JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, D09S14, doi:10.1029/2005JD006103, 2006
Atmospheric Radiation Measurement site atmospheric state best
estimates for Atmospheric Infrared Sounder temperature and water
vapor retrieval validation
David C. Tobin,1 Henry E. Revercomb,1 Robert O. Knuteson,1 Barry M. Lesht,2
L. Larrabee Strow,3 Scott E. Hannon,3 Wayne F. Feltz,1 Leslie A. Moy,1 Eric J. Fetzer,4
and Ted S. Cress5
Received 20 April 2005; revised 14 June 2005; accepted 19 July 2005; published 16 March 2006.
[1] The Atmospheric Infrared Sounder (AIRS) is the first of a new generation of advanced
satellite-based atmospheric sounders with the capability of obtaining high–vertical
resolution profiles of temperature and water vapor. The high-accuracy retrieval goals of
AIRS (e.g., 1 K RMS in 1 km layers below 100 mbar for air temperature, 10% RMS in 2 km
layers below 100 mbar for water vapor concentration), combined with the large temporal and
spatial variability of the atmosphere and difficulties in making accurate measurements of the
atmospheric state, necessitate careful and detailed validation using well-characterized
ground-based sites. As part of ongoing AIRS Science Team efforts and a collaborative effort
between the NASA Earth Observing System (EOS) project and the Department of Energy
Atmospheric Radiation Measurement (ARM) program, data from various ARM and other
observations are used to create best estimates of the atmospheric state at the Aqua overpass
times. The resulting validation data set is an ensemble of temperature and water vapor
profiles created from radiosondes launched at the approximate Aqua overpass times,
interpolated to the exact overpass time using time continuous ground-based profiles,
adjusted to account for spatial gradients within the Advanced Microwave Sounding Unit
(AMSU) footprints, and supplemented with limited cloud observations. Estimates of the
spectral surface infrared emissivity and local skin temperatures are also constructed. Relying
on the developed ARM infrastructure and previous and ongoing characterization studies of
the ARM measurements, the data set provides a good combination of statistics and accuracy
which is essential for assessment of the advanced sounder products. Combined with the
collocated AIRS observations, the products are being used to study observed minus
calculated AIRS spectra, aimed at evaluation of the AIRS forward radiative transfer model,
AIRS observed radiances, and temperature and water vapor profile retrievals. This paper
provides an introduction to the ARM site best estimate validation products and characterizes
the accuracy of the AIRS team version 4 atmospheric temperature and water vapor retrievals
using the ARM products. The AIRS retrievals over tropical ocean are found to have very
good accuracy for both temperature and water vapor, with RMS errors approaching the
theoretical expectation for clear sky conditions, while retrievals over a midlatitude land site
have poorer performance. The results demonstrate the importance of using specialized
‘‘truth’’ sites for accurate assessment of the advanced sounder performance and motivate the
continued refinement of the AIRS science team retrieval algorithm, particularly for retrievals
over land.
Citation: Tobin, D. C., H. E. Revercomb, R. O. Knuteson, B. M. Lesht, L. L. Strow, S. E. Hannon, W. F. Feltz, L. A. Moy, E. J.
Fetzer, and T. S. Cress (2006), Atmospheric Radiation Measurement site atmospheric state best estimates for Atmospheric Infrared
Sounder temperature and water vapor retrieval validation, J. Geophys. Res., 111, D09S14, doi:10.1029/2005JD006103.
1
Cooperative Institute for Meteorological Satellite Studies, Space
Science and Engineering Center, University of Wisconsin, Madison,
Wisconsin, USA.
2
Argonne National Laboratory, Argonne, Illinois, USA.
3
Department of Physics, University of Maryland Baltimore County,
Baltimore, Maryland, USA.
4
NASA Jet Propulsion Laboratory, Pasadena, California, USA.
5
Pacific Northwest National Laboratory, Richland, Washington, USA.
Copyright 2006 by the American Geophysical Union.
0148-0227/06/2005JD006103$09.00
1. Introduction
[2] Temperature and water vapor soundings from satellites have been available for more than 35 years, beginning
with the launch of two pioneering sensors on the Nimbus 3
satellite in April 1969 [e.g., Wick, 1971]. By providing
twice daily atmospheric profiles on a global scale, the
satellite soundings were expected to provide significant
improvements in numerical weather prediction. Because
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of improved data assimilation methods, use of the satellite
data for model initialization, and expanded computer
resources, many significant improvements in forecast accuracy have indeed been realized. However, the relatively low
vertical resolution provided by these broadband sensors,
such as the Advanced TIROS Operational Vertical Sounder
(ATOVS) on NOAA’s current Polar Operational Environmental Satellite, has limited the benefit of these satellite
observations for improved weather forecasting. In many
circumstances, for example, the low vertical resolution is
responsible for large absolute errors as well as reduced
horizontal variance, and strong horizontal error correlation
of the satellite soundings [e.g., Phillips, 1980]. In order to
provide further advances in numerical weather prediction
and climate studies, the requirement for sensors and algorithms with improved vertical resolution and with an ability
to handle partially cloudy scenes was recognized. Beginning in the mid-1970s, extensive retrieval algorithm development, instrument design, and experimental field studies
were then conducted to determine the feasibility and
requirements of a future advanced sounder; the reader is
referred to Smith [1991] for an overview of the evolution of
the satellite sounder to achieve high vertical resolution and
improve weather prediction. As a result of these efforts,
specific requirements for the NOAA/NASA ‘‘Interagency
Sounder’’ were established in the late 1980s, providing
motivation for the Atmospheric Infrared Sounder (AIRS)
as part of NASA’s Earth Observing System (EOS).
[3] Launched into orbit on 4 May 2002 on the EOS Aqua
platform, AIRS is the first of a new generation of satellitebased advanced infrared sounders, designed to provide data
with higher vertical resolution and accuracy to numerical
weather prediction centers for improved medium range
weather forecasting. Aumann et al. [2003] present a thorough overview of the science objectives, data products,
retrieval algorithms, and ground data processing concepts of
AIRS. Exploiting the higher vertical resolution made possible by high spectral resolution and the combined use of
Advanced Microwave Sounding Unit (AMSU) microwave
and infrared radiances in the presence of clouds, AIRS is
expected to produce retrievals of temperature and water
vapor with high accuracy under clear and partly cloudy
conditions (for scenes with cloud fraction up to 80%). The
AIRS retrieval performance requirements are stated in terms
of RMS errors for vertically averaged layers in the troposphere. The required performance is 1 K RMS in 1 km
vertical layers below 100 mbar for temperature profile
retrievals and 20% (10% goal) RMS in 2 km vertical layers
below 100 mbar for water vapor profile retrievals. There are
no documented requirements for the yield or mean error
(bias) of the retrieved profiles.
[4] Detailed characterization of the accuracy of the satellite products is required for many applications of the data.
The high-accuracy retrieval goals of the advanced sounders,
combined with the temporal and spatial variability of the
atmosphere and difficulties in making accurate measurements of the atmospheric state, necessitates careful validation using well-characterized ground-based sites. Starting in
the early 1990s the Department of Energy Atmospheric
Radiation Measurement (ARM) program has developed
several ground sites around the world to collect detailed
measurements of the atmospheric state and coincident
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radiation with an overall goal of improving the representation of radiation and clouds in climate models [Stokes and
Schwartz, 1994]. The ARM Climate Research Facility
ground sites have matured and are now well suited for
providing data sets that are both accurately characterized
and statistically significant for performing satellite validation [Ackerman and Stokes, 2003]. As part of an ongoing
AIRS Science Team effort initiated under team member Dr.
