Calibration / Validation
The Big Picture
Paul Menzel
NOAA/NESDIS/ORA
Cooperative Institute for Meteorological Satellite Studies (CIMSS)
Madison, Wisconsin
July 2005
Building blocks for Cal/Val
Obs from all vantage points
Integrated Earth Observing System
Climate Monitoring Principles
The building blocks for a calibration / validation system
include
(1) on-board calibration devices (e.g. black bodies, solar
diffusers),
(2) in situ measurements of the state of the surface and
atmosphere (e.g. DOE Cloud and Radiation Testbed
(CART) site, aircraft instruments with NIST
calibrations),
(3) radiative transfer models that enable comparison of
calculated and observed radiances, and
(4) assimilation systems that merge all measurements
into a cohesive consistent depiction of the earthatmosphere system.
Space-based component of the Global
Observing System
Infrared composite image – enhanced to show discontinuities
GOES vs. POES IR Window
Courtesy of Gunshor et al.
Leo – Geo intercomparisons
results are being posted at
http://cimss.ssec.wisc.edu/goes/intercal.
Intersatellite Calibration of POES and GOES using SNO
POES
GOES vs. POES
SNO: Simultaneous Nadir Overpass
SNO Studies at NOAA/NESDIS
•Spectral cal of
HIRS using AIRS
•Detected onboard
calibration
anomalies (HIRS)
AIRS
HIRS
•Evaluate the effect
of spectral
uncertainties on
climate trending
•Monitor cal
coeff. updates
(AVHRR VIS)
•Intersatellite cal (1980-2003) to improve
consistency for climate studies (AVHRR&HIRS)
Channel 4, Antarctic
• Quantify biases between
MODIS and AVHRR/N-16
(uncertainties < NEDT)
8
Brightness temperature (K)
7
• Study MODIS striping in
the SST bands
6
5
4
3
NOAA09
NOAA10
NOAA11
NOAA12
NOAA14
NOAA15
NOAA16
v.s.
v.s.
v.s.
v.s.
v.s.
v.s.
v.s.
NOAA10
NOAA11
NOAA12
NOAA14
NOAA15
NOAA16
NOAA17
Nonlinearity effect
2
1
0
-1
-2
-3
-4
1988 1990 1992 1994 1996 1998 2000 2002 2004
AVHRR-MODIS
Year
AIRS spectrum and Aqua MODIS Band
Spectral Response Functions
wavenumber
MODIS Band /
wavelength(mm)
36
35
34
33
32
31
/
/
/
/
/
/
14.2
13.9
13.7
13.4
12.0
11.0
30
29
28
27
/ 11.0
/ 9.7
/ 7.3
/ 6.8
25
24
23
22
21
/
/
/
/
/
4.5
4.4
4.1
4.0
4.0
Fantastic AIRS - MODIS Agreement for Band 22 (4.0mm)!
AIRS Tb (K)
AIRS Histogram
MODIS
AIRS minus MODIS (K)
Uniform Scenes
Selected
Summary of AIRS-MODIS
mean Tb differences
Red=without accounting for convolution error
Blue=accounting for convolution error with mean
correction from standard atmospheres
p-p Convolution Error (CE) Estimate
Band
21
22
23
24
25
27
28
30
31
32
33
34
35
36
Diff
0.10
-0.05
-0.05
-0.23
-0.22
1.62
-0.19
0.51
0.16
0.10
-0.21
-0.23
-0.78
-0.99
CE
-0.01
-0.00
0.19
0.00
0.25
-0.57
0.67
-0.93
-0.13
0.00
0.28
-0.11
0.21
0.12
Diff
0.09
-0.05
0.14
-0.22
0.03
1.05
0.48
-0.41
0.03
0.10
0.07
-0.34
-0.57
-0.88
Std
0.23
0.10
0.16
0.24
0.13
0.30
0.25
0.26
0.12
0.16
0.21
0.15
0.28
0.43
N
187487
210762
244064
559547
453068
1044122
1149593
172064
322522
330994
716940
1089663
1318406
1980369
Band
mm
Atmospheric transmittance in
CO2 sensitive region of spectrum

Studying spectral sensitivity
with AIRS Data

AIRS BT[747.8] – BT[747.4]
Spectral change of 0.4 cm-1
causes BT changes > 8 C
The building blocks for a calibration / validation system
include
(1) on-board calibration devices (e.g. black bodies, solar
diffusers),
 (2) in situ measurements of the state of the surface and
atmosphere (e.g. the Cloud and Radiation Testbed
(CART) site, aircraft instruments with NIST
calibrations),
(3) radiative transfer models that enable comparison of
calculated and observed radiances, and
(4) assimilation systems that merge all measurements
into a cohesive consistent depiction of the earthatmosphere system.
