AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-1403-2017Long-term assessment of the CALIPSO Imaging Infrared Radiometer (IIR)
calibration and stability through simulated and observed comparisons with
MODIS/Aqua and SEVIRI/MeteosatGarnierAnneanne.garnier@latmos.ipsl.frScottNoëlle A.PelonJacquesArmanteRaymondCrépeauLaurentSixBrunoPascalNicolasScience Systems and Applications, Inc., Hampton, VA 23666, USANASA Langley Research Center, Hampton, VA 23681, USALaboratoire de Météorologie Dynamique, Ecole Polytechnique–CNRS,
91128 Palaiseau, FranceLaboratoire Atmosphères, Milieux, Observations Spatiales,
UPMC–UVSQ–CNRS, 75252 Paris, FranceUniversité Lille 1, AERIS/ICARE Data and Services Center, 59650 Lille, FranceHygeos, AERIS/ICARE Data and Services Center, 59650 Lille, FranceAnne Garnier (anne.garnier@latmos.ipsl.fr)13April20171041403142413October201614November20166March201721March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/1403/2017/amt-10-1403-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/1403/2017/amt-10-1403-2017.pdf
The quality of the calibrated radiances of the medium-resolution Imaging
Infrared Radiometer (IIR) on-board the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite is quantitatively
evaluated from the beginning of the mission in June 2006. Two complementary
“relative” and “stand-alone” approaches are used, which are related to
comparisons of measured brightness temperatures and to
model-to-observations comparisons, respectively. In both cases, IIR channels
1 (8.65 µm), 2 (10.6 µm), and 3 (12.05 µm) are paired with
the Moderate Resolution Imaging
Spectroradiometer
(MODIS)/Aqua Collection 5 “companion” channels 29, 31, and 32, respectively,
as well as with the Spinning Enhanced
Visible and Infrared Imager (SEVIRI)/Meteosat companion channels IR8.7, IR10.8, and IR12,
respectively. These pairs were selected before launch to meet radiometric,
geometric, and space-time constraints. The prelaunch studies were based on
simulations and sensitivity studies using the 4A/OP radiative transfer model
and the more than 2300 atmospheres of the climatological Thermodynamic
Initial Guess Retrieval (TIGR) input dataset further sorted into five air
mass types. Using data from over 9.5 years of on-orbit operation, and
following the relative approach technique, collocated measurements of IIR
and of its companion channels have been compared at all latitudes over
ocean, during day and night, and for all types of scenes in a wide range of
brightness temperatures. The relative approach shows an excellent stability
of IIR2–MODIS31 and IIR3–MODIS32 brightness temperature differences (BTDs)
since launch. A slight trend within the IIR1–MODIS29 BTD, that equals -0.02 K yr-1 on average over 9.5 years, is detected when using the relative
approach at all latitudes and all scene temperatures. For very cold scene
temperatures (190–200 K) in the tropics, each IIR channel is warmer than its
MODIS companion channel by 1.6 K on average. For the stand-alone approach,
clear sky measurements only are considered, which are directly compared with
simulations using 4A/OP and collocated ERA-Interim (ERA-I) reanalyses. The clear sky
mask is derived from collocated observations from IIR and the CALIPSO lidar.
Simulations for clear sky pixels in the tropics reproduce the differences
between IIR1 and MODIS29 within 0.02 K and between IIR2 and MODIS31 within
0.04 K, whereas IIR3–MODIS32 is larger than simulated by 0.26 K. The
stand-alone approach indicates that the trend identified from the relative
approach originates from MODIS29, whereas no trend (less than ±0.004 K yr-1) is identified for any of the IIR channels. Finally, using the
relative approach, a year-by-year seasonal bias between nighttime and
daytime IIR–MODIS BTD was found at mid-latitude in the Northern Hemisphere.
It is due to a nighttime IIR bias as determined by the stand-alone approach,
which originates from a calibration drift during day-to-night transitions.
The largest bias is in June and July
when IIR2 and IIR3 are warmer by 0.4 K on
average, and IIR1 is warmer by 0.2 K.