H. E. Revercomb and continuing under a collaborative
effort between the NASA EOS project and the ARM
program, data from the heavily instrumented ARM sites
are used to create ‘‘best estimates’’ of the atmospheric state
and surface properties at the Aqua overpass times. Combined with the colocated AIRS observations, the ARM
products are being used for various studies, including
assessment of clear sky observed minus calculated radiance
spectra, aimed at development and validation of the AIRS
clear sky forward radiative transfer model. Validation of the
AIRS clear sky radiative transfer algorithm using the ARM
best estimate and other data is presented by Strow et al.
[2006].
[5] The primary purpose of this paper is to provide a
description of ARM site best estimate validation products
(section 2) and, using the ARM products, to provide an
objective and accurate characterization of the AIRS Team
version 4 atmospheric temperature and water vapor retrievals (section 3). Section 4 summarizes the results of this
study and plans for further development and applications of
the ARM validation data.
2. ARM Site Atmospheric State and Surface
Best Estimate Products
[6] Best estimates of vertical profiles of atmospheric
pressure, air temperature, water vapor concentration, and
surface parameters are produced for Aqua overpasses of the
ARM sites using ARM and other data. This section includes
an introduction to the ARM sites included in this effort, a
description of what data are used and how the best estimate
products are created, and a discussion of the accuracy and
statistical significance of the products for AIRS temperature
and water vapor profile retrieval validation.
[7] Three ARM sites, shown in Figure 1, are included in
this validation effort. These include the Tropical Western
Pacific (TWP) site on the small equatorial island of Nauru,
the Southern Great Plains (SGP) central facility site in north
central Oklahoma, and the North Slope of Alaska (NSA)
site near Barrow, Alaska. These sites were chosen by ARM
to represent three of Earth’s primary climate regimes.
Covering a range of conditions with varying degrees of
retrieval difficulty, the sites are also well suited for characterizing the AIRS retrieval performance. The TWP Nauru
site has been very instrumental in AIRS validation because
it is an ocean site with well-known surface emissivity, high
water vapor amounts, and low variability in the atmospheric
temperature and water vapor. This provides a valuable data
set for validation of various components of the AIRS
molecular absorption and radiative transfer models and
provides the most straightforward retrieval conditions. Operational since 1991, the ARM SGP central facility site is
one of the most heavily instrumented ground-based sites in
the world for atmospheric state and radiation measurements.
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Figure 1. Locations of the ARM sites. Southern Great Plains (SGP) site in north central Oklahoma
(97.5W, 36.6N), North Slope of Alaska (NSA) site in Barrow Alaska (156.6W, 71.3N), and Tropical
Western Pacific (TWP) site on the small equatorial island of Nauru (166.9E, 0.5S).
Numerous detailed experiments have been conducted at the
SGP site in order to characterize and improve the accuracy
of ARM’s temperature and water vapor profiling capabilities. The site is located within a large agricultural region and
experiences wide fluctuations on many timescales in the
atmospheric temperature and water vapor, surface conditions, and cloud conditions. Having to deal with these
conditions, most notably the complex and unknown surface
emission, satellite retrievals are more difficult at the SGP
site. The NSA site provides data of clouds, atmospheric
state, and radiative processes for cold and dry conditions. It
is located near the coastal town of Barrow on the Arctic
Ocean, with surrounding areas including open ocean, sea
ice, coastal inland tundra, and fresh water ecosystems.
Passive remote sounding of arctic atmospheres is historically very difficult because of low contrast of clouds and the
variable surface, and the NSA site serves as one of the most
difficult sites for AIRS retrievals.
[8 ] Each ARM site is instrumented with numerous
ground-based sensors to measure wind speed and direction,
water vapor and liquid water, temperature, cloud properties,
aerosols, and radiation. For AIRS temperature and water
vapor retrieval validation, the primary profile measurements
are made with Vaisala RS-90 radiosondes [Paukkunen et al.,
2001]. Depending on the particular site, radiosondes are
typically launched two to four times per day at the ARM
sites. These normally scheduled launches were supplemented to provide an optimal data set for AIRS validation.
Three ‘‘phases’’ of dedicated radiosonde launches have
been completed at each of the three ARM sites as of
October 2004. During these periods, two radiosondes are
launched for Aqua overpasses of the ARM sites. The first
radiosonde is launched 45 to 90 min prior to the overpass
time and the second is launched just before (5 min) the
overpass time, providing radiosonde measurements of the
upper and lower troposphere at the AIRS overpass time. At
the SGP and NSA sites, this is accomplished using two
radiosonde receivers, allowing two radiosondes to be
tracked simultaneously. At the TWP site, a single receiver
is used and the first sounding is terminated prior to initiating
the second. Each dedicated radiosonde launch phase
includes approximately 90 overpasses (180 radiosondes)
spanning several months and including both day and
nighttime overpasses. Beginning and ending dates of each
phase are listed in Table 1. On a given day, the dedicated
launches are performed for the overpass with the smallest
local zenith viewing angle. The launches are performed for
all conditions except for totally overcast or precipitating
conditions (conditions also for which full infrared plus
microwave AIRS retrievals are not performed) or for local
zenith angles greater than 30 degrees. Additional dedicated
launch periods are scheduled for later in 2005.
[9] Each Vaisala radiosonde water vapor profile is multiplied by a height-independent scale factor such that the
total column precipitable water vapor (pwv) of the radiosonde profile is equal to that measured simultaneously by a
ground-based two-channel microwave radiometer (MWR).
This ‘‘microwave scaling’’ technique for the radiosonde
water vapor profiles has several important advantages
[Revercomb et al., 2003, and references therein] including
the removal of significant radiosonde batch-to-batch calibration variations and diurnal biases in the radiosonde total
column water vapor, and providing an absolute calibration
reference. The Goff Gratch formulation of vapor pressure
Table 1. Beginning and Ending Dates of Aqua Overpass Dedicated Radiosonde Launch Phases
Site
Phase 1
Phase 2
Phase 3
SGP
TWP
NSA
2 September 2002 to 25 May 2003
15 September 2002 to 29 May 2003
1 September 2002 to 27 December 2002
9 September 2003 to 7 February 2004
8 September 2003 to 20 March 2004
10 September 2003 to 8 February 2004
2 April 2004 to 10 August 2004
2 April 2004 to 29 September 2004
23 April 2004 to 5 September 2004
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Figure 2. Time series of various SGP ARM data on 25 July 2002. (top) Cross section of AERI+
temperature retrievals (with color scale and left-hand altitude scale) and time series of the near-surface air
temperature measured with a surface met station (dotted shaded curve) and the surface temperature
measured with a down-looking broadband IRT (dashed black curve), both with the right-hand y axis
scale. (bottom) Cross section of AERI+ relative humidity retrievals (with color scale and left-hand
altitude scale) and time series of total column precipitable water vapor measured by the MWR (dashed
black curve with right-hand y axis scale). In both panels the Aqua overpass time (black vertical dashed
line) and radiosonde trajectories (four solid black curves) are overlaid.
over liquid water [Smithsonian Institution, 1984] is used to
compute mass mixing ratio profiles from the radiosonde
temperature and relative humidity measurements.
