Introduction
Because different thermal wavelengths are sensitive to different atmospheric
components, remotely sensed hyperspectral and narrowband radiance
measurements contain valuable information about atmospheric, surface and
cloud properties, and also reveal fingerprints of long-term climate trends
. Additionally, they have a unique value in
evaluating climate models . As such there is a need
for detailed and complete satellite observations of terrestrial outgoing long-wave radiation (OLR) in the 25–3000 cm-1
wavenumber range
(3–400 µm wavelength) at the spectral level
. At the present time, however, there is no
satellite instrument in operation that isolates a substantial part of the OLR
with the longest wavelengths, known as the far infrared (FIR).
The FIR, which we define as those wavenumbers between 25 and 650 cm-1
(15–400 µm), is modulated by water vapour absorption in the pure
rotation band and, to a lesser extent, the water vapour continuum. For the
all-sky, estimated that about 45 % of the total OLR
from the Earth is from the FIR. Although individual transitions in this
region are low in energy, because rotational transitions are lower in
characteristic frequency than vibrational transitions, the combined intensity
of outgoing radiance at these wavelengths is large. The abundance of water
vapour in the troposphere strongly absorbs most of the FIR that originates
from the surface meaning that, apart from over some very dry and cold
regions, the majority that reaches the top-of-atmosphere (TOA)
is emitted from the upper troposphere. Satellite measurements in this wavelength range would be a rich
source of information about upper-tropospheric water vapour
, its continuum absorption
, the radiative influence of cirrus clouds
as well
as providing unique information for climate model validation.
Current operational space-borne hyperspectral sounders such as the
Atmospheric Infrared Sounder (AIRS) or the Infrared
Atmospheric Sounding Interfermeter (IASI) have been
designed to measure only the mid-infrared part of the OLR. Photons at FIR
frequencies have lower energies than typical band gap energies, so suitable
photodiodes are difficult to make. Mercury–cadmium–telluride (Hg–Cd–Te)
detectors such as those used within the IASI instrument can be designed for
lower frequencies; however, a 650 cm-1 cut off is common due to the
enhanced sensitivity required to measure below this threshold. In order to
maintain the high signal-to-noise ratio the detector needs to be cooled
significantly to reduce the number of photons generated by the detector
itself and achieve the precision required. Microwave satellite detectors such
as the microwave limb sounder (MLS) or the advanced microwave sounding unit
(AMSU) sense wavelengths that are just longer than the FIR. However, they use
very different radiance measurement technologies. Both of these restrictions
from either side of the FIR result in an unmeasured segment of
electromagnetic radiation that has generally only been observed as part of
the total infrared radiation by broadband devices.
Currently the only spaceborne instrument to spectrally resolve part of the
FIR has been the Infrared Interferometer Spectrometer (IRIS) which flew
onboard the Nimbus 3 and Nimbus 4 satellites in 1969 and 1970, respectively
. It had a maximum wavenumber of 400 cm-1
(25 µm) and a spectral resolution of 2.8 cm-1. Since then, a
limited number of instruments have been developed to measure part of, or all
of, the FIR. Some have been part of balloon-borne and ground-based campaigns,
such as the Atmospheric Emitted Radiance Interferometer (AERI)
and the Radiation Explorer in the Far InfraRed – BreadBoard/Prototype for Applications and Development
(REFIR-BB/PAD)
, whose measurements have been used
to test the representation of the FIR by line-by-line radiative transfer
models (LBLRTM) . Aircraft campaigns using instruments such
as the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS)
, REFIR-PAD and the
Interferometer for Basic Observation of Emitted Spectral Radiance of the
Troposphere (I-BEST) , have been used to gain
insights into the FIR continuum. Though these airborne experiments do prove
useful for testing parametrisations in radiative transfer models, only
spaceborne instruments can give the full Earth coverage of sufficient
temporal length needed for climate studies.
Recently, much work has been put into developing and testing a detector
proposed for a spaceborne mission with a response in the 50–2000 cm-1
range at high spectral resolution (approximately 0.643 cm-1). The
Far-Infrared Spectroscopy of the Troposphere (FIRST) instrument
has detectors that are cooled to 4.2 K with liquid
helium to achieve the necessary sensitivity (for comparison the optical core
of IASI is 91.3 K). Initial comparisons of FIRST measurements taken on
balloon flights against theoretical calculations and spectral overlaps with
coincident satellite instruments show excellent fidelity
. However, despite high-priority recommendations
(see ) there is currently no scheduled launch date
for its deployment, even though it is often noted that the FIR has been
measured extensively and directly on every planet in the solar system except
Earth .
Historically, when parts of the infrared spectrum are unmeasured from space
the remaining bands have often estimated through alternate means. Previous
studies have sought to reproduce total OLR from narrowband and hyperspectral
sounders with the combined motivations of validating current operational
broadband sounders, mitigating them against potential failure and gaining
wider diurnal coverage. The absence of an instrument that measured total
outgoing long-wave flux in the 1970s led to its estimation using a single waveband
in the 800–950 cm-1 window region (10.5–12.5 µm) from the
two-channel scanning radiometer onboard the NOAA-1 to NOAA-5 (National Oceanic and Atmospheric Administration) satellite
platforms via a non-linear regression model derived from radiative transfer
calculations applied to 99 different atmospheric profiles
. As the reference broadband
results are obtained from a radiative transfer code this method is termed
“theoretical”. Alternatively, used the Earth radiation budget (ERB) broadband OLR measurements on the Nimbus 7 satellite
as a reference to obtain regression coefficients between these, and window
band observations from the Temperature–Humidity Infrared Radiometer (THIR)
instrument on the same satellite at collocated footprints. This method is
termed “empirical”, because actual measured data are used as a reference.
