuri
Kihai1,3, XingMing Liang1,4, Wei Guo1,2, Feng
Xu1,4, Prasanjit Dash1,4, Michael Goldberg1 ,
John Sapper5 1NOAA/NESDIS/STAR 2IMSG Inc
3Perot Systems Government Services 4Colorado
State University- CIRA 5NOAA/NESDIS/OSDPD
JP2.15
Comparison with Global Reference SSTs
Introduction
(c)
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Geostationary Operational Environmental
Satellite-R Series (GOES-R) will carry Advanced
Baseline Imager (ABI) onboard. Sea Surface
Temperature (SST) algorithm for ABI is being
developed by the SST Team which is a part of the
GOES-R Algorithm Working Group (AWG). ABI SST
production is prototyped with the Meteosat Second
Generation (MSG) Spinning Enhanced Visible and IR
Imager (SEVIRI) data (Schmetz et al., 2002). The
Advanced Clear-Sky Processor for Oceans (ACSPO)
developed at NOAA/NESDIS and currently
operational with AVHRR data onboard NOAA-18 and
MetOp-A has been adopted to SEVIRI. ACSPO-SEVIRI
system processes 15-minute full-disk (FD) SEVIRI
Level 1 data in near-real time (NRT) and
generates Level 2 clear-sky products over ocean,
including top-of-atmosphere (TOA) clear-sky
brightness temperatures (BTs), SST and aerosols.
This poster evaluates initial BT and SST
retrievals from Meteosat-9 SEVIRI for one full
month of data in June 2008.
The SST Quality Monitor (SQUAM) currently
routinely generates consistency statistics
between the retrieved AVHRR SST and multiple
reference SST fields (Dash et al., 2009). SQUAM
was applied to quickly evaluate SEVIRI SST
retrievals. The following reference SST were
used Daily Reynolds OISST (AVHRR-based) and
OISST-A (AVHRRAMSR-E), OSTIA, RTG HR (High
Resolution) and Low Resolution (LR), and
Pathfinder SST climatology.

Fig. 2. Example of MSG-2 SEVIRI 15-min FD data
for June 14, 2008, 200pm UMT, processed by the
ACSPO-SEVIRI system. (a) Visible true-color image
constructed from albedo. Black strip expanding
from SE corresponds to night-time (b) Cloud
Mask (c) SST (d) AOD retrieved from Ch01 using
single-channel algorithm (De Paepe et al., 2008).
CRTM and BT Simulations
ACSPO-SEVIRI System
SST Diurnal Cycle
Community Radiative Transfer Model (CRTM) is used
in ACSPO in conjunction with Reynolds SST and
NCEP Global Forecast System (GFS) upper air
fields to simulate clear-sky BTs in SEVIRI Ch4,
9, and 10. CRTM BTs are used for cloud masking,
physical SST retrievals, and to monitor quality
of SEVIRI radiances. Here, Model (CRTM) minus
Observation (SEVIRI) (M-O) biases are
preliminarily evaluated using nighttime SEVIRI
data (Sun Zenith Angle gt 90).
Fig.3. Solid lines show FD monthly mean diurnal
cycles of Retrieved minus Reynolds SST.
Individual data points correspond to different
days in June 2008.

• Ancillary OISST Data
• Reynolds SST
• (HDF, 1ox1o, weekly or
• 0.25o x 0.25o, daily )

• SEVIRI Static Data
• Land/Water mask
• View geometry (VZA)
• Lat/Lon
• (HDF, 5x5 km (at nadir))

• SEVIRI Dynamic Data
• Optical bands 1, 2, 3
• Thermal bands 4, 9, 10
• Illumination geometry (SZA, RAA)
• (HDF, 5x5 km (at nadir), 15 min)

• Ancillary GFS Data
• Air and Surface Temperature
• Water vapor
• Wind speed
• Ozone concentration
• (HDF, 1x1, 6-hours)

Reference SST
Physical
SST Algorithm
Channel Calibration
Regression
Physical (CRTM-based)
Regression (Split window)
First Guess SST
CRTM simulations of BT and BT Jacobian at coarse
(1x1) Grid
Cloud Mask Algorithm (7 tests)
Bi-Linear Interpolation (Reynolds SST) 1ox1o -gt
5x5 km (at nadir)
Single Channel 6S-based LUTs
AOD Algorithm
Fig.7. Nighttime M-O biases in SEVIRI Ch4, 9,
and 10 for one FD image taken on 14 June 2008 _at_
300am. Data shown are for Satellite VZA lt 70.
Retrieved SST exhibits the diurnal cycle with an
average amplitude of 0.3C.
1) SST (currently on) 2) BT (on) 3) Ch 3 Albedo
(currently off) 4) Reflectance Ratio (off) 5)
Three-Five (off) 6) Ultra Low Stratus (off) 7)
Uniformity (off)
Fig.4. Time series of SST anomalies evaluated
over clear sky pixels for June 2008.
Reference SST
Bi-Linear Interpolation (BT and BT
Jacobian) 1ox1o -gt 5x5 km (at nadir)
High M-O biases of 0.6K in Ch9 (11µm) and
0.3K in Ch10 (12 µm) are consistent with AVHRR
(Liang et al., 2009). They may be due to CRTM
(missing aerosol, using bulk rather than skin
SST, not correcting SST for diurnal cycle) or
SEVIRI data (residual clouds). The low bias in
Ch4 (3.7 µm) is inconsistent with AVHRR analyses.
The SQUAM methodology will be routinely applied
to analyze SEVIRI SSTs.
QC Mask

