Inter-satellite Calibration of
HIRS OLR Time Series
Hai-Tien Lee
University of Maryland, CICS/ESSIC-NOAA
Arnold Gruber
University of Maryland, CICS/ESSIC-NOAA
Robert G. Ellingson
Florida State University, Dept. of Meteorology
Istvan Laszlo
NOAA/NESDIS
NOAA/NESDIS Cooperative Research Program 2nd Annual Science Symposium
Madison, WI. July 13-14, 2005
Outline
 Cal/Val Issues for the OLR Climate Data
Record (CDR)
 Inter-satellite Calibration of HIRS OLR
Time Series
 Findings and Discussion
2
Cal/Val Issues for the OLR Climate Data Record
What is CDR?
“A time series of measurements of sufficient
length, consistency, and continuity to
determine climate variability and change.”
– Committee on Climate Data Records from NOAA
Operational Satellites, NRC/NAS, 2004.
3
Satellite OLR Products
4
Equator Crossing Time for NOAA Polar Orbiters
Typical Satellite Track Crossing for a Morning and an Afternoon POES
AM
PM
6
Inter-satellite calibration for HIRS OLR
Collocation:
• 1°x1° lat/lon
• ±30 minutes
•n>1
Satellites
Bias (Wm-2)
TN
0.15
N06
1.80
N07
2.13
N08
2.03
N09
Reference
N10
0.53
N11
-5.36
N12
-2.42
N14
-5.14
N15
-3.65
N16
-3.25
Homogeneity filter:
• Std error of mean OLR < 1 Wm-2
7
Improvement with inter-satellite calibration
The blended HIRS monthly mean OLR
data with the adjustments determined by
inter-satellite calibration agrees much
better with the CERES.
Tropical Mean
Magenta - CERES (TRMM, Terra, Aqua)
Black solid/dotted - HIRS, blended with calibration method 1/2
Brown - HIRS from individual satellites: NOAA11, 12, 14, 15, 16
Global Mean
Magenta - CERES (TRMM, Terra, Aqua)
Black solid/dotted - HIRS, blended with calibration method 1/2
Brown - HIRS from individual satellites: NOAA11, 12, 14, 15, 16
9
Instrument Continuity & Algorithm Consistency
N10-N09
N11-N10
HIRS/2
N14-N11
HIRS/2
vs.
HIRS/2I
N15-N14
HIRS/2I
HIRS/2I
vs.
HIRS/3
10
Collocation occurs in the
rhombus ABCD:
B
N06
C
AB & CD: ZA5 > ZA6
BC & AD: ZA6 > ZA5
A
D
N06 > N05
N05
N06 < N05
11
12
Summary
 The HIRS OLR inter-satellite calibration method
worked very well. It significantly reduced the
discontinuity in the OLR time series that were
introduced by various sources of inconsistency and
errors.
 Identified inconsistent OLR retrievals caused by the
changes in HIRS instruments and OLR algorithm.
 Found some large OLR differences that might point to
the limb correction and/or modeling for extreme
conditions.
 Call for revision of the Operational algorithm.
 This procedure may apply to the inter-satellite
calibration of other thematic CDR products.
13
Backup Slide
15
Multi-spectral HIRS OLR Algorithm



I (zt ;, )   B (0)T (zt ,0; ,  ) 
OLR 

2
1
0
0
0
  
N i ( ) 


0
T (zt , z;, )
B (z)
dz
z

I (zt; ,  )ddd

i

zt
I (zt , ) f i ( )d
OLR  a0 ( )   ai ( ) N i ( )
  cos()
=local zenith angle
ai=regression coefficients
i
(Ellingson et al., 1989)
16
Validation of Multi-spectral OLR Algorithms
Ellingson et al., 1994: Validation of a
Ba et al., 2003: Validation of a
technique for estimating outgoing longwave
radiation from HIRS radiance observations
J. Atmos. Ocean. Technol., 11, 357-365.
technique for estimating OLR with the
GOES sounder. J. Atmos. Ocean. Technol.,
20, 79–89.
17
NOAA16
HIRS/3
GOES8
Sounder
Channel selections
GOES8
Imager
GOES-R
ABI
OLR Spectrum
Mid-lat Summer
18
Earth Radiation Budget
Kiehl and Trenberth, 1997. Bull. Amer. Meteor. Soc., 78, 197-208.
19