There are uncertainties involved in using only one narrow band to estimate
the entire OLR because the atmospheric information contained in one spectral
region is limited, e.g. see . An early theoretical
OLR product derived with a multi-spectral regression technique used the four
infrared channels from the Medium-Resolution Infrared Radiometer (MRIR) on
the Nimbus-3 satellite . This method has been
adapted for use with the High-Resolution Infrared Sounder (HIRS) instruments
that have been operational since 1978, thus providing a continuous long-term
surrogate for total OLR . The product has been
continuously developed since its creation and has demonstrated extremely high
correlations with Clouds and the
Earth's Radiant Energy System (CERES) broadband data . Recently,
have used the empirical approach to derive
broadband data from AIRS using the CERES outgoing long-wave flux to generate
regression coefficients from principal component analysis of AIRS radiance.
Traditionally, these methods employ data from instruments that fly on polar
orbiting satellites which are beneficial for global climate studies in terms
of their high spatial coverage. However, as they are restricted to monitoring
each subsatellite point just twice a day they fall short of the requirements
for diurnal analyses. Geostationary satellites, on the other hand, complete a
full Earth scan in approximately 30 min thus capturing the daily
variability, but are restricted to one nadir location with views at
increasingly unfavourable angles away from the subsatellite point.
was the first to use geostationary radiance from
the two infrared channels (10.2–13 and 5.7–7.5 µm) on the
METEOSAT-1 satellite to estimate total OLR flux theoretically.
modified this approach using METEOSAT-2 data to
include a better treatment of limb-darkening using the method developed by
, and calculated the
relationship between METEOSAT-2 data and collocated footprints from broadband
Earth Radiation Budget Experiment (ERBE) measurements to produce empirical
regression coefficients. The Geostationary Operational Environment
Satellite 6 (GOES-6) Imager window channel (10.2–12.2 µm) has also
been employed in an empirical estimation of OLR fluxes using ERBE data
, and blended HIRS OLR
fluxes from polar satellites with GOES-8 Imager data to provide OLR data to
incorporate multi-spectral information on temperature and humidity at
different elevations, with wider diurnal coverage.
The body of work that exists surrounding the derivation of broadband OLR from
narrowband mid-infrared measurements is extensive and ongoing; however, with
regard to spectrally resolved measurements in the FIR, progress is limited to a
handful of studies. used clear-sky radiance
from the IRIS instrument to predict fluxes in its uncovered spectral regions
below 400 cm-1 and above 1400 cm-1 by assuming a linear
relationship between these regions and fluxes in the H2O ν2 band
and a narrow window region. Regression coefficients between measured and
unmeasured wavebands are obtained from calculated radiance using the MODerate resolution atmospheric TRANsmission
(MODTRAN)
radiative transfer model applied to simulated profiles from the Geophysical Fluid Dynamics Laboratory Atmospheric Model 2 (GFDL
AM2)
global climate model. These coefficients are then applied to IRIS to simulate
the whole OLR. adapted this theoretical method for
the hyperspectral AIRS to derive spectral
fluxes in its uncovered wavebands using principle component analysis. A
complete set of clear-sky fluxes from 10 to 2000 cm-1 are calculated at
10 cm-1 intervals, and validated with broadband observations using
collocated CERES data for the tropical oceans. Corresponding studies were
carried out for cloudy data , and additional years
. extend this work to
include land and extra-tropical ocean regions using clear-sky data and
built upon this by using all-sky data and calculating
spectral cloud radiative effects (CREs).
In lieu of complete FIR observations, we follow a theoretical approach and
develop an algorithm to “fill in the gaps” of the available data, but with
a spectral resolution and range of estimated wavenumbers that is an advance
on previous studies. To do this we use the IASI instrument which measures in
the mid-infrared, originally designed to fulfil both meteorology requirements
of high spatial coverage, and atmospheric chemistry needs such as accuracy
and detailed vertical resolution . IASI has
4 times as many channels as the AIRS instrument for the same range of thermal
infrared wavelengths, and is free from gaps over the whole spectral range. It
is part of the payload of the MetOp-A (Meteorological Operation) satellite, which provides a differently
timed polar orbit and hence a different sampling of the diurnal cycle to
existing satellites that carry broadband instruments. In the absence of any
current spaceborne instrument that isolate the FIR, our new algorithm has the
potential to provide valuable proxy measurements, within the limitations of
the spectroscopy implemented in the radiative transfer code used to construct
the prediction model. To ensure high accuracy, we use the
LBLRTM available publicly at http://rtweb.aer.com,
which has a long and successful heritage of being at the leading edge of the
field, is continually updated and has been well validated; see for example
, and .
This approach extends existing observations into the far and near infrared
using physical knowledge of atmospheric radiative transfer provided by
LBLRTM, which we then evaluate using broadband observations from the CERES
satellite instrument.