Introduction
The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) satellite (Winker et al., 2010), launched in April 2006, includes
a payload of three instruments, the Cloud-Aerosol Lidar with Orthogonal
Polarization (CALIOP), a visible wide-field camera, and the Imaging Infrared
Radiometer (IIR). The IIR was built in France by the Centre National
d'Études Spatiales (CNES), the Société d'Études et de
Réalisations Nucléaires (SODERN), and Institut Pierre Simon Laplace
(IPSL) (Corlay et al., 2000). It includes three spectral bands in the
thermal infrared atmospheric window, at 8.65 µm (IIR1), 10.6 µm
(IIR2),
and 12.05 µm (IIR3) with bandwidths of 0.85, 0.6, and 1 µm, respectively. These three channels were chosen
to optimize retrievals of ice cloud properties in synergy with collocated
observations from the CALIOP lidar (Garnier et al., 2012, 2013).
It is now well recognized by the international community that to be fully
useful for climate and meteorological applications, satellite observations
require quality control during the instrument's lifetime. Indeed, any
systematic error or spurious trend not identified in the calibrated
radiances may induce artifacts in the retrieved variables. In the mid-1990s,
the NOAA/NASA Pathfinder Program and, later in 2005, the Global
Space-based Inter-Calibration System (GSICS) initiated international
collaborative efforts to improve and harmonize the quality of observations
from operational weather and environmental satellites in order to create
climate data records (e.g., Goldberg et al., 2011). A recent update on the
GSICS vision is given in a 2015 World Meteorological Organization (WMO)
report (GSICS, 2015).
In this paper, IIR observations since launch are monitored and characterized
using two complementary approaches, which are the heritage of the processing
of numerous years of satellite radiances for the restitution of climate
variables (Chédin et al., 1985, and papers following on similar topics
referenced and regularly updated at
http://ara.abct.lmd.polytechnique.fr/index.php?page=publications). The
monitoring of observational and computational biases or trends over long
periods of time started with the NOAA/NASA TIROS Operational Vertical
Sounder (TOVS) Pathfinder Program (Scott et al., 1999). Since then, it has
been implemented for more recent hyperspectral sounders, namely the
Atmospheric Infrared Sounder (AIRS) on-board the Aqua satellite since 2003,
and since 2007 for the Interféromètre Atmosphérique de Sondage
Infrarouge (IASI) on the MetOpA and MetOpB satellites in cooperation with CNES (e.g., Jouglet
et al., 2014) within the frame of its GSICS activities.
The first approach, called the “relative” approach, is based on a channel to
channel intercomparison of radiances, further converted into equivalent
brightness temperatures, with collocated measurements in companion channels
of companion instruments under controlled conditions. The relative approach,
sometimes referred to as the “inter-channel” or “inter-calibration”
approach, was initially developed in a geostationary–low Earth orbit
(GEO–LEO) combination for the calibration of Meteosat-1, based on space and
time collocations with TOVS on the NOAA TIROS-N series (Bériot et al.,
1982). The second approach, called the “stand-alone” approach, is based on
comparisons between measured and simulated brightness temperatures for each
companion channel of each companion instrument. The two approaches are
complementary: the inter-calibration approach studies the behavior of one
channel relative to its companion regardless of the underlying clear or
cloudy scene and therefore allows for the study of a wide range of
brightness temperatures. The stand-alone approach screens each channel of
each instrument, individually, for clear sky scenes and helps to identify
which channel deviates from the other(s). In this study, the clear sky mask
is derived from collocated observations from the IIR and CALIOP.
The IIR companion instruments chosen for this study are the Moderate Resolution
Imaging Spectroradiometer (MODIS) on-board Aqua and the Spinning Enhanced
Visible and Infrared Imager (SEVIRI) on-board the geostationary second
generation satellites Meteosat 8, 9, and 10. Both the MODIS and SEVIRI
instruments, in operation since 2002 and 2004, respectively, include
medium-resolution spectral bands similar to IIR channels.