• Individual Cloud Mask Tests results
• AOD flag
• L/W Mask
• Nay/might flag
• Glint flag

• Output (up to 32 layers/SDS)
• TOA albedo in VIS bands (1, 2, 3)
• TOA BT at Thermal bands (4, 9,10)
• SST (Regression Physical)
• Aerosol Optical Depths in VIS bands (1,2,3)
• CRT BTs
• Geometry and Geolocation
• QC (cloud mask, L/W, day/night flag, etc)
• OISST (Reynolds)
• GFS fields interpolated to SEVIRI
• (HDF, 5x5 km (at nadir), 15 min)

• Web-based QC Tools
• Error characterization as function of
  Environmental Conditions (SSES)
• SST evaluation with reference Fields and Buoy
  Data (SQUAM)
• BT evaluation with CRTM simulations (MICROS)

SST anomaly changes from day to day and as a
function of time of day.
Fig. 1. Flow-chart of the ACSPO-SEVIRI system.
Fig.5. Physical-Reynolds SST as a function of
wind speed. FD data from 1-7 June 2008 are binned
at V1 m/s and averaged for 2 hours centered on
Midday and Midnight.
Input to ACSPO-SEVIRI system is 15-min SEVIRI FD
channel data (optical Ch1-3 and thermal Ch4,
9-10). 1-resolution weekly Reynolds SST and
6-hour National Centers for Environmental
Prediction Global Forecast System (NCEP/GFS) data
are used as input to the fast Community Radiative
Transfer Model (CRTM) to simulate clear-sky BT in
Ch4, 9-10 (Liang et al., 2009). Two SST
algorithms are implemented. The Regression
(split-window, NLSST) algorithm is based on
Walton et al. 1985 equation Currently used
regression coefficients a0a3 were derived by
EUMETSAT for Meteosat-8. The Physical algorithm
uses CRTM to invert BT9 and BT10 for SST and
water-vapor optical depth scaling factor
(Merchant et al., 2008). The regression and
physical algorithms will be cross-evaluated, to
generate a superior quality hybrid SST algorithm.
For this study, a simplified cloud-mask was
implemented which currently uses only two tests
SST test (comparison of retrieved and Reynolds
SST) and BT test (comparison of CRTM simulated
and measured BTs). The end-to-end NRT processing
system has been set up for data stream
processing (1) downloading SEVIRI data from NOAA
operational servers (in collaboration with AWG
Land Team), (2) data processing at SST Team
servers, (3) data analysis with web-based QC
tools. SEVIRI data for 2008 are online, 2006-2007
are on external disks. The product file contains
up to 32 data layers, file size of one FD product
file is 450MB. Execution time is 7min per FD on
DELL PowerEdge 2900.
Fig.8. Time Series of M-O biases in SEVIRI Ch4, 9
and 10 in June 2008. (Nighttime data with
VZAlt70). The root causes of the two drop-out
anomalies on 11 and 15 June 2008 are being
investigated.
At night, SST is a fairly insensitive to wind
speed. During daytime, SST changes as a function
of wind speed with an amplitude of 0.7 K.
Difference between day and night is largest at
low winds, due to forming a strong diurnal
thermocline. The higher the wind speed, the
smaller the diurnal heating.
SSTa0a1 BT9a2 TReynolds (BT9-BT10 )a3
(BT9-BT10 )(sec? - 1),
Literature

• Dash et al., (2009). The SST Quality Monitor
  (SQUAM), submitted, RSE.
• De Paepe et al., (2008). Aerosol retrieval over
  Ocean from SEVIRI for the use in GERB Earths
  radiation budget analysis, RSE, 112, 2455-2468.
• Liang et al., (2009). Implementation of CRTM in
  ACSPO and validation against nighttime AVHRR
  radiances, submitted, JGR.
• Merchant et al., (2008). Optimal estimation of
  sea surface temperature from split-window
  observations, in press, RSE.
• Schmetz et al., (2002). An Introduction to
  Meteosat Second Generation, BAMS, 83, 977-992.
• Walton et al. (1998). The development and
  operational application of nonlinear algorithms
  for the measurement of sea surface temperatures
  with the NOAA polar-orbiting environmental
  satellites. JGR, vol. 103(C12), 27999- 28012.

Fig. 6. SST bias as function of Number of Ambient
Clear-Sky pixels (NAC calculated within 2121
GAC pixels). FD data from 1-7 June 2008 are
binned at NAC20 and averaged for 2 hours
centered on Midday and Midnight.
CRTM simulations for SEVIRI longwave bands 9 (11
µm) and 10 (12 µm) are consistent with similar
results for the AVHRR. More analyses are needed
to better quantify these preliminary
observations. M-O bias in Ch4 (3.7 µm) is
inconsistent with AVHRR and requires further
analyses.
Disclaimer
The views, opinions and findings contained in
this report are those of the authors and should
not be construed as an official NOAA or U.S.
Government position, policy, or decision. This
poster does not reflect the views or policies of
the GOES-R Program Office or Algorithm Working
Group.
SST depends upon ambient clear-sky conditions
during both day and night (Xu et a., 2009). The
diurnal signal is smallest when ambient
cloudiness is high (NAC?0) and largest under
clear skies (NAC400).

89th AMS Annual Meeting and 5th GOES Users'
Conference , Phoenix, AZ , 11-15 January, 2009
Correspondence Nikolay.Shabanov_at_noaa.gov, Tel.
301-763-8102 x154, Fax [phone]