This study differs from most of its associated predecessors by remaining in
the directional radiance regime, with no attempt made to translate unfiltered
IASI radiance or the total integrated OLR product to flux at this
stage. Flux is calculated by integrating the measured radiance over all solid
angles, which can be split into zenith and azimuth angles. The outgoing
radiation field is strongly anisotropic and must be estimated using a
predetermined model, of which many exist involving varying degrees of
sophistication and assumptions. These can be either theoretically determined
using radiative transfer model calculations of flux or empirically derived
using satellite measurements over several different viewing angles and
locations; see for example ,
and . The resulting angular distribution models (ADMs) relate the radiance measured at a single angle to
irradiance estimated over all angles, and as such introduce a further level
of uncertainty into the validation, which can be up to 2.3 % for recent
satellite products (Instantaneous long-wave TOA flux; see the CERES Terra Edition3A
single-scanner footprint (SSF) Data Quality Summary). To avoid confusion we use the abbreviation INLR
(Integrated Nadir Long-wave Radiance) to refer to the extended spectrum of
IASI radiance that has been integrated over all wavenumbers, and is distinct
from OLR which is synonymous with the integrated fluxes. This approach has
the advantage of allowing for a cleaner comparison with climate model
simulated satellite products, such as those calculated by the Radiative
Transfer for TOVS – RTTOV , which can simulate IASI
radiances from vertical profiles of climate variables. The adaption of the
methodologies adopted in this study for the additional calculation and
evaluation of flux quantities are left for future studies.
We use a theoretical-based regression technique similar to the one used to
derive OLR from the HIRS instrument based on physical atmospheric profiles
which is described in Sect. . In order to verify the
extended IASI spectrum, we compare the calculated INLR with broadband CERES
instruments on other satellites. Section explains how times and
locations are identified where the path of MetOp-A crosses those of the Aqua
and Terra satellites, both of which carry CERES instruments. By restricting
this set further to only nadir-looking views the instruments will sense the
same atmospheric path at the same time, providing the opportunity for
indirect validation of the new IASI product. Results of this are presented in
Sect. . Finally, the complete constructed IASI spectrum is
presented in the remaining Sects. ,
and .
Data and methodology
IASI level 1c and combined sounding<?xmltex \hack{\break}?> products data set
The IASI Flight Model 2 (FM2) instrument on the sun-synchronous MetOp-A
satellite was launched by EUMETSAT in October 2006. It is a 8461 channel
passive sounder that measures in the mid-infrared spectral region between
645 and 2760 cm-1 (3.62–15.5 µm) at a 0.25 cm-1 sampling
interval with no gaps. The apodised level 1c radiances have a 0.5 cm-1
resolution. The effective field of view (EFOV) is a 2 × 2 matrix of
four circular instantaneous fields of view (IFOV) that each have an approximate
footprint diameter of 12 km at nadir. There are 30 EFOV per scan line which
takes 8 s to complete and with a maximum scan angle of 48.3∘ in the
across track direction. In its nominal mode IASI uses a view of an internal
blackbody and a deep space view once every scanline to calibrate on board, as
described by . It has been demonstrated to have a
very low absolute brightness temperature radiometric accuracy of 0.25 K in
pre-flight testing, and post-launch assessments place the absolute
calibration uncertainty at or better than 0.5 K . For discussion of the spectral structure of
the noise see .
We restrict the data to the IFOV with the smallest satellite zenith angles
in order to retain only nadir-looking pixels. There are four IFOV with angles
less than 1.5∘ which are indices 57, 58, 63 and 64 in the across
track direction which have viewing angles of 1.34∘, 1.37∘,
1.41∘, and 1.39∘, respectively. Alongside the level 1c
radiance clear-sky flags are obtained from the related level 2 combined
sounding products to construct an equivalent clear-sky product which is used
to show clear/cloudy differences in the mean spectrum in
Sect. . Cloud detection in IASI pixels is performed from
a choice of five separate tests, involving window channels, AMSU-A, advanced very high resolution radiometer (AVHRR) and
CO2 slicing, depending on the quality of the input data.
Method for estimating radiances at unmeasured wavenumbers from IASI
Strong correlations are found between frequencies in the long-wave spectra with
similar spectroscopic properties. Unmeasured radiance with FIR wavenumbers
between 25 and 650 cm-1 and those between 2760 and 3000 cm-1
(which we will term near-infrared (NIR) radiance) can be estimated from IASI
observations. For example, FIR wavenumbers in the strong H2O rotational
band at 25.25 cm-1 have strong correlations with those in the centre of
the 667 cm-1 CO2 and 1533 cm-1 H2O ν2 bands by
virtue of their similar sensitivity to high altitude temperatures
(Fig. ). However, the 1533 cm-1 band is physically more
similar to frequencies in the FIR and therefore has comparably larger
correlations.
Linear correlation coefficients between the radiances at
25.25 cm-1 and the rest of the spectrum. Data are simulated by the
LBLRTM from Phillips Soundings.
![]()
Adapting the simulation methodology of , the
LBLRTM is used to simulate long-wave spectra
over the spectral range 25–3000 cm-1 at 0.5 cm-1 resolution with
radiosonde data from 1596 soundings . This data set
was compiled by Norman Phillips of NOAA/National Meteorological Center (NMC) with the purpose of creating a representative
sample of the range of conditions found in the atmosphere, and has been
demonstrated to be adequate enough to base global models upon; see
. The profiles were measured during a 2-week
winter period and a 2-week summer period and continental and maritime
locations were sampled equally. The following details from
described the data set. Each sounding includes
temperature values at 65 different pressure levels extrapolated from 0.1 to
1000 mb and mixing ratios of H2O and O3 in the corresponding 64
layers. The soundings were compiled from radiosonde ascents from land and
ocean stations between 30∘ S and 60∘ N, and the soundings
were equally divided between tropical (30∘ S–30∘ N) and
mid-latitude (400 summer and 400 winter) conditions. The O3 data were chosen
to be climatologically consistent with the temperature profiles, and the
stratospheric H2O mixing ratio is assumed to be 3 ppmm. Stations with
surface heights above 300 m and profiles with no moisture below 700 mb were
not used.