An assessment of IIR radiances after 9.5 years of nearly continuous
operation is presented, thereby updating the first results published in
Scott (2009). This paper presents a brief description of the IIR instrument
in Sect. 2, with prelaunch studies for the selection of IIR companion
instrument and channels identified in Sect. 3, and the implementation of the
relative and stand-alone approaches described in Sect. 4. Results and
findings from the relative and the stand-alone approaches are presented in
Sect. 5 and Sect. 6, respectively, and our assessment is summarized in the
last section.
The IIR/CALIPSO instrument
The entire CALIPSO payload flies in a near-nadir, down-looking
configuration, with a 0.3∘ angle off-nadir in the forward
direction. The off-nadir angle was increased to 3∘ at the end of
November 2007 to reduce specular reflections from horizontally oriented ice
crystals in CALIOP observations (Hu et al., 2009). The IIR instantaneous
field of view is ±2.6∘ or 64 × 64 km on the ground, with 1 km
size pixels. Thus, the viewing angles from nadir range from 0 to
3∘ until November 2007 and from 1 to 6∘ after
the CALIPSO payload pitch change, which is not expected to have any
identifiable impact on the observations.
The IIR instrument (Corlay et al., 2000) includes three filters which are
mounted on a rotating wheel for sequential acquisition within their
respective spectral bands. The sensor is an uncooled microbolometer array
(U3000) manufactured by Boeing and also implemented in the IASI
instruments. IIR is regularly calibrated in flight using images of a
temperature-monitored warm blackbody source (at about 295 K) and from cold
deep space (about 4 K) views. For each spectral band and for each pixel in
the image, cold space views are used to determine the offset of the
detection system, and images from the calibration blackbody source at known
temperatures are used to retrieve the gains. The linearity of the detection
system was verified before launch for scene temperatures ranging between 215
and 320 K, and the gain retrieved from the warm calibration source is
directly applied to calibrate the earth view images. Instrument spectral
response functions (ISRFs) for each band were established by CNES before
launch and are available upon request.
The overall calibration accuracy of the IIR measurement is specified to be
better than 1 K in all channels. In-flight performances were assessed by
CNES at the beginning of the mission (Trémas, 2006; Tinto and
Trémas, 2008). The in-flight short-term gain stability was found to be
better than 0.1 K of equivalent brightness temperature, except during the
day-to-night transitions, during which the gain varies by up to 0.45 K. The
in-flight noise equivalent differential temperature (NedT) at 210 K was
between 0.2 and 0.3 K, similar to values measured before launch and better
than the specified value of 0.5 K. The NedT is of the order of 0.1 K at 250 K.
The calibrated radiances are reported in the CALIPSO Version 1 IIR Level 1B
products (Vaughan et al., 2015) available from the Atmospheric Science Data
Center of the NASA Langley Research Center and at the AERIS/ICARE data
center in France. They are resampled and registered on a 1 km resolution
unique grid centered on the CALIOP ground track, at sea level, with a 69 km
swath. The products are organized by separating the daytime and nighttime
portions of an orbit to match the definition chosen for CALIOP and thereby
facilitate synergetic analyses. Because the lidar is very sensitive to
daylight background noise, the nighttime portion of the orbit is when the
solar elevation angle at earth surface is less than -5∘ (Hunt et
al., 2009).
Main characteristics of the instruments and channels considered for
this study.
InstrumentIIRMODISSEVIRIPlatformCALIPSOAQUAMeteosat 8, 9, and 10OrbitLEO, A-TrainLEO, A-TrainGEO73 s behind AQUATemporal coverageSun-synchr. 13:43Sun-synchr. 13:3516-day repetition16-day repetition15 min repeat cycleGeographicalFrom WRS-2 gridWRS-2 gridcoverage for the studyLat.: 82∘ S–82∘ NLat.: 82∘ S–82∘ NLat.: 10∘ S–10∘ NAll longitudesAll longitudesLong.: 10∘ W–10∘ ESpectral bands#1: 8.2–9.05 µm#29: 8.4–8.7 µmIR8.7: 8.3–9.1 µm#2: 10.35–10.95 µm#31: 10.78–11.28 µmIR10.8: 9.8–11.8 µm#3: 11.6–12.6 µm#32: 11.77–12.27 µmIR12: 11.0–13.0 µmSwath69 km2330 kmFull diskResolution1 km1 km3 km sub-satelliteNedT0.2–0.3 K @ 210 K< 0.025 K< 0.12 K0.1 K @ 250 K(Xiong et al., 2015)(EUMETSAT, 2007a)Specification: < 0.5 KMethod and prelaunch studies
Both the relative and the stand-alone approaches require (i) companion
instruments that offer the best possible spatiotemporal coincidences with
IIR (primarily in order to see the same scenes simultaneously), and (ii) companion channels presenting close characteristics in terms of spectral
coverage and spatial resolution. These constraints have oriented our choice
towards two companion instruments: MODIS/Aqua and SEVIRI/Meteosat.