[10] To account for temporal differences between the
AIRS overpass and the radiosonde measurements, time
continuous profiles of temperature and water vapor at the
ARM site are used to interpolate the radiosonde profiles to
the overpass time. At the SGP site, AERI-plus [Feltz et al.,
2003] profiles of temperature and water vapor are used for
this. The AERI-plus retrievals are derived from groundbased downwelling spectral radiance measurements by the
Atmospheric Emitted Radiance Interferometer (AERI) and
from coincident model analysis fields, providing vertical
temperature and water vapor profiles every 6 to 8 min with
high vertical resolution in the boundary layer, and lower –
vertical resolution profiles extending to 2 mbar. The
AERI-plus fields are obtained at the (altitude-dependent)
radiosonde observation times and at the satellite overpass
time, and the relative changes in the fields are used to
interpolate the radiosonde profile to the overpass time. This
is performed by adding AERI-plus temperature differences
to the radiosonde profile and multiplying the radiosonde
water vapor profile by AERI-plus water vapor amount
ratios. This is done independently for each altitude bin of
the AERI-plus fields. With this approach, the drift of the
radiosonde position with height is not taken into consideration. The time interpolation is performed for the two
radiosondes with launch times bounding the overpass time,
and the two profiles are then combined in a weighted mean,
with the weights determined by the time difference and by
the magnitude of change in the AERI-plus fields between
the observation and overpass times. Only radiosondes that
reach a minimum air pressure of 200 mbar are used. AERIplus time cross sections and radiosonde trajectories for an
example nighttime SGP site overpass on 25 July 2002 are
shown in Figure 2. If the AERI-plus fields are not available
for a given case, fields from the Rapid Update Cycle (RUC2 [Benjamin et al., 2004]) are used. In the event that neither
AERI-plus nor RUC-2 fields are available, simple linear
interpolation between the two bounding radiosonde profiles
is used to compute the profile at the overpass time. Note that
at the SGP site, continuous water vapor retrievals are
available from a Raman Lidar. This sensor, however, was
not fully operational during the past two years [Turner and
Goldsmith, 2005] and it has therefore not been included in
the production of the best estimate profiles to date.
[11] Figure 3 displays estimates of the short-term variability of the temperature and water vapor profiles at the
SGP site at the Aqua overpass times, utilizing differences
between the pairs of microwave scaled radiosondes
launched 60 min apart during the three Aqua overpass
dedicated launch phases. Differences for individual high –
vertical resolution profiles are shown. Analogous to how the
AIRS retrieval validation statistics are computed (as de-
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Figure 3. Short-term temporal variability at the SGP site. Differences between pairs of dedicated
radiosonde profiles are shown, one launched 60 min before the Aqua overpass and one launched at the
overpass time. (top) Daytime overpasses and (bottom) nighttime overpasses; (left) temperature
differences and (right) percent difference in water vapor amounts. In each panel the light shaded curves
are differences for individual profiles, and the dashed and solid curves are the mean and RMS differences,
respectively, for 1 km (temperature) and 2 km (water vapor) thick layers. MWR total column water vapor
scaling has been applied to each radiosonde water vapor profile as discussed in the text.
scribed in more detail in section 3), the mean differences
(biases) and RMS differences for 1 km (temperature) and
2 km (water vapor) vertical layering are also shown. Note
that in this process, the individual profiles are first reduced
to lower vertical resolution and then the bias and RMS
statistics are computed; this has the dramatic effect of
suppressing many of the largest errors associated with
vertically fine-scale structure present in the high – vertical
resolution profiles. Throughout the troposphere for both day
and night cases, the temperature RMS differences range
from 0.5 to 1.0 K, and the water vapor RMS percent
differences range from 10 to 25%, as shown in Figure 3.
Variability at the TWP site is less, with temperature RMS
differences ranging from 0.3 to 0.7 K, and water vapor RMS
percent differences ranging from 7 to 15%. Considering the
AIRS retrieval goals, this temporal variability is significant.
This emphasizes the importance of minimizing the temporal
mismatches between the ARM and AIRS measurement times
when attempting to accurately characterize the AIRS retrievals, and the need for the temporal interpolation approach
described previously. Note that the mean temperature difference for the lowest altitude bin for daytime overpasses is
nonzero, which can be attributed to the warming typically
experienced at the SGP site in the early afternoon.
[12] The AIRS retrievals are performed using a combination of a 3 by 3 array of AIRS footprints and one AMSU
footprint and have footprint diameters ranging from 50 km
at nadir to 150 km at the edge of scan. Spatial gradients
can be significant on this scale, and the local ARM site
profiles are therefore adjusted to account for large-scale
spatial gradients and the location of the ARM site within the
AMSU footprint. At the SGP site, this is accomplished
using 4 km spatial resolution Geostationary Environmental
Operation Satellite (GOES) retrievals [Ma et al., 1999]. The
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Figure 4. Spatial maps of 4-km GOES 8 products for the SGP Aqua overpass on 25 July 2002 at
08:35 UTC: (a) band 8 (11.0 mm) brightness temperatures, (b) near-surface pressure, (c) near-surface air
temperature, and (d) near-surface water vapor. In each panel the 3 by 3 array of AIRS footprints, the
AMSU footprint, and the ARM site location (star) are shown.
hourly GOES fields are linearly interpolated to the overpass
time. As with the temporal interpolation, the spatial adjustment is performed in a relative sense, where relative differences in the GOES fields are computed between the GOES
footprint closest to the ARM site and the mean of all GOES
footprints within the AMSU footprint. The profile adjust-
ments are performed using temperature differences and
water vapor amount ratios and are done independently for
each altitude bin of the GOES retrievals. Sample spatial
gradients for the 25 July 2002 SGP overpass are shown in
Figure 4. Analogous to Figure 3 in regard to short-term
temporal variability, Figure 5 displays the differences be-
Figure 5. Spatial variability at the SGP site. Differences between local ARM site profiles and those
which have been adjusted to account for large-scale spatial gradients and the location of the ARM site
within the AMSU footprints. (left) Temperature differences. (right) Percent difference in water vapor
amounts. In each panel the light shaded curves are differences for individual cases, and the dashed and
solid curves are the mean and RMS differences, respectively, for 1 km (temperature) and 2 km (water
vapor) thick layers.
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Figure 6. Sample temperature and water vapor profiles at the (top) TWP and (bottom) SGP sites.
tween local SGP site profiles and those which have been
adjusted to account for large-scale spatial gradients within
the AMSU footprints as discussed above. As with the
temporal differences, spatial gradients at the SGP site are
significant in terms of the ability to validate the AIRS
retrievals. Spatial variability for the tropical ocean is far
less than for land sites in the middle and high latitudes.
Attempts have been made to use data from the Geostationary Meteorological Satellite (GMS-5, prior to May 2003)
and GOES 9 (after May 2003) to account for the spatial
gradients at TWP. Various issues have been encountered in
obtaining and using high-spatial and high – temporal resolution atmospheric profiles from these data, however, and
spatial adjustments are not performed for the TWP site
profiles. Future work will involve the use of atmospheric
profile retrievals from the Moderate resolution Imaging
Spectroradiometer (MODIS) [Seemann et al., 2003] on
Aqua to account for the spatial gradients within the AMSU
footprints at all three sites.