Relationship of radiance at 33.75 and 2091.25 cm-1 simulated
by LBLRTM from Phillips Soundings, where the scatter points and fitting curve
are based on data for local zenith angles (LZA) of 0∘ (red),
21.48∘ (orange) and 47.93∘ (green). Units are
W m-2 sr-1 (cm-1)-1.
![]()
A second set of cloudy simulations was obtained by inserting a cloud into
each profile at a particular level (randomly distributed) to give 3200
different conditions, 1600 clear and 1600 cloudy. The water vapour profile
was not altered when a cloud layer was included, and the clouds were nearly
uniformly distributed in low (950–850 mb), middle (675–525 mb) and high
(400–240 mb) layers. Clouds are all considered to have 100 % horizontal
coverage of the profile. Those with cloud-top pressures greater than 450 mb
are assumed to be spectrally black, whereas the high level clouds are assumed
to have the spectral properties of cirrus given by
. Surface emissivities are assumed as unity
(black body) for all wavenumbers and surface types. Recent studies have shown
this is not an accurate assumption as emissivity varies spectrally and with
surface type ; however, over much of globe the FIR
itself measured from the TOA contains little contribution from the surface
due to the strong water vapour absorption. The channels that best estimate
this part of spectrum will have similar physical properties and hence are
around the water vapour ν2 band (see left panel of Fig. ), which is also
highly opaque to the surface in the regions where the radiosonde data were
taken. However, for those channels in the near and mid-infrared, or in
“microwindows”, this assumption will have an impact upon results though
the combined effect will likely be small. For certain polar scenes, such as
over the Antarctic Plateau, differences will be greater because the
atmosphere here is high and dry enough to allow more FIR emission to
space from the surface .
Several regression model formulations were investigated for the purpose of
NIR/FIR radiance prediction. A log–log transformation was found to provide
the optimal performance in minimization of estimation errors and regression
residual distributions. This empirical behaviour can also be explained
physically, as transmittances vary with optical path via an exponential
relationship and hence the model will be approximately linear.
Figure shows an example of the log–log relationship between
the
radiance at 33.75 cm-1 and the channel that has a maximum correlation
with it (2091.25 cm-1).
The wavenumbers of IASI observed radiance spectrum (y axis) that
show empirically the maximum correlation coefficients for the FIR (left) and
NIR (right) wavenumbers (x axis), based on a log–log
transformation.
![]()
The best predictor channels are selected as those with maximum correlation
coefficients between the log radiances (Fig. ) whose values are
shown in Fig. . For this application, the local zenith angle is
restricted to the nadir case. The prediction equation to estimate the
radiance IνFIR/NIR in either the FIR or NIR regions at
wavenumber ν can be written as
ln(IνFIR/NIR)=a0+a1ln(Iνpredictor),
where Iνpredictor is the radiance observed by IASI at the
predictor wavenumber (W m-2 sr-1 (cm-1)-1), and a0
and a1 are the calculated regression coefficients. The mean spectral
radiance calculated by LBLRTM for each wavenumber in the FIR is shown in the
left panel of Fig. and has a total integrated value of
36.32 W m-2 sr-1. The corresponding value for the NIR region is
0.028 W m-2 sr-1 (Fig. ). The root mean square (rms)
errors in the regression model serves as the theoretical estimates for the
reconstruction uncertainties in the reconstructed spectrum, shown in the
right panels of Figs. and . The rms of
the summed radiance errors, including cancellations from positive and
negative values, is 0.054 W m-2 sr-1 over all simulated regions
which gives a total relative error of 0.15 %. For comparison radiometric
noise from IASI is below 10-5 W-2 sr-1 cm-1 for
individual channels (Fig. 2 of ) Individual
rms relative errors are shown in Fig. which shows a
very low dependency on wavenumber for the FIR region, most of which do not
exceed 1 %, with higher relative errors seen in the extreme NIR due to low
absolute values.
The maximum correlation coefficients between wavenumbers in the FIR
(left) and NIR (right) and the corresponding predictor wavenumbers shown in
Fig. .
![]()
The mean FIR radiance spectrum based on all LBLRTM simulations
performed with the 3192 radiosonde profiles (left), and the spectral radiance
estimation errors (regression rms errors) associated with them (right). All
simulations shown are for a local zenith angle of 0∘.
![]()
The mean NIR radiance spectrum based on all LBLRTM simulations
performed with the 3192 radiosonde profiles (left), and the spectral radiance
estimation errors (regression rms errors) associated with them (right). All
simulations shown are for a local zenith angle of 0∘.
![]()
CERES single-scanner footprint Ed3A
The INLR product constructed from the extended IASI radiances is compared
with the existing CERES directional radiance product. The CERES SSF Edition 3A data set is obtained from the Atmospheric Science Data Center at the NASA
Langley Research Center for both the Terra and Aqua polar orbiting satellites
. In the cross-track scanning mode there are
90 FOVs in a single scanline with a 25 km footprint at nadir; however, in
terms of measurements and products it usually considered to have a resolution of
about 20 km. The swath takes 6.6 s to complete and has a maximum scan angle
of 65.8∘. For the present study only pixels with the minimum
satellite zenith angles, which are less than 1∘ (FOV 45 and 46) are
selected to retain only nadir-looking views. Each satellite carries two
identical CERES instruments. For the data acquired, Flight Model 1 (FM1) on
Terra and Flight Model 3 (FM3) on Aqua are operational in the cross-track
mode.