Description of companion instruments
MODIS/Aqua and SEVIRI/Meteosat in-flight performances have been extensively
characterized (Xiong et al., 2015; EUMETSAT, 2007a, and references herein).
The main instrumental characteristics of interest for comparison with IIR
are summarized in Table 1 and are detailed in the following sub-sections.
Histograms of MODIS viewing angles for clear sky pixels collocated
with the IIR swath over ocean in July 2014. Top row from left to right:
60–82, 30–60, 0–30∘ N. Bottom row from left to right: 82–60, 60–30, and 30–0∘ S.
MODIS/Aqua
The first companion instrument chosen for this study, MODIS/Aqua, includes
three medium-resolution spectral bands in the thermal infrared window (29,
31, and 32), with 1 km spatial resolution of interest for comparisons with
IIR. Furthermore, both Aqua and CALIPSO, which is nominally positioned 73 s
behind Aqua, have been flying in formation with other satellites of the
A-Train (Stephens et al., 2002) since June 2006. The A-Train satellites
follow a Sun-synchronous polar orbit at 705 km altitude with a
98.2∘ inclination. CALIPSO measurements provide global coverage
between 82∘ N and 82∘ S. There is a 16-day repetition
cycle, with an equator crossing time at about 13:35 local time. CALIPSO is
controlled according to a customized grid shifted by 7 min 44 s local time
from the nominal Worldwide Reference System-2 (WRS-2)
grid used by Aqua, or 215 km eastward at the equator
crossing. Due to the relative positions of the CALIPSO and Aqua satellites
in the A-Train, the MODIS 2330 km swath always covers the IIR 69 km swath.
The viewing angles for the MODIS pixels collocated with the IIR swath vary
with latitude, as illustrated in Fig. 1, which shows histograms of MODIS
viewing angles for six 30∘ latitude bands for clear sky pixels over
ocean in July 2014. The MODIS viewing angle is largest at the equator and
ranges from 12 to 20∘ at 0–30∘
latitude in both hemispheres. It decreases progressively as latitude
increases, to be mostly between 8 and 18∘ at
30–60∘ north and south and less than 12 at
60–82∘ N. The pixels over ocean south of
60∘ S are actually north of about 70∘ S due the presence
of the Antarctic continent, which explains why histograms at 82–60∘ S exhibit larger angles on average than those at
60–82∘ N. These geometries of observation are
accounted for in the simulations.
SEVIRI/Meteosat
Comparing IIR and MODIS/Aqua observations, both from the A-Train, ensures a
very large sampling for statistical analyses. A complementary view is
available through comparisons with SEVIRI on-board the geostationary second
generation Meteosat satellites. SEVIRI provides radiometric data every 15 min in three spectral bands in the thermal infrared (channels IR8.7,
IR10.8, and IR12) with a 3 km resolution at the sub-satellite point, which is
at 0∘ in both latitude and longitude for the prime satellites.
SEVIRI data are from three different prime geostationary satellites since
June 2006: Meteosat 8 until 11 April 2007, then Meteosat 9 until 21 January
2013, and currently Meteosat 10. IIR and SEVIRI are best compared when
SEVIRI viewing angles are smaller than 10∘ to be close to IIR
quasi-nadir observations, therefore, at latitudes between 10∘ N and
10∘ S and longitudes between 10∘ W and 10∘ E.