[13] The high – vertical resolution pressure, temperature,
and water vapor profiles are interpolated to a standard
altitude grid and written to an output file for each Aqua
overpass of each site. The profiles extend to the maximum
height of the shortest radiosonde used in the processing,
with a minimum height of 200 mbar. Three versions of the
profiles are provided in the files, including the unmodified
profiles (before time and space interpolation) for the two
radiosondes with launch times bounding the overpass time,
the weighted mean profile with only the time interpolations
performed, and the weighted mean profile with both time
interpolation and spatial adjustment performed. Various
additional data included in the output files include cloud
base height data from a Vaisala ceiliometer, total column
liquid water vapor from the MWR, estimates of the skin
temperature and surface emissivity, and metadata regarding
the logistics of the case.
[14] Sample ARM best estimate profiles and sample
AIRS spectra for the TWP and SGP sites are shown in
Figures 6 and 7. In Figure 6, note the relatively low
temporal variability of the TWP site profiles and the large
variability of the SGP site profiles. Also note the relative
lack of high – vertical resolution structure in the TWP
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Figure 7. Sample AIRS spectra at the (top) TWP and (bottom) SGP sites.
profiles, as opposed to the SGP profiles. Figure 7 also
demonstrates the range of thermodynamic and cloud conditions that are observed at each site.
[15] In the following discussion regarding the accuracy of
the validation profiles and in the AIRS retrieval validation
analysis presented in section 3, only the TWP and SGP sites
are included; characterization and validation of the more
difficult polar atmospheres at the NSA site is deferred to a
subsequent paper. Numerous analyses have been performed
to characterize the accuracy of the ARM site temperature
and water vapor measurements [e.g., Clough et al., 1994;
Lesht and Liljegren, 1995; Tobin et al., 2002; Revercomb et
al., 2003; Turner et al., 2003, 2004; Ferrare et al., 2004;
Miloshevich et al., 2006; Whiteman et al., 2006]. Considering these various investigations in the context of AIRS
retrieval validation, the uncertainty of the ARM best estimate temperature profiles is estimated to be 0.2 K below
100 mbar and for water vapor the uncertainty is estimated to
be 3% below 500 mbar and 10% from 500 to 100 mbar.
These are estimates of the absolute uncertainties in the mean
biases for the TWP and SGP ensembles, due primarily to
sensor calibration uncertainties, and should be considered
when interpreting the mean biases of the AIRS retrievals
investigated in section 3.
[16] Uncertainties that contribute to individual profiles
and overpasses are more difficult to quantify and depend
directly on the temporal and spatial variability of the case,
the temporal and spatial collocation of the AIRS and ARM
measurements, and the uncertainty of the ARM measurements for those conditions. When reduced to 1 km and 2 km
vertical layering, the vertical resolution of the best estimate
profiles contributes a negligible uncertainty. When performing the microwave scaling, the MWR data are averaged
over a 20 minute period coincident with the radiosonde
ascent through the lower troposphere, and short-term variability in the MWR observations is also negligible. However, despite the previously described approaches to account
for temporal and spatial variability, we should expect some
real variability which is not accounted for in the best
estimate profiles. For example, even with the MWR scaling
approach, residual radiosonde calibration differences for the
radiosonde pairs can produce differences that are interpreted
as real atmospheric variability. Also, because of radiosonde
drift, the MWR, AERI, and radiosondes do not sample the
exact same air mass. Since these profiles are used as ‘‘truth’’
in the AIRS retrieval validations, the computed RMS
retrieval errors should therefore be considered as upper
bounds of the true errors. This is particularly true for the
SGP site which experiences large variability. We expect
negligible impact of this effect on the computed mean
biases since the residual variability is random from case
to case. Very rough estimates of the residual RMS contributions due to the residual variability at the SGP site,
computed as 20% of the observed short-term temporal
and spatial variability (Figures 3 and 5), are 0.2 K for
temperature and 7% for water vapor. Further discussion of
this issue is included in section 3.
[17] While significant improvements in radiosonde performance and characterization have been achieved in recent
years, measurements of low water vapor amounts in the
upper troposphere remain challenging. In this regard it
should be noted that candidate corrections to the Vaisala
RS-90 upper level water vapor profiles [Miloshevich et al.,
2006] are not included in this analyses, but are under
consideration for future analyses. The component of these
corrections which have the largest potential impact on the
AIRS retrieval assessments is based on comparisons of
eight nighttime radiosonde profiles with colocated measurements by a reference frost point hygrometer. For the TWP
phase 1 profiles, for example, these corrections produce an
overall moistening of the radiosonde profiles by approximately 15% from roughly 200 mbar to the tropopause, with
significant variations in the corrections from profile to
profile. Additionally, the Vaisala radiosonde water vapor
profiles are known to have a diurnal bias, with daytime total
column water vapor amounts 3 to 8% drier than nighttime
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Figure 8. (left) AERI measurements of the two dominant emissivity types used in the SGP emissivity
parameterization: bare soil (shaded) and vegetated (solid). (right) A coarse model of fractional vegetation
cover (bare soil (shaded) and vegetated (solid)) as a function of season used in the parameterization. The
time variation is based on the agricultural growth cycle typical of winter wheat production in north central
Oklahoma.
total column amounts, as demonstrated with coincident
MWR and downwelling infrared radiance observations
[e.g., Turner et al., 2003; Miloshevich et al., 2006]. In
terms of total column water vapor, the microwave scaling
approach accounts for this diurnal bias. Analyses of clear
sky observed and calculated AIRS radiances for upper level
water vapor spectral channels using day and nighttime
MWR scaled RS-90 profiles, however, demonstrate that
the diurnal bias is height-dependent, with the daytime RS90s drier by 5 to 8% in the upper troposphere [Strow et al.,
2006]. Impact of this bias on the AIRS retrieval validation is
investigated in section 3.
[18] As mentioned previously, estimates of the surface
emissivity and of the local surface skin temperature are
included in the best estimate products. Considering the size
of the AMSU footprints and the spatial nonhomogeneity of
the SGP and NSA sites, it is a difficult task to estimate the
effective skin temperature and emissivity without retrieving
the quantities from the AIRS data themselves. The skin
temperature and emissivity are included, however, along
with the best estimate profiles to provide initial estimates as
input for top-of-atmosphere radiance calculations. The local
skin temperature estimates are measured with downlooking
broadband infrared radiometers at the ARM sites. These
estimates are not expected to be representative of the larger
AMSU footprints. For observations over ocean, the surface
emissivity is homogeneous and well known [Masuda et al.,
1988; Wu and Smith, 1997]. Over land, however, the
spectral surface emissivity can vary largely in both time
and space. Likewise, the remote sensing of land surface
temperature from satellite requires a detailed knowledge of
infrared land surface emissivity. Roughly speaking, a 2%
error in the knowledge of the land surface emissivity near
10 microns leads to an error in the derived surface temperature of about 1 K. Since the emissivity of bare soil can vary
across the infrared spectrum by 10% or more, errors in the
remote sensing of surface temperature from satellites can be
substantial. To address this issue for the ARM SGP domain,
investigations utilizing land type surveys, unique AERI
observations of the distinct surface types, and aircraft-based
sounding and imager data have been used to develop a
parameterization of the surface emissivity [Knuteson et al.,
2003]. In this parameterization, shown in Figure 8, the
surface emissivity representative of an AIRS footprint is
computed as the linear combination of pure bare soil
emissivity and pure vegetated surface emissivity, with the
weighting determined from vegetation fractions determined
from land type surveys. Improvement of this empirical
model for the SGP site, and the development of a similar
model for the NSA site, is the subject of ongoing research.