AVHRR VIS/NIR Vicarious Calibration
using the Libyan Desert Target
–NOAA 16 AVHRR Albedo
–NOAA 17 AVHRR Albedo
CH1
CH2
CH3
Courtesy of X. Wu
Degradation of GOES-8 Imager Ch
(Sonora; 1994.294 - 2001.088)
280
Degradation rate: 4.662%/ye
260
Adjusted Counts
240
220
200
180
160
140
120
0
400
800
1200
1600
2000
2400
2800
Days after launch
Adjusted count
Courtesy M. Weinreb
fitted 29494-08801fitted 29494-27399
Satellite Calibration
requires in-situ measurements
• Satellite observations are improving significantly
with respect to radiometric calibration and stability this is particularly true for high spectral infrared
measurements
• Accuracy of satellite retrievals are dependent on
very accurate in situ observations (radiative transfer
modeling)
• Conversely, accurate satellite observations are
needed to calibrate in situ observations
• Coincident (temporal and spatial) satellite
observations and in situ observations are essential
Need to sustain & expand DOE ARM Sites
To completely characterize the atmosphere and surface to develop
accurate radiative transfer models the following are needed (partial
list)
– High quality Sondes
– Raman Lidar (RL) to measures vertical profiles of water-vapor
mixing ratio and several cloud- and aerosol-related quantities.
– The Microwave Radiometer (MWR) provides time-series
measurements of column-integrated amounts of water vapor and
liquid water.
– Ground-based GPS for total water – systematic difference.
– Carbon Dioxide Flux Instruments (CO2FLX)
– Millimeter-Wavelength Cloud Radar (MMCR) The main purpose
of this radar is to determine cloud boundaries (e.g., cloud bottoms
and tops).
– Downward/Upward looking ground-based IR interferometer to
characterize surface.
CART Raman Lidar (CARL)
• Water vapor, aerosol,
depolarization profiles
• Precipitable water vapor and
aerosol optical thickness
(355 nm)
• Designed for continuous,
autonomous (24/7) operation
• Operational retrievals since 1998
(~50-60% uptime)
• Data available via ftp from ARM
(http://www.arm.gov)
December 3, 1998
Water Vapor Mixing Ratio
Relative Humidity
(Turner et al., JAOT, 2002)
Aerosol Backscatter (355 nm)
Aerosol Extinction (355 nm)
Optical
depth
ELF
time
CREST student Ray Rogers at UMBC worked with ORA at UW/CIMSS
to compare MODIS (multi-spectral VIS/NIR/IR) and ELF (lidar)
detection of thin cirrus
MWR TPW comparison with
GOES, MODIS, and RAOB
ATMOSPHERIC EMITTED RADIANCE INTERFEROMETER (AERI)
Clear Sky and Cloud Downwelling Emission
H 2O
140
Radiance
120
CLOUD
100
CO2
80
800
60
900
O3
40
20
850
CLEAR
0
600
800
1000
1200
1400
Wavenumber
Operational at DOE ARM
Accurate High Resolution Radiometry
Continuous Atmospheric Profiling - Temperature and Water Vapor
Need Aircraft and Ship Campaigns
• Include advanced instruments:
–
–
–
–
Very high spectral resolution interferometers
Advanced microwave instruments
Cloud /aerosol lidars
Dropsondes
• Objective to validate satellite radiances, improve
radiative transfer models and retrieval algorithms in
cloudy atmospheres, and to better understand physical
processes. Improve model physics.
S-HIS zenith and cross-track scanning Earth views
11-16-2002 from Proteus @ ~14km
UW Scanning HIS
(HIS: High-resolution Interferometer Sounder, 1985-1998)
Characteristics
Spectral Coverage: 3-17 microns
Spectral Resolution: 0.5 cm-1
Resolving power: 1000-6000
Footprint Diam: 1.5 km @ 15 km
Cross-Track Scan: Programmable
including uplooking zenith view
CO2
Midwave
CH4/N2O
O3
H2O
Longwave
H2O
Shortwave
N2O
CO2
CO
Applications:
Radiances for
Radiative Transfer
• Temp & Water Vapor
Retrievals
• Cloud Radiative Prop.
• Surface Emissivity & T
• Trace Gas Retrievals
•
Scanning-HIS Radiometric
Calibration 3-sigma Error Budget
SW
SW
MW
LW
0.2 K 3-sigma
240K
310K
MW
LW
0.2 K 3-sigma
220K
Scene Brightness temperature
ER2, 21 Nov 2002
Proteus,16 Nov 2002
TABB = 260K, THBB = 310K
TABB = 227K, THBB = 310K
310K
Okavanga Delta Surface Emissivity
( 27 August 2000)
Tb (980 cm-1)
Tb (980) - Tb(1125)
+6
-2
T=5 K
Tb(K)
750 cm-1
1250 cm-1
Intercomparison of 2 Marine AERIs
Measuring Sea Surface Temperature
16 Day Cruise
Hawaii
New Zealand
Track of the R/V Roger Revelle
28 Sept. - 14 Oct. 1997
Largest Daily Mean Difference: 0.020 K
Ten Day Mean Difference:
0.005 K
MAERI marks solar heating of sea surface skin
MAERI high spectral resolution detects
daytime surface skin heating in clear skies
Skimmer (green) warmer at night and cooler in day
 solar heating
showing up
Need GPS RO Data
•Limb sounding geometry complementary to ground and
space nadir viewing instruments
•High accuracy (equivalent to < 1 deg K from 5-25 km)
•High vertical resolution (0.1 km surface - 1km tropopause)
•All weather-minimally affected by aerosols, clouds or
precipitation
•Independent height and pressure
•Requires no first guess sounding
•Independent of radiosonde calibration
•No instrument drift
•No satellite-to-satellite bias
In Situ data need satellites, too
• Very accurate satellite observations are needed for
calibration
– Radiosonde temperatures systematic errors are dependent
on the radiative environment, which is a function of
day/night, solar angle.