Selection of IIR, MODIS, and SEVIRI companion channels
The IIR companion channels were chosen before launch among the various
spectral channels of MODIS/Aqua and SEVIRI/Meteosat following a specific
procedure. For each IIR channel, the selected MODIS or SEVIRI companion
channel is the one that not only minimizes the brightness temperature
differences (BTDs) with IIR, but also shows a similar sensitivity to the
atmosphere and to the surface. This evaluation was conducted before launch
by simulating the IIR and candidate companion channels using the forward
radiative transfer model 4A (4A: Automatized Atmospheric Absorption Atlas)
and more than 2300 atmospheres from the Thermodynamic Initial Guess
Retrieval (TIGR) input dataset. MODIS/Aqua ISRFs can be retrieved from the
MODIS Characterization Support Team website
(http://mcst.gsfc.nasa.gov/calibration/parameters) and SEVIRI/Meteosat ISRFs
from the EUMETSAT MSG Calibration website
(http://www.eumetsat.int/website/home/Data/Products/Calibration/MSGCalibration/index.html).
In addition to the radiative coherence of the different pairs of channels,
the quality of the comparisons performed for both the relative and the
stand-alone approaches is based on the highest possible homogeneity of the
observed surfaces and of the atmospheric optical paths. Differences in the
spectral position and shape of the ISRFs can induce differences in surface
emissivity. Furthermore, regardless of the perfection of the space-time
collocation of the different instruments, the emitting surface may still be
observed under different conditions. This is inherently related to the
difference in the pixel size of each instrument as well as to the difference
in the optical paths resulting from different viewing angles. Handling these
differences is more difficult over land where altitude and nature of the
ground contribute to enhance the inhomogeneity of the observed scenes. We
have chosen to minimize these issues by restricting our study to
observations over ocean. It is worthwhile to note that this choice allows a
robust calibration assessment, in a wide range of brightness temperatures,
at most latitudes and at any season, which is compatible with the aim of our
study. Future work will include analyses at the highest latitudes, which are
currently not examined because they are covered by ice, and analyses in
extreme conditions of land surface temperatures. The simulated BTDs between
IIR and the selected companion channels are shown in Sect. 3.2.3, after a
brief description of the simulation model and of the auxiliary datasets in
Sect. 3.2.1 and 3.2.2.
The forward radiative transfer model: 4A
The radiative transfer model used at all stages of this study is 4A/OP, the
operational version of 4A, adapted and maintained by NOVELTIS
(http://4aop.noveltis.com/) in collaboration with CNES and Laboratoire de
Météorologie Dynamique (LMD). The 4A model is a fast and accurate line-by-line
(LBL) radiative transfer model initially developed at LMD (Scott and
Chédin, 1981). As recalled in Anthony Vincent and Dudhia (2017), 4A was
among the pioneer radiative transfer models to bypass LBL processing time by
calculating once and for all a set of compressed look-up tables (LUTs) of
monochromatic optical depths. These LUTs are generated by the nominal
line-by-line STRANSAC model (Scott, 1974; Tournier et al., 1995) coupled to
the Gestion et Étude des Informations Spectroscopiques Atmosphériques
(GEISA) spectroscopic database. The 4A model generates transmittances, radiances,
and jacobians (Chéruy et al., 1995) for any instrumental, spectral, and
geometrical configuration (ground, airborne, and satellite).
The 4A model has a long history of validation within the frame of the international
radiative transfer community. From the very beginning, most of the
validation results have been extensively discussed in a number of
intercomparison exercises and in particular during the Intercomparison of
Transmittance and Radiance Algorithms (ITRA)
working groups – 1983, 1985,
1988, and 1991 – of the International Radiation Commission (Chédin et al.,
1988) and during the Intercomparison of Radiation Codes in Climate Models
(ICRCCM) campaigns (Luther et al., 1988). More recently, observations from
hyperspectral sounders such as AIRS and IASI have led to even more extensive
validations, again within the frame of international observation campaigns
or working groups, among them the International TOVS Study Conferences
(ITSC) and the IASI Sounding Science Working Group (ISSWG).