[19] In summary, the ARM site best estimate data set is a
collection of ‘‘microwave scaled’’ Vaisala RS-90 radiosonde
profiles, launched at the approximate Aqua overpass times,
interpolated to the exact overpass time using time continuous
ground-based profiles, adjusted to account for spatial gradients within the AMSU footprints, and supplemented with
surface temperature, surface emissivity, and some limited
cloud observations. A wide range of atmospheric, cloud, and
surface conditions (hot/cold, wet/dry, day/night, clear/cloudy,
ocean/land/ice) are sampled at the three ARM sites. Relying
on the developed ARM infrastructure and previous and
ongoing characterization studies of the ARM measurements,
the data set provides a good combination of statistics and
accuracy which is essential for assessment of the advanced
sounder temperature and water vapor retrievals.
3. Validation of AIRS Temperature and Water
Vapor Retrievals
[20] Various validation activities to assess the accuracy of
the AIRS radiances, forward model, cloud cleared radiances,
and retrievals have been conducted to date. Summaries of
many of these activities and additional references are
provided by Fetzer et al. [2003] and Fetzer [2006]. In
particular, the absolute accuracy of the AIRS spectral
radiances have been validated to 0.2 K in selected window
channels using sea surface temperatures [Aumann and
Gregorich, 2006] and for most channels with weighting
functions peaking below 50 mbar using aircraft-based
spectral radiance observations [Tobin et al., 2006], and the
AIRS clear sky radiative transfer model has been validated
to 0.2 K for the majority of spectral channels used for
tropospheric temperature and water vapor sounding [Strow
et al., 2006]. With validated radiance observations and a
well-characterized forward model, validation of the AIRS
cloud cleared radiance and retrieval products is being
pursued. The AIRS team has taken a tiered approach to
retrieval algorithm development and validation, starting
with clear sky conditions over nonpolar ocean, followed
by clear and cloudy conditions over nonpolar ocean, then
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nonpolar land conditions, and finally polar conditions.
Various validation data have been used to assess the
accuracy of the retrievals, including numerical forecast
model analysis fields, global radiosondes, dedicated radiosondes, lidar, and retrievals from high-altitude aircraft.
Using the ARM site best estimate profiles, validation of
AIRS Team version 4 temperature and water vapor retrievals for the tropical ocean TWP and midlatitude land SGP
conditions is presented here. Susskind et al. [2003] and
J. Susskind and M. Chahine (Accuracy of geophysical
parameters derived from AIRS/AMSU as a function of
fractional cloud cover, submitted to Journal of Geophysical
Research, 2005, hereinafter referred to as Susskind and
Chahine, submitted manuscript, 2005) provide detailed
descriptions of the AIRS Team retrieval algorithm and
products. Version 4 is the latest data processing package
developed by the AIRS science team and was delivered to
the NASA Goddard Distributed Active Archive Center in
April 2005 for reprocessing of past and future AIRS data.
Relevant to this study, it should be noted that elements of the
AIRS fast radiative transfer algorithm used in the version 4
retrievals have been adjusted on the basis of the TWP phase 1
best estimate data set, as described by Strow et al. [2006].
[21] The retrieval validation analysis presented here
includes only satellite overpasses for which the ARM site
best estimate products are successfully produced, the matching AIRS retrievals are available, and which involved two
radiosondes that were each launched within two hours of
the satellite overpass time and reached at least 200 mbar. All
retrievals (irrespective of their quality) within 120 km of the
ARM site have been considered for the three phases of
special ARM site radiosonde launches, resulting in up to
10 retrieval comparisons per overpass. The resulting data
set includes a total of 79 overpasses and 722 retrieval
comparisons for the TWP site and 151 overpasses and
1594 retrieval comparisons for the SGP site, with nearly
equal sampling of day and nighttime cases. To account for a
recent finding that routine processing of MWR data produces total column pwv values which are too wet by 3%
[Liljegren et al., 2005], all of the best estimate water vapor
amounts are reduced by 3% before the retrieval statistics are
generated. This bias is due to a processing issue and affects
the routine processing of ARM microwave radiometer data
from April 2002 through the present study. Additionally,
since full atmospheric profiles are required for top-ofatmosphere radiance calculations and analyses, the best
estimate profiles are supplemented with coincident analysis
fields from the European Centre for Medium-Range Weather
Forecasts (ECMWF). ECMWF temperature fields are
inserted above 60 mbar (or lower for radiosondes which
do not reach 60 mbar) and water vapor fields are inserted
above 200 mbar. Note that the 200 mbar cutoff is below the
tropopause height for TWP profiles while the tropopause
height can vary at SGP, as shown in Figure 6.
[22] The process of computing the AIRS retrieval performance statistics is consistent with the approach used by
Susskind et al. [2003] in assessing the retrieval performance
using simulated data. For each profile, the ARM best
estimate profile is converted to layer quantities (layer mean
temperatures and water vapor layer amounts in units of
molecules/cm2) consistent with the AIRS fixed 101 pressure
level grid, and the lowest valid layer values are adjusted to
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account for the fractional layer above the true surface. The
AIRS 100 ‘‘pseudo-level’’ retrieval profiles provided in the
Level 2 ‘‘support’’ product files are also converted to layer
values (for temperature) and the bottom fractional layer
values above the surface are similarly adjusted. For the
temperature profile comparisons, the ARM and AIRS
profiles are degraded to 1 km vertical layer values, and
mean differences (AIRS minus ARM) and RMS differences
are then computed for each layer. For water vapor comparisons, the profiles are degraded to 2 km vertical layer
values, and mean percent differences (100 (AIRS-ARM)/
ARM) and RMS percent differences are computed for each
layer.
[23] For water vapor, it should be noted that the mean
percent differences (i.e., biases) and RMS percent differences are computed using the convention used in reporting
AIRS retrieval statistics [e.g., Susskind et al., 2003], where
water vapor layer amounts are used to weight the observed
percent differences. The weighting is done independently
for each 2-km thick layer. For ensembles with higher
water vapor variability, this process has the effect of downweighting percent errors for cases with lower water vapor
amounts. The weights are normalized for the ensemble, and
the weighting therefore has lower impact on the results for
ensembles with lower water vapor variability (e.g., in the
tropics and in the upper troposphere).
[24] As mentioned in section 2, the process of degrading
the profiles to lower vertical resolution (e.g., 1 km for
temperature) before computing the mean and RMS differences has the dramatic effect of suppressing large errors
associated with fine-scale vertical structure in the ARM
profiles. The satellite soundings have limited vertical resolution and cannot resolve this fine structure. The comparisons presented here therefore suppress most of this ‘‘nullspace’’ error [Rodgers, 1990] associated with the retrievals.
[25] Temperature and water vapor profile validation
statistics are shown for four ensembles of profiles, corresponding to four different selections of retrievals according to the version 4 retrieval quality flags. The reader is
referred to Susskind and Chahine (submitted manuscript,
2005) for a detailed description of the quality flags and how
their values are determined. Quality flags are provided for
various retrieved quantities including the surface fields
(Qual_Surf), water vapor profile (Qual_H2O), and temperature profile for three vertical regions (Qual_Temp_Profile_
Top, above 200 mbar; Qual_Temp_Profile_Mid, 3 km to
200 mbar; Qual_Temp_Profile_Bot, below 3 km). Each flag
can have values of 0, 1, or 2 corresponding to retrievals of
highest, good, and poor (i.e., not accepted) quality, respectively. Sea surface temperatures are used in evaluating
Qual_Surf and this flag is applicable only to nonfrozen
ocean profiles (and is therefore not used for the SGP site
selections). The criteria for the four selections of quality
flags are given in Table 2. A range of selections is included
to demonstrate how the accuracy and yield of the retrievals
vary as a function of the assigned quality flags. The first
selection (denoted QC1) includes retrievals for which all of
the products (including the surface fields, water vapor
profile, and temperature profile at all levels) of a given
profile are flagged as highest quality. The last selection
(QC4) consists of retrievals for which the selected products
(temperature at various levels, water vapor) are accepted.