– Corrections are not perfect.
– Intercalibration of different radiosonde instruments is also
not perfect, because biases are dependent on the radiative
environment
– Use satellite data to detect, adjust and understand
differences in the radiosonde record.
Satellites can
serve as
transfers
standards to
monitor
radiosondes
VIZ B to Vaisala (RS80) at Chuuck Island
Validation of Mid- Upper-Trop WV
Averages over
hundreds of sonde
launches over 3 years,
5+ sites.
Possible RS-90
day vs night bias
Day/Night Bias versus Altitude
 (cm-1)
Obs B(T) is proxy for
altitude.
Day/Night bias increases
with altitude.
Highly variable with site, so
this result questionable.
dB(T)/dQ = ~1K (for dQ =
10%) at high altitudes, so this
represents ~10% water
variability.
Lower altitude global bias is
about +-3-5% water.
The building blocks for a calibration / validation system
include
(1) on-board calibration devices (e.g. black bodies, solar
diffusers),
(2) in situ measurements of the state of the surface and
atmosphere (e.g. the Cloud and Radiation Testbed
(CART) site, aircraft instruments with NIST
calibrations),
 (3) radiative transfer models that enable comparison of
calculated and observed radiances, and
(4) assimilation systems that merge all measurements
into a cohesive consistent depiction of the earthatmosphere system.
Observed and Caculated zenith views from Proteus @ ~14km
Calculated
Observed
Calculation based on 18Z ECMWF analysis, with 0.0004 cm H2O above 14km
The building blocks for a calibration / validation system
include
(1) on-board calibration devices (e.g. black bodies, solar
diffusers),
(2) in situ measurements of the state of the surface and
atmosphere (e.g. the Cloud and Radiation Testbed
(CART) site, aircraft instruments with NIST
calibrations),
(3) radiative transfer models that enable comparison of
calculated and observed radiances, and
 (4) assimilation systems that merge all measurements
into a cohesive consistent depiction of the earthatmosphere system.
ECMWF Model Calculated
versus
GOES Observed Water Vapor Radiances
GOES-10
GOES-8
Data
Mean
GOES
minus
Model
Standard
Deviation
Data
Counts
GOES Imager 6.7 μm
Leo and Geo TPW Comparisons before assimilation in NWP Models
MODIS
GOES 8 & 10
North America Nighttime: June 2, 2001
Elements of Integrated Earth
Observing System
• Research and operational observation
instruments and platforms
• In situ and remote sensing observation
networks
• Communication links and computing capacity
• Research and applications development
• Scientific and mathematical algorithms to
combine multiple-source data
• Sustained Cal/Val Program
Calibration measurements from all vantage points
GCOS Climate Monitoring Principles
Satellite systems for monitoring climate need to:
(a) Take steps to make radiance calibration, calibration-monitoring and satellite-to-satellite cross-calibration of the
full operational constellation a part of the operational satellite system; and
(b)
Take steps to sample the earth system in such a way that climate-relevant (diurnal, seasonal, and long-term
interannual) changes can be resolved.
Thus satellite systems for climate monitoring should adhere to the following
specific principles:
11. Constant sampling within the diurnal cycle (minimizing the effects of orbital decay and orbit drift) should be
maintained.
12. A suitable period of overlap for new and old satellite systems should be ensured for a period adequate to
determine inter-satellite biases and maintain the homogeneity and consistency of time-series observations.
13. Continuity of satellite measurements (i.e. elimination of gaps in the long-term record) through appropriate
launch and orbital strategies should be ensured.
14. Rigorous pre-launch instrument characterization and calibration, including radiance confirmation against an
international radiance scale provided by a national metrology institute, should be ensured.
15. On-board calibration adequate for climate system observations should be ensured and associated instrument
characteristics monitored.
16. Operational production of priority climate products should be sustained and peer-reviewed new products
should be introduced as appropriate.
17. Data systems needed to facilitate user access to climate products, metadata and raw data, including key data
for delayed-mode analysis, should be established and maintained.
18. Use of functioning baseline instruments that meet the calibration and stability requirements stated above
should be maintained for as long as possible, even when these exist on de-commissioned satellites.
19. Complementary in-situ baseline observations for satellite measurements should be maintained through
appropriate activities and cooperation.
20. Random errors and time-dependent biases in satellite observations and derived products should be identified.