Cloud properties for CERES instruments are inferred from the
Moderate-Resolution Imaging Spectroradiometer (MODIS) imager which flies on
the same satellites, and are based on threshold tests with adjacent channels
. Clear and cloudy scenes are identified using the
NIR 3.7 µm channel on MODIS. Single channel retrieval of
cloud properties has its weaknesses; however, the 3.7 µm channel is
recommended for most cloud applications because of its greater sensitivity to
thin cloud-top microstructure and because it is less affected by surface
contamination than its neighbouring 1.6 µm channel; for example
. A recent comparison of MODIS retrievals
with in situ data also revealed that the 3.7 µm channel displays
the best agreement . A threshold clear fraction of
greater than 80 % is chosen to partition clear pixels.
CERES measures filtered radiance in terms of physical origin (i.e. thermal
or solar), rather than imposing wavelength boundaries; however, approximate
ranges for the three channels are reflected short wave (0.3–5 µm),
total (0.3–200 µm) and window (8–12 µm). Long-wave radiation
is determined from a weighted combination of measurements from the other
channels and hence all emitted thermal radiance that fall within the
0.3–200 µm (50– > 3000 cm-1) range are included.
Relative errors due to the process of unfiltering radiance are found to be
generally less than 0.2 % in the long wave . The
uncertainty in net TOA flux due to absolute calibration uncertainty including
the radiance-to-flux conversion is 2 % in the short-wave channel and 1 % in the
total channel at the 95 % confidence level .
Since night-time long-wave radiation is based only on the total channel, the
uncertainties are essentially the same at 1 %. For the daytime combining
the uncertainties of the short-wave channel yields an estimate of around 2.1 %,
which produces an average daily long-wave uncertainty of 1.5 % (see Appendix of
, for the derivation). Given that the present
study uses CERES unfiltered radiance only, contributed uncertainties from
the radiance-to-flux conversion do not apply, but as these errors are unknown
the total level of uncertainty has an upper bound of about 1.5 %.
Relative radiance estimation error for the FIR (left) and the NIR
(right) regions. Calculated from the rms values of the radiance
errors divided by the mean radiance spectrum (the right and left plots of
Figs. and , respectively).
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Determining absolute radiometric calibration uncertainty once in orbit is
dependent on a reference instrument and it remains a challenge to achieve a
reference traceable to international standards. This is a problem of such
critical importance that it led to the formation of an international effort
called the Global Space-Based Intercalibration System (GSICS)
. The current CERES Edition 3 product established
FM1 as the reference to place all the CERES instruments of the same
radiometric scale and as such will contain fewer correction uncertainties.
All flight models were corrected for spectral darkening at shorter
wavelengths (< 1 µm) due to UV exposure which caused degradation
in both the short-wave channel and the shorter wavelength region of the total
sensors. Studies that use an edition of CERES prior to Edition 3 will be
subject to this error, which overestimates flux by as much as a 0.8 %
(CERES long-wave flux daytime for FM1 and FM3; see the CERES Terra and Aqua
Edition3A SSF Data Quality Summary). Further refinements for the spectral
correction have been proposed for the CERES Edition 4 production
. This revision is expected to improve the accuracy
and stability of CERES data, particularly over the daytime land scenes. The
present study uses CERES Edition 3 data, and as such, it is important to be
aware of the possible errors relating to this version.
Locations of nearest nadir viewing SNOs, chosen as described in
Sect. between Metop-A and Terra (inner crosses) and Aqua (outer
circles) for (a) the Arctic, and (b) Antarctic, for
2012.
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Identifying simultaneous nadir overpasses
Two satellites in sun-synchronous polar orbits with different equatorial
crossing times will cross in the polar regions at approximately the same
north–south latitude each time. When radiometers from both satellites view
the same nadir scene at the same time this is called a simultaneous nadir
overpass (SNO). Using SNOs is preferable to comparing composite measurements
over the same time period because individual scene differences between cloud
and surface properties are avoided. This study uses the database of predicted
SNOs provided by the National Calibration Center of NOAA that is available at
http://ncc.nesdis.noaa.gov/SNO/SNOs//NCC_SNOs_prediction_service.html,
which makes SNO predictions based on the SGP4 orbital perturbation model
.
Aqua has local equatorial crossing times (LECTs) of 13:30 (ascending) and
01:30 (descending), and Terra has LECTs of 22:30 (ascending) and 10:30
(descending). MetOp-A has an ascending node of 21:30 and a descending node
LECT of 09:30. In all, 2012 SNOs between MetOp-A and Aqua, and MetOp-A and Terra, are
first filtered following the criteria set out in the methodology of
. This specifies that at the SNO: (1) the time difference
between nadir pixels is less than 30 s, and (2) the distance between nadir
pixels is less than the diameter of one footprint. Based on the average of
the 20 km CERES pixel and the 12 km IASI pixel this threshold is set to
16 km. This yields approximately 100 SNOs for each satellite pair over the
course of a year. Using the predictions the closest matches in terms of time
and distance were identified in the satellite data for the most nadir-looking
field of views for each instrument. The resulting locations of IASI pixels
identified as SNOs are shown in Fig. . By virtue of their
different equatorial crossing times, MetOp-A and Aqua SNOs all lie around
74∘ N–S and MetOp-A and Terra SNOs all lie around 81∘ N–S.