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Table 2. Retrieval Quality Flag Selections
Ensemble
Quality Flag Criteria
QC1
Qual_Surf = 0a AND Qual_H2O = 0 AND Qual_Temp_Profile_Top = 0 AND
Qual_Temp_Profile_Mid = 0 AND Qual_Temp_Profile_Bot = 0
Qual_Surf 6¼ 2a AND Qual_H2O 6¼ 2 AND Qual_Temp_Profile_Top 6¼ 2 AND
Qual_Temp_Profile_Mid 6¼ 2 AND Qual_Temp_Profile_Bot 6¼ 2
Qual_Temp_Profile_Top = 0 AND Qual_H2O = 0, above 200 mbar
Qual_Temp_Profile_Mid = 0 AND Qual_H2O = 0, 3 km to 200 mbar
Qual_Temp_Profile_Bot = 0 AND Qual_H2O = 0, below 3 km
Qual_Temp_Profile_Top 6¼ 2, temperature above 200 mbar
Qual_Temp_Profile_Mid 6¼ 2, temperature 3 km to 200 mbar
Qual_Temp_Profile_Bot 6¼ 2, temperature below 3 km
Qual_H2O 6¼ 2, water vapor
QC2
QC3
QC4
a
Qual_Surf criteria is used for the TWP (ocean) site but not the SGP (land) site.
That is, for example, if Qual_H2O = 0 or 1 then the water
vapor profile is selected irrespective of the temperature and
surface field quality flags. The other two somewhat arbitrary selections represent data sets of intermediate quality.
The QC2 ensemble are retrievals for which all of the
products for a given profile are accepted (all quality
flags = 0 or 1) and QC3 are retrievals for which both the
water vapor and temperature profiles at a given level are of
highest quality. Selections QC1, QC2, and QC4 (for water
vapor) have yields (percent of selected cases out of all
cases) that are independent of altitude while selections QC3
and QC4 (for temperature) have yields that are altitudedependent.
[26] Assessment of the AIRS temperature and water
vapor retrievals for the TWP site is shown in Figure 9.
The highest-quality retrievals (QC1) demonstrate very
good performance for both temperature and water vapor.
The yield of these retrievals is 10%, and inspection of the
coincident AIRS spectra and retrieved cloud fractions
show that these are primarily clear sky cases. The 1 km
layer temperature biases are less than 1 K at all levels,
with small but clearly defined oscillating biases as a
function of altitude (AIRS warmer than ARM at 500
and 150 mbar, AIRS colder than ARM at 350 mbar,).
The temperature RMS errors are 0.6 K or less for nearly
all layers below 200 mbar. The 2 km layer water vapor
biases are less than 5% below 400 mbar and approximately
minus 10% (AIRS drier than ARM) from 300 to 100 mbar.
Considering the estimated accuracy of the best estimate
upper level water vapor amounts (10%) the significance
of the bias reported for the upper troposphere is questionable. Application of the suggested radiosonde corrections
[Miloshevich et al., 2006] would produce a negligible
impact on these comparisons below 200 mbar but would
moisten the TWP profiles by 15% above 200 mbar.
Note that above 200 mbar the water vapor comparisons are
with ECMWF, because of the radiosonde cutoff height.
The water vapor RMS errors are very good, with values of
10% below 400 mbar and increasing to 25% at 150
mbar. As discussed in section 2, the reported retrieval
statistics should be interpreted as upper bounds of the
AIRS retrieval performance since the ARM validation
profiles contain uncertainties that also contribute to the
computed statistics. The observed RMS performance at
TWP, however, is in fact very similar to the retrieval
performance predicted for global clear sky cases using
simulated AIRS data. See, for example, Susskind et al.
[2003, Figures 5 and 7]. This confirms that the AIRS
retrieval algorithm is behaving as expected for clear sky
tropical atmospheres over a well-known (ocean) surface. In
terms of the best estimate profile accuracy, this result is
also significant because the simulation studies include no
errors in the ‘‘truth’’ profiles or in the radiative transfer
algorithms, which suggests that the uncertainty in the
ARM profiles do not contribute significantly to the
reported RMS differences. For the other quality flag
selections, the temperature and water vapor mean biases
do not change significantly with respect to the QC1
results. Going from QC1 to QC4, the yields increase and
the temperature and water vapor RMS errors degrade
gradually, as shown in Figure 9. Considering all accepted
temperature and water vapor products (i.e., QC4), the
temperature yield is 55% (89%) below (above) 200 mbar
and the water vapor yield is 88%. For these retrievals, the
temperature RMS errors are 1 K or less below 200 mbar
and the water vapor RMS errors are 20% or less below
400 mbar. Retrieval statistics for the QC1 and QC4
ensembles are listed in Tables 3 and 4.
[27] As mentioned previously, analyses of clear sky
observed and calculated AIRS radiances suggest that the
ARM best estimate water vapor profiles possess a heightdependent diurnal bias, with daytime profiles 5 to 8% drier
in the upper troposphere. The bias is suspected to be due
to effects of solar radiation on the Vaisala radiosonde
water vapor sensor. To assess the impact of the bias on
the AIRS retrieval profile validation, the TWP site profile
statistics were generated separately for the day and night
cases using the highest-quality (QC1) retrievals. With this
approach, the AIRS retrievals are assumed to not have a
diurnal bias and in this sense are used as a relative
reference to assess the ARM profiles. As shown in
Figure 10, the presence of coherent differences from day to
night are not obvious in the retrieval comparisons. Taken
literally, the comparisons in the 200 to 300 mbar region
suggest that the daytime profiles have higher variability
(5% RMS) and that the daytime ARM profiles are wetter
than the nighttime profiles by 5 to 10%. Further investigation
is required in order to resolve this finding with respect to the
observed minus calculated spectral analyses.
[28] Analogous to Figure 9 for the TWP site, assessment of the SGP site retrievals is shown in Figure 11. As
opposed to the TWP site results, the SGP site retrieval
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Figure 9. Validation of AIRS version 4 atmospheric profile retrievals using ARM TWP site profiles.
