Cal/Val-Related Activities at
CICS
Andy Harris, Ralph Ferraro
Hai-Tien Lee
CICS
• Is the NOAA Cooperative Institute for Climate
Studies
• Is based within the Earth System Science
Interdisciplinary Center at U. Maryland
• CICS is generally tilted towards
– Climate
– Geophysical applications
• Calibration is important, but not “primary
function”
• But it does matter for what we do – sometimes
folks have to get their hands dirty!
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
Improvement with inter-satellite calibration
The blended HIRS monthly mean OLR data agrees better with
CERES with the adjustments determined by inter-satellite
calibration.
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
AMSU-A Asymmetry
• Preliminary coefficients
derived using 18 days of
AVHRR, GDAS and AMSUA data
– Clear sky
– RT calculations
• Asc/Dsc (example is Asc)
• Expand to 30 days of
matchups
• Testing impact in MSPPS,
in particular, TPW and CLW
Product Impacts – Cloud Liquid Water (CLW)
After asymmetry coeff’s implemented
RTM improvements: GOES-9 Case Study
Unusually large scatter
and warm bias at low
atmospheric corrections
may be due to diurnal
warming
Nighttime retrievals also
show small trend vs
atmospheric correction
Updated RT model
removes most of the
trend
Application of daytime
coefficients to nighttime
data gives small –ve
bias (expected)
Summary
• Climate-focused work in particular requires very
stringent calibration – at least of the end-product
• Study of products can often highlight areas
which feed back into more fundamental aspects,
such as calibration and forward modeling
• Other product areas include snow mapping &
aerosols
• Some applications have more complex
responses to calibration errors – often have to
be “tuned” (e.g. ocean color → biological model)