Validation of INLR at simultaneous nadir overpasses with CERES
For maximum consistency with CERES, IASI INLR is cut off at the
50 cm-1 lower wavenumber limit, and integrated over all remaining
radiance up to 3000 cm-1. INLR estimates from coincident IASI and
CERES pixels generally lie close together, with the majority falling within
2 W m-2 sr-1 of each other (Fig. ). In
general,
differences will be introduced by the slightly different nadir angles and
footprint sizes between CERES and IASI, and the accuracy of the co-locations.
Absolute values range from 30 to over 80 W m-2 sr-1 yet there is
no identifiable relationship between scene radiance and bias, indicating our
algorithm is robust against profile conditions at these latitudes. Night-time
radiance show a slightly higher correlation (0.99) compared with daytime
scenes (0.98). Whether the lower daytime correlation originates from errors
in the short-wave channel involved in estimating daytime CERES radiance, solar
backscatter contamination of either instrument or the increased variability
of daytime radiance is beyond the scope of this study.
Absolute values of INLR constructed from IASI on Metop-A against
CERES measurements for both the Terra and Aqua satellites at the closest SNO
events in 2012 for (a) day and (b) night.
![]()
Instantaneous biases between CERES and IASI INLR at SNO events with
standard errors. Standard errors are the standard deviations divided by the
square root of the total number of points. Units are W m-2 sr-1.
Figures in brackets are relative differences between the bias and the mean
radiation measured by both CERES and IASI.
All times
Day
Night
Both
0.33 ± 0.11 (0.53 %)
0.61 ± 0.17 (0.95 %)
-0.02 ± 0.14 (0.01 %)
Aqua
0.33 ± 0.14 (0.57 %)
0.48 ± 0.20 (0.78 %)
0.11 ± 0.19 (0.25 %)
Terra
0.32 ± 0.18 (0.50 %)
0.76 ± 0.28 (1.15 %)
-0.12 ± 0.2 (-0.17 %)
Clear
0.34 ± 0.13 (0.50 %)
0.84 ± 0.17 (0.31 %)
-0.21 ± 0.13 (-0.35 %)
Cloudy
0.30 ± 0.16 (0.51 %)
0.48 ± 0.23 (0.78 %)
0.06 ± 0.2 (0.15 %)
The same results shown as absolute biases (CERES – IASI) are presented as a
time series in Fig. , revealing no dependency of error upon
season. Table breaks down these biases by CERES instrument.
Mean IASI INLR values are about 0.3 W m-2 sr-1 lower than CERES
when all local times are considered, and individual differences are generally
within ±6 W m-2 sr-1. When split into clear and cloudy
scenes (Fig. ), it is evident that these larger biases are
associated with partly cloudy or overcast scenes and are likely due to
horizontal cloud inhomogeneity in the region of the SNO which can have a
large effect on the height, and hence temperature/radiance of emission.
Though this results in a larger standard deviation for all cloudy scenes, the
mean clear and cloudy biases are very similar, with a difference of
0.04 W m-2 sr-1) between them, suggesting our algorithm is
robust against this division of scenes. However, developing scene dependent
coefficients is a feature that could be added in the future to further
improve the method's performance, combined with an enhanced treatment of
different cloud types, such as cirrus, within the radiative transfer code.
Time series of INLR bias at SNOs between CERES and IASI for 2012 for
(a) day and (b) night. CERES measurements from Terra are
marked with crosses and those from Aqua are shown as dots. Standard error is
the standard deviation divided by the square root of the total number of
points.
![]()
Time series of INLR bias at SNOs between CERES and IASI in 2012 for
(a) cloudy and (b) clear pixels, as identified by the MODIS
near-infrared (NIR) 3.7 µm channel. CERES measurements from Terra are
marked with crosses and those from Aqua are shown as dots. Standard error is
the standard deviation divided by the square root of the total number of
points.
![]()
When relative differences are considered, long-wave radiance are about 0.5 %
higher in CERES than IASI overall (Table ). Split into daytime and
night-time scenes, it is apparent that this bias is dominated by daytime pixels
as the mean night-time relative error is only 0.01 %, whereas daytime
differences are 0.95 %. Higher biases are seen in the daytime relative to
night-time across all scene types and platforms and are calculated to be
statistically significantly different from zero, indicating a systematic bias
which could be related to the CERES Ed4 findings about the SRF correction
determination method described in Sect. .
The total outgoing long-wave spectral radiances
(25.25–2999.75 cm-1) constructed from IASI measurements (black) and
estimated far infrared radiance (blue) for four instantaneous scenes over:
(a) tropical equatorial land, (b) midlatitude land,
(c) the Sahara desert and (d) Antarctica. All are
night-time scenes from the 17 April 2012.
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All relative differences are well within the uncertainty range of CERES
unfiltered radiance based on absolute radiometric calibration uncertainty and
relative unfiltering errors as detailed in Sect. . Given that the
original correlation coefficients between radiances were calculated using
only tropical and mid-latitude profiles, the fact that the algorithm performs
well in polar regions shows that it is robust under different scene types;
however, maximum accuracy would be achieved by developing separate algorithms
for appropriate subsets.