(top left) The 1 km layer temperature differences (AIRS-ARM), (top right) temperature retrieval yield,
(bottom left) percent difference in 2 km layer water vapor amounts (100(AIRS-ARM)/ARM), and
(bottom right) water vapor retrieval yield. Each panel includes the bias (dashed curves) and RMSE (solid
curves) for four selections of quality control flags (QC1, black; QC2, green; QC3, purple; QC4, blue) as
discussed in the text. Note that when computing the mean and RMS water vapor profiles, the percent
differences are weighted by the ARM water vapor amounts, as discussed in the text.
statistics do not vary significantly between the four
different quality flag selections. The 1 km layer temperature biases are 1 K or less and display vertical oscillations similar in character and magnitude to the TWP
temperature biases above 700 mbar. Temperature RMS
errors are 2 K near the surface, decrease to 1 K from 500
to 400 mbar, and increase to 2 K at 200 mbar. Divakarla
et al. [2006] report similar temperature biases as a
function of altitude and also discuss potential retrieval
issues responsible for the biases. The 2 km layer water
vapor biases are 5% or less below 400 mbar and increase
to approximately minus 10% at 200 mbar, similar to the
TWP result. The water vapor RMS errors are larger than
those for TWP, with values of 25% below 500 mbar and
increasing to 35% at 200 mbar. There are no significant
changes in the results shown in Figure 11 if the time
window (radiosonde launch minus overpass time) is
reduced from 2 hours to 1 hour and the spatial window
(distance from center of AMSU footprint to the ARM
site) is reduced from 120 km to 50 km. As discussed
previously, AIRS retrievals at the SGP site are more
difficult (compared to the TWP site) because of several
issues, including the need to account for land surface
emissivity effects, much larger atmospheric state variability, and the interpretation of microwave observations of
land surfaces for infrared cloud clearing.
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Table 3. Summary of 1 km Layer Temperature Differences (AIRS-ARM) for the QC1 and QC4 Quality Flag Selectionsa
Pressure
Range, mb
Mean
Altitude,b
km
99.5 – 121.7
121.7 – 138.1
138.1 – 155.8
155.8 – 185.1
185.1 – 217.7
217.7 – 266.4
266.4 – 307.0
307.0 – 336.1
336.1 – 399.1
399.1 – 468.8
468.8 – 525.4
525.4 – 606.8
606.8 – 672.4
672.4 – 765.6
765.6 – 865.6
865.6 – 995.9
16.1
15.2
14.4
13.5
12.5
11.3
10.1
9.2
8.3
7.0
5.9
4.9
3.9
2.9
1.8
0.7
TWP Site
QC1
SGP Site
QC4
QC1
QC4
Yield, % Bias, K RMS, K Yield, % Bias, K RMS, K Yield, % Bias, K RMS, K Yield, % Bias, K RMS, K
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
0.8
0.3
0.1
0.1
0.1
0.1
0.3
0.5
0.7
0.0
0.4
0.0
0.1
0.3
0.0
0.1
1.7
1.5
1.2
0.6
0.7
0.6
0.6
0.9
0.9
0.5
0.7
0.5
0.6
0.7
0.5
0.6
89
89
89
89
55
55
55
55
55
55
55
55
55
55
55
55
0.3
0.4
0.7
0.3
0.1
0.3
0.5
0.5
0.3
0.2
0.7
0.4
0.2
0.4
0.0
0.5
1.7
2.0
1.9
1.3
0.9
1.1
1.0
1.0
0.9
0.8
1.1
0.9
0.9
0.8
0.8
1.2
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
21
0.6
0.3
0.4
1.1
0.8
0.3
0.7
0.6
0.4
0.1
0.1
0.5
0.4
0.1
0.5
0.5
1.8
1.8
1.9
2.1
1.7
1.5
1.5
1.1
1.0
1.0
1.0
1.0
1.0
1.1
1.4
2.0
89
89
89
89
71
71
71
71
71
71
71
71
71
71
71
71
0.5
0.3
0.3
0.8
0.6
0.2
0.6
0.6
0.3
0.1
0.0
0.3
0.3
0.0
0.3
0.5
1.8
1.9
1.9
2.1
1.7
1.6
1.4
1.2
1.0
1.0
1.0
1.1
1.1
1.2
1.4
2.1
a
Data are plotted in Figures 9 and 11.
Approximate.
b
[29] The SGP ensemble includes a wide range of hot/
wet and cold/dry profiles, and it should be noted that the
water vapor weighting technique used in computing the
water vapor statistics has a large effect on the SGP
statistics reported here. Computing the statistics without
the water vapor weighting, the mean bias and RMS
percent errors are approximately 20 and 55%, respectively,
in the lower troposphere (as opposed to values of 5 and
25%, shown in Figure 11). This is due to fractional water
vapor errors that have a strong dependence on the total
column pwv, as shown in Figure 12. For low-pwv cases
encountered at the SGP site, the fractional pwv retrieval
errors have larger variability and larger mean values, with
the mean pwv error approaching 25% (AIRS wetter than
ARM) at 1 cm pwv. The ARM best estimate pwv values
are determined by the MWR retrievals, which have an
estimated absolute accuracy of 3% for all but very dry
(<0.2 cm pwv) conditions. For the TWP site, the water
vapor variability is much less and the water vapor
weighting therefore has little impact on the statistics
computed for the TWP ensemble. Similarly, water vapor
variability in the upper troposphere is relatively low at
both SGP and TWP sites, and the water vapor weighting
has little impact on the statistics computed for the upper
troposphere.
[30] Last, the AIRS retrievals are examined as a function
of cloudiness. The 1 km temperature and 2 km water vapor
statistics are computed for the accepted (i.e., QC4) products
for a range of cloud fraction bins and displayed as a
function of the AIRS retrieved cloud fraction in Figures 13
and 14 for the TWP and SGP sites, respectively. For both
sites, the small-scale vertical oscillations in the temperature
biases are largely independent of cloud fraction but with the
positive bias at 500 mbar increasing slightly with cloud
fraction, particularly for the TWP site. The upper level
water vapor bias increases slightly with cloud fraction at the
TWP site. For both the SGP and TWP sites, the temperature
and water vapor RMS differences increase slightly with
increasing cloud fraction.
4. Conclusions and Summary
[31] Validation of products from the new generation of
advanced satellite sounders requires correlative data sets
Table 4. Summary of 2 km Layer Water Vapor Percent Differences (100(AIRS-ARM)/ARM) for the QC1 and QC4 Quality Flag
Selectionsa
Pressure
Range, mb
Mean
Altitude,b
km
99.5 – 138.1
138.1 – 185.1
185.1 – 266.4
266.4 – 336.1
336.1 – 468.8
468.8 – 606.8
606.8 – 765.6
765.6 – 995.9
15.7
13.9
11.8
9.7
7.6
5.3
3.3
1.2
TWP Site
QC1
SGP Site
QC4
QC1
QC4
Yield, % Bias, % RMS, % Yield, % Bias, % RMS, % Yield, % Bias, % RMS, % Yield, % Bias, % RMS, %
10
10
10
10
10
10
10
10
9.0
14.2
3.4
12.0
1.8
5.8
3.1
5.4
24.7
26.1
20.1
19.6
9.2
12.7
8.9
9.5
88
88
88
88
88
88
88
88
4.5
14.3
15.9
5.4
1.8
4.9
0.3
3.4
a
Data are plotted in Figures 9 and 11.