The 4A model is the
official code selected by CNES for calibration and validation activities of the
IASI, Merlin (https://merlin.cnes.fr), and MicroCarb
(https://microcarb.cnes.fr) missions. A detailed description of the protocol
and of the results of the interactive validation of GEISA and 4A/OP may be
found in Armante et al. (2016).
Throughout the 9.5 years of IIR operation analyzed here, we have used a
static version of 4A (2009 version) in order to avoid undesirable, however
smooth, jumps. The LUTs were generated with STRANSAC coupled to the 2011
version of GEISA (Jacquinet-Husson et al., 2011). The 4A/OP model is in
“down–up” mode, which means that the emission by the surface, the
upwelling atmospheric radiation, and the reflection at the surface of the
downwelling atmospheric radiation are taken into account, modulated by the
emissivity or the reflectivity. The attenuated reflected downward radiance
has been computed using a constant diffusivity factor. This approximation
avoids computing a large number of downward radiances (and, above all, the
computation of a large number of transmittance functions) corresponding to a
large number of incident angles as well as integrating overall these
angles. Such an approximation for the integration over the angle is usual in
radiative transfer calculations: it was suggested as early as 1942 by
Elsasser. Later on, tests on the validity of this approximation have been
presented by Rodgers and Walshaw (1966), Liu and Schmetz (1988), Turner (2004), and many others. A value of 53∘ is used here for the
computation of the downwelling reflected radiances. Simulations are
conducted for relevant MODIS and SEVIRI viewing angles (see Sect. 3.1 and
Fig. 1), and IIR is considered as a nadir viewing instrument. Because the
viewing angles are smaller than 20∘, no specific dependence of
the emissivity on the emission angle has been taken into account. A mean
ocean surface emissivity equal to 0.98 for all the channels is used for
these simulations.
Atmospheric inputs: the TIGR dataset
The simulations have been conducted for the 2311 atmospheres of the TIGR
climatological library (Chédin et al., 1985; Chevallier et al., 1998).
The 2311 atmospheres are sorted into five air mass types according to their
virtual temperature profiles (Achard, 1991; Chédin et al., 1994), which
are namely (1) tropical, (2) mid-lat1 for temperate conditions, (3) mid-lat2 for cold temperate and
summer polar conditions, (4) polar1 for very cold polar conditions, and (5) polar2 for
winter polar conditions. The tropical air mass type is composed of 872 atmospheres,
mid-lat1 and mid-lat2 are composed of 388 and 354 atmospheres, respectively, and polar1 and
polar2 are composed of 104 and 593 atmospheres, respectively.
IIR/CALIPSO (red), MODIS/Aqua (green), and SEVIRI/Meteosat 8, 9, 10
(navy blue, medium blue, light blue) instrument spectral response functions
against wavelength in microns (top x axis) and wavenumber in cm-1
(bottom x axis).
Brightness temperatures simulations
The 4A model and the TIGR atmospheres input data have been used to simulate
the brightness temperatures of IIR and of the candidate companion channels.
Each of the five TIGR air mass types includes one individual simulation for
each individual atmosphere included in the air mass type (i.e., 872
simulations for the tropical type, 388 for mid-lat1, 354 for mid-lat2, 104 for polar1, and 593 for
polar2). Each TIGR air mass type is then characterized through the mean
BTD
between IIR and MODIS or SEVIRI channels and associated standard deviations
derived from the individual simulations (hereafter “TIGR_BTD”). The simulations presented here have been obtained using the 2009
version of the 4A/OP model described above, which does not call into
question the initial evaluations conducted before the 2006 launch.
The most suitable radiometric pairings of IIR–MODIS channels for our study
are IIR1–MODIS29, IIR2–MODIS31, and IIR3–MODIS32. Similarly, the most
suitable IIR–SEVIRI pairs are IIR1–SEVIRI8.7, IIR2–SEVIRI10.8, and IIR3-SEVIRI12.