Extended IASI spectral nadir radiance
Instantaneous spectrum
Example extended instantaneous IASI radiance spectra from 17 April, 2012, show
that the estimated FIR contributes between 42 and 64 % to the total INLR
depending on the scene type (Fig. ). This is within the range of
previous estimates . We present night-time scenes which
are when the FIR is particularly dominant as temperatures fall. In the
daytime, however, higher surface temperatures often allow the window region
to reach higher intensities when there is little or no cloud
. In non-cloudy cases temperature is the
dominating factor controlling the total intensity of long-wave radiance received at
the TOA. For example, the spectrum over the Sahara (Fig. c) emits
about double the total radiance as that over Antarctica (Fig. d).
However when clouds are present their height and coverage can have a highly
significant influence. For example it is certain that the temperature in the
tropics will be higher than that in Antarctica, and yet Fig. a
and d have similar values of total INLR. This is because it
is likely that deep convective cloud brings the height of tropical emission
to the cold upper troposphere where photons have lower energies. Clouds give
more weight to the FIR as part of the INLR overall. The desert and the
tropics are both warm regions and yet the FIR contributes 42 % of the
former clear, dry case and 62 % of the latter moist, cloudy case, which is
almost a third greater. The low stratiform clouds that are prevalent over
mid-latitude land will not have as large an effect on the whole spectrum
(Fig. b), but emission in the window region is still reduced with
respect to the FIR.
The outgoing long-wave spectral radiances constructed from IASI data
globally averaged for (a) clear (purple) and cloudy (green) pixels.
Numbers in brackets are the fractional FIR contributions to the total long-wave
broadband INLR. The all-sky curve is between the clear and cloudy curves but
is not plotted for clarity. (b) The difference between the clear-sky
and all-sky spectrums constructed from IASI measurements (black) and
estimated far infrared radiance (blue) from predictor wavelengths in the mid-infrared with the highest correlations (red dots). The number in
brackets
is the fractional contribution of the FIR INLR (FIR CINLR) to the total INLR
(CINLR). Data are the area weighted mean of April 2012.
![]()
Mean clear and cloudy spectrum
When split into global mean clear and cloudy scenes, an average of 47 % of
the total long-wave radiance comes from wavenumbers less than 645 cm-1 when
clouds are present, and 44 % when the atmospheric column is clear
(Fig. a). The peak wavelength of emission also shifts from 558.25
to 513.25 cm-1 in the cloudy only case. The NIR region constructed from
a similar method contributes near-negligible radiance of
0.03 W m-2 sr-1 (0.04 %) in cloudy cases and
0.05 W m-2 sr-1 (0.06 %) in clear cases. This is a region of
partial transparency, and hence like the 800–1250 cm-1 window is more
important in the clear-sky.
The difference between the averaged clear-sky and all-sky is equivalent to the
cloud effect, and using flux quantities this is often know as the cloud radiative forcing (CRF) or the cloud radiative effect (CRE). By analogy we
use the term “Cloud Integrated Nadir Long-wave Radiance” (CINLR) to refer to
the radiance equivalent of CRF. Figure b shows this radiance
equivalent cloud effect for the whole long-wave spectrum, with a total CINLR value
of 8.1 W m-2 sr-1. In general there is more outgoing radiation
at all wavenumbers in the clear-sky because liquid clouds are nearly opaque
to the whole infrared radiance spectrum and re-emit at lower
temperatures/energies than the clear-sky case. Wavebands at
0–200 cm-1, 650–700 cm-1 and around 1500 cm-1 are
strongly sensitive to rotational water vapour transitions, CO2 ν2
transitions, and the vibrational ν2 water vapour transitions,
respectively. As such peak emissions are in the upper troposphere–lower
stratosphere where clouds are few and hence the CINLR is low. Even though in
the cloudy case the FIR represents a more significant proportion of the total
INLR, the clear-sky still emits more over this wavelength range in terms of
absolute magnitude. Although the majority of the energy in the CINLR is
distributed over the atmospheric window spectral interval, the FIR still
accounts for 19 % of the total CINLR.
INLR and CINLR maps created from all April 2012 pixels binned to a
2.5 by 2.5 grid and averaged. Zonal means are shown to the right of each map.
On the left-hand side is all-sky: (a) INLR, (c) FIR as a
percentage of INLR, (e) the window region as a percentage of INLR.
On the right is CINLR (clear-sky–all-sky) for (b) INLR,
(d) the percentage of CINLR that is FIR, (f) the percentage
of CINLR that is in the window region. Note that the colour scales are
different for every panel. Missing data are shown in white.
![]()
Maps of INLR, FIR and window wavebands
Spatially, all-sky IASI INLR averaged over the whole month of April 2012
peaks in the clear desert and extra-tropical subsidence regions around
±20∘. In the latter, low maritime clouds emit radiation at high
temperatures similar to those at the surface (Fig. a). Deep
convective clouds over the inter-tropical convergence zone, Indo-Pacific warm
pool and monsoon regions of Africa and South America reduce INLR because
emission is from high, cold cirrus cloud tops. Correspondingly, these regions
also have the highest CINLR values (Fig. b), as the difference
between the all-sky and the clear-sky is at a maximum. An interesting feature of
this plot are occasional negative CINLR values, bordering the polar
continents. These values tend not to be lower than
-1 W m-2 sr-1. CINLR is generally a positive quantity; i.e. if
a cloud is added to any particular clear-sky scene instantaneously the
radiation emitted from the top of the cloud will be reduced with respect to
the clear-sky amount. However, when a lower tropospheric temperature
inversion is present, clouds can be warmer than the surface. These clouds are
particularly common over the Antarctic Plateau in austral winter as a result
of the snow-surface emissivity being greater than the atmospheric emissivity.