Approximate.
b
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30.9
34.6
31.3
27.3
18.5
20.2
13.9
8.2
21
21
21
21
21
21
21
21
1.4
5.2
13.5
10.4
1.3
3.8
1.9
1.8
28.6
29.6
34.0
29.4
28.7
23.3
22.2
25.8
87
87
87
87
87
87
87
87
4.8
0.7
11.1
5.2
0.4
1.7
4.4
2.6
33.1
36.5
34.3
33.4
29.1
25.9
26.0
25.9
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TOBIN ET AL.: AIRS RETRIEVAL VALIDATION
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Figure 10. Bias (dashed) and RMSE (solid) differences for daytime (shaded) and nighttime (solid) fully
accepted retrievals (QC1) at TWP. (left) The 1 km layer temperature differences, (middle) percent
difference in 2 km layer water vapor amounts, and (right) yield.
which are accurate, well characterized and statistically
significant. Using various ARM data, including radiosondes
launched at the Aqua overpass times, ensembles of ‘‘best
estimate’’ temperature and water vapor profiles for Aqua
overpasses of the ARM sites have been constructed, as
described in section 2. This data set has been used to assess
the accuracy of the AIRS radiative transfer algorithm and, in
section 3 of this paper, assess the accuracy of the AIRS
Team version 4 temperature and water vapor retrievals at the
TWP and SGP ARM sites. Specific findings of the retrieval
validation study include:
[32] 1. Short-term and small-scale variability of the
atmosphere is significant and should be taken into account
when validating large area footprint retrievals from polar
orbiting satellites. This is particularly important for assessing RMS errors of the retrievals, opposed to the mean
differences (biases) for which the random variability cancels
for a large ensemble of cases. RMS errors computed using
validation profiles that do not fully account for the variability should be interpreted as upper bounds of the true
errors.
[33] 2. The conventional approach used to report AIRS
water vapor retrieval statistics (and used in this paper) of
weighting the observed percent errors by water vapor
concentration can produce significant differences from the
traditional, unweighted, calculations. This is particularly
true for ensembles with high water vapor variability, such
as the SGP site lower troposphere.
[34] 3. The yields of AIRS retrievals with temperature,
water vapor and surface products flagged with highest
quality (i.e., the QC1 ensembles) are 10 and 21% for the
TWP and SGP sites, respectively. Considering all accepted products (i.e., the QC4 ensemble) at the TWP site, the
middle and bottom level (below 200 mbar) temperature
yield is 55%, the top level (above 200 mbar) temperature
yield is 89%, and the water vapor yield is 88%. Analogous yields for the SGP site are 71, 89, and 87%,
respectively.
[35] 4. AIRS retrievals for the tropical ocean TWP site
have very good performance, with RMS errors approaching
the theoretical limit predicted by retrieval simulation studies
performed with no errors in the truth data or radiative
transfer algorithms. Retrievals for which the temperature,
water vapor, and surface products are flagged as highest
quality (QC1) have the best performance, and the performance degrades gradually as retrievals flagged with lower
quality (and more cloudy scenes) are included. For all
accepted temperature and water vapor products (QC4),
1 km layer temperature RMS errors are 1 K or less below
200 mbar and 2 km layer water vapor RMS errors are 20%
or less below 400 mbar.
[36] 5. AIRS retrievals for the midlatitude land SGP site
have poorer RMS performance with respect to the TWP site
results for both temperature and water vapor. 1 km layer
temperature RMS errors range from 1 to 2 K and 2 km layer
water vapor RMS errors range from 25 to 35%. This
performance is largely independent of the retrieval quality
flags, yield, and cloud fraction.
[37] 6. AIRS total column precipitable water vapor (pwv)
fractional errors are higher for lower-pwv conditions encountered at the SGP site, with mean fractional errors of
25% (AIRS wetter than ARM) at 1 cm pwv. These larger
percent errors observed for lower water vapor amounts are
suppressed when the water vapor weighting approach is
used to compute the AIRS water vapor mean and RMS
differences.
[38] 7. For both the TWP and SGP ensembles, small-scale
(0.5 K) vertical oscillations are present in the AIRS
temperature retrievals (AIRS retrievals are too warm at
600 mbar, too cold at 300 mbar, too warm at
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Figure 11. Same as Figure 9 but for the SGP site.
150 mbar). The magnitude of the oscillations are largely
independent of retrieved cloud fraction.
[39] 8. For both the TWP and SGP sites, water vapor
biases are 5% or less below 400 mbar and increase to
minus 10% (AIRS drier than ARM) at 200 mbar. The
biases are largely independent of retrieved cloud fraction.
The significance of the reported upper troposphere bias is
questionable given the estimated absolute accuracy of the
ARM profiles (10%) at these levels.
[40] 9. Evidence of diurnal biases in the upper level water
profiles is not evident in the ARM/AIRS comparisons
shown here.
[41] The AIRS Team version 4 retrieval results reported
here for the ARM TWP site demonstrate the potential of
the new generation of advanced high – spectral resolution
satellite sounders. The TWP site retrievals meet or
surpass the 1 K/1-km and 20%/2-km RMS performance
requirements throughout the middle and lower troposphere for clear and partly cloudy conditions. The high
accuracy is due in part, however, to the well-known
(ocean) surface and to the tropical profiles which have
little vertical structure and low spatial and temporal
variability. The results for the SGP midlatitude land site
are more indicative of the AIRS version 4 sounding
accuracy under more typical meteorological condition.
For the SGP site, the 1-km temperature and 2-km water
vapor RMS performance requirements are not met, motivating further improvement of the AIRS science team
retrieval algorithm. Retrievals at the SGP site are difficult
because of several potential issues, including the need to
account for land surface emissivity effects, much larger
atmospheric state variability, and the interpretation of
microwave observations of land surfaces for infrared
cloud clearing. The AIRS Team is currently considering
several different algorithm changes to address these
issues. An improved method to retrieve and account for
the spectral surface emissivity, for example, is currently
being evaluated. Improved temperature and water vapor
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Figure 12. Total column precipitable water vapor (pwv) ratios (pwvAIRS/pwvARM) as a function of
pwvARM for the SGP (shaded) and TWP (solid) site profiles for QC4 selected profiles (all cases with
Qual_H2O = 0 or 1). Note that pwvARM is determined by the MWR observations.
retrieval performance over land is expected to be provided in a
subsequent AIRS Team algorithm release.
[42] The ARM site best estimate products described in
this paper will continue to be used to provide systematic and
objective evaluations of future AIRS Team algorithm
changes. Further applications include assessment of retrievals performed using combined AIRS and MODIS observa-
tions [Li et al., 2005], assessment of the AIRS Team version
4 retrievals for the polar NSA site, and assessments of
NOAA 16 ATOVS and Aqua MODIS temperature and
water vapor retrievals. Case studies and statistical correlations of the retrieval performance versus retrieval and
validation parameters are also being used to diagnose
specific issues within the retrieval algorithms. Further
Figure 13. TWP site retrieval bias and RMS as a function of AIRS retrieved cloud fraction, using QC4
selected profiles: (a) temperature mean difference, (b) temperature RMS differences, (c) water vapor
mean percent difference, and (d) water vapor RMS percent difference.
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Figure 14. Same as Figure 13 but for the SGP site.
development of the ARM best estimates products will also
be pursued; specific activities include continued studies to
characterize the accuracy of the ARM upper level water
vapor measurements, improved representation of the SGP
and NSA site surface emissivities, and the use of Aqua
MODIS products to account for spatial gradients within the
AMSU footprints. The data set will also be extended to
include additional radiosonde launch campaigns.
[43] Acknowledgments. This research was supported by the EOS
Science Project Office under NASA grant NNG04GG22G. We gratefully
acknowledge numerous ARM infrastructure personnel, David Starr of the
EOS Project, and the AIRS project at JPL for facilitating this work. Thanks
to Chris Barnet, Bill Smith, and Dave Turner for discussions regarding
various aspects of the analysis and comments provided by three anonymous
reviewers. ARM data were obtained from the Atmospheric Radiation
Measurement (ARM) Program sponsored by the U.S. Department of
Energy, Office of Science, Office of Biological and Environmental Research, Climate Change Research Division.
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