Additionally, cloud detection algorithms often struggle in the polar regions
due to lack of thermal contrast between ice covered surfaces and cloud tops.
Temperature inversions are also a prominent feature of the subtropical trade
wind regimes produced by the subsiding air masses in the descending branches
of the Hadley and Ferrel cells. As the mass descends the pressure increases
and its volume decreases adiabatically, and hence as the energy is unable to
be dissipated as heat its temperature rises warming the air and producing
shallow cumulus clouds with a higher temperature than the surface. Examples
of this behaviour are visible in the data around ±20∘ in
Fig. b.
The proportion of the radiance spectrum that falls within the FIR waveband
peaks in the coldest latitudes of Antarctica, as most of the outgoing photons
have very low energies and hence low wavenumbers (Fig. c). It is
also higher in regions of greater cloud cover, and this can be identified in
the deep convective cloud regions with respect to the surrounding clearer
areas, such as over the Sahara. The FIR and the window (WIN) waveband between
800 and 1250 cm-1 (Fig. e) are inverse to one another in
terms of zonal variability; i.e. when the FIR contribution is higher the WIN
contribution is lower and vice versa. However, the FIR contributes an average
of 40 % more that the WIN to the INLR overall in terms of absolute
magnitude. In terms of contribution to the CINLR though, the WIN is 3 times
greater on average than the FIR (Fig. d and f), but again the
patterns of zonal variability are inverse to each other. Interestingly, in
the subtropical subsidence regions there are some negative values of CINLR in
the FIR, meaning the average all-sky radiation is more than the average
clear-sky at these wavenumbers. As the total INLR and the WIN CINLR are still
positive in (most of) these locations these cannot be attributed solely to
temperature inversions. It is possible to speculate about the cause of this
behaviour; for example, clear skies associated with humid conditions and
trade wind inversion clouds associated with dryer conditions would result in
a higher emission level for the FIR. The dissipation of marine boundary layer
clouds resulting in moister clear areas surrounding dryer cloudy areas is a
phenomena that has been observed by previous studies
, and is something that could be verified by IASI
humidity retrievals in future work. As a result of these negative FIR CINLRs,
the corresponding positive WIN CINLRs peak at these locations because they
are now contributing more to the positive total INLR CINLR. This value is
still low due to these two parts of the spectrum cancelling with one another,
something that would go unobserved with a purely broadband analysis.
Conclusions and discussion
In this study we have shown that IASI can be used to simulate the entire
range of wavenumbers (25–3000 cm-1) needed to estimate the total
spectrum of outgoing long-wave radiances at a sampling resolution of
0.5 cm-1 in the FIR (< 645 cm-1) and the near
infrared (> 2600 cm-1). The method is based on correlations between
measured and un-measured parts of the spectrum, derived using simulations
from the line-by-line radiative transfer model (LBLRTM) applied to 3200
measured atmospheric profiles. Broadband observations on other satellite
platforms place constraints on the total radiant energy which effectively
provides a direct comparison of the simulated regions, assuming the parts of
the spectrum where CERES overlaps with IASI are in agreement, within the
bounds of uncertainty introduced by calibration differences and other
factors. This uncertainty is quantified at an upper limit of 1.5 % for long-wave
CERES radiance, and coincident all-sky measurements between IASI and CERES
at simultaneous nadir overpasses in polar regions show mean differences of
about 0.3 W m-2 sr-1 (0.5 % relative difference), which is
well within this range. This is a strict test of the regression model, given
that the two sets of measurements are completely independent and
approximately 50 % of the INLR is being estimated. However, precise
validation of each simulated channel is not possible at the present time and
such an assessment would have to be left for the future when space-borne FIR
measurements are made available. This study strengthens the case for such an
instrument with which to further validate and develop this model on a
spectral level.
Instantaneous examples of the simulated spectrum show the FIR
contributes between 43 and 64 % to the total Integrated Nadir Long-wave
Radiance (INLR) with a global weighted average of 47 % in the all-sky and
44 % in the clear-sky. The results of our comparison are similar to
previous values proposed in the literature for the angular integrated flux
(45 % for the all-sky) , which is interesting
considering they are two distinct quantities. This study serves as a proof of
concept of the usefulness of IASI for estimating the terrestrial FIR
at an unprecedented level of spectral resolution. Quantities such as cloud
radiative forcing (CRF) which are commonly studied only as a single integrated
quantity across the long-wave spectrum contain much more information when
examined on a spectral level, and in the absence of any corresponding
empirical data in the FIR region this product provides a “next best”
alternative.
It is feasible that this product could be developed by applying angular
distribution models (ADMs) to the radiance to give flux estimates using a similar
approach taken by previous studies (e.g. ), and
as such IASI has the potential to be supplement existing broadband instrument
observations. The algorithm as it stands is self-contained for all scene
types; however, as anisotropy varies considerably with scene the regression
algorithm could be customised to consider cloud cover, surface type and
further inhomogeneities. Other inclusions in the construction of the model,
such as instrument noise and determination of the optimal spectral interval
size for the predictors could additionally refine the models performance
further in the future, and as such we consider the model presented herein as
a “first version”. Given that IASI will eventually be carried by
three different MetOp satellites in the same local-time orbit, and IASI – new
generation proposed for the second generation of MetOp satellites will have
even higher sampling resolution (0.125 cm-1)
, this provides the possibility of a product
with valuable length and the ability to be inter-satellite calibrated between
instruments.