Simulations were for SEVIRI/Meteosat 8, which was the primary
satellite in June 2006, but our channel selection remains unchanged for the
more recent instruments. The ISRFs of these nine channels are plotted in
Fig. 2. ISRFs of SEVIRI Meteosat 9 and 10 are also shown for visual
comparison with Meteosat 8. Brightness temperatures derived from the modeled
radiances are computed using the relevant ISRF. As an indication, shown in
Table 2 are the equivalent central wavenumbers and wavelengths that minimize
the differences between the true temperature and the temperature derived
using the Planck function over a range of temperatures stretching from 200 K
to 310 K. The central wavenumbers are relevant in the case of blackbody
radiances expressed in W m-2 sr-1 cm, as in the output of the 4A
model, whereas the central wavelengths are relevant in the case
of radiances reported in W m-2 sr-1µm-1, as in IIR and MODIS
satellite observations. It is noted that the equivalent central wavelengths
of IIR1, IIR2, and IIR3 are found to be 8.635, 10.644, and
12.096 µm, respectively.
Simulated brightness temperature difference (TIGR_BTD) in Kelvin between IIR and MODIS/Aqua companion channels for MODIS
viewing angles of 0, 12, and 20∘,
whenever relevant (NA if not), and standard deviation (in italic) for five
air mass types from the TIGR data base.
TIGR IIR1–MODIS29IIR2–MODIS31IIR3–MODIS32Air massNumber of Atm.0∘/12∘/20∘/SD0∘/12∘/20∘/SD0∘/12∘/20∘/SDtropical872NA/0.23/0.37/0.13NA/0.27/0.37/0.33NA/-1.02/-0.89/0.28mid-lat13880.05/0.09/0.17/0.070.02/0.04/0.07/0.06-0.54/-0.52/-0.49/0.29mid-lat23540.04/0.08/0.14/0.06-0.01/-0.00/0.02/0.02-0.41/-0.39/-0.37/0.19polar11040.0/-0.00/NA/0.04-0.01/-0.00/NA/0.03-0.13/-0.13/NA/0.15polar2593-0.01/0.03/NA/0.060.03/0.03/NA/0.03-0.15/-0.15/NA/0.15
Simulated brightness temperature difference (TIGR_BTD) in Kelvin between IIR and SEVIRI/Meteosat 8 companion channels for
SEVIRI viewing angles of 0 and 12∘, and standard
deviation (in italic) for the tropical air mass type from the TIGR data
base.
The TIGR_BTDs between IIR and MODIS companion channels are
reported in Table 3 for the five TIGR air mass types. They are given for
MODIS viewing angles of 0, 12, and 20∘,
chosen according to the latitude-dependent, and therefore air mass
type-dependent, range of viewing angles discussed in Sect. 3.1 and shown in
Fig. 1. The TIGR_BTDs for each pair of companion channels and
their variations with the TIGR air mass type encompass the difference in
shape and position of the paired ISRFs and the inherent different
sensitivity to surface temperature, temperature and water vapor profiles,
and other absorbing atmospheric constituents. For the three pairs of
channels, the absolute TIGR_BTDs and the standard deviations
are overall larger for the tropical air mass type than for the other air
mass types, which is related to the high content and high variability of the water
vapor in the tropical regions. Except for the tropics, absolute
TIGR_BTDs are smaller than 0.2 K for IIR1–MODIS29 and 0.1 K
for IIR2–MODIS31, with similar standard deviations smaller than 0.1 K. The
largest absolute TIGR_BTDs and standard deviations are for
the IIR3–MODIS32 pair, with TIGR_BTDs of about -1 K and
standard deviations up to 0.3 K for tropical air mass types.
TIGR_BTDs between IIR and SEVIRI/Meteosat 8 companion
channels for the TIGR tropical air mass and SEVIRI viewing angles equal to
0 and 12∘ are reported in Table 4. For a 12∘
viewing angle, IIR–SEVIRI TIGR_BTDs differ by up to 0.5 K
from the respective IIR–MODIS TIGR_BTDs.
Implementation of the relative and stand-alone approaches
For both the relative and the stand-alone approaches, observations of IIR
and its companion channels are first spatially and temporally collocated, as
described below, followed by the various steps specific to the
implementation of each approach.
Collocations
Collocated observations are from the REMAP product that is developed,
processed, and available at the AERIS/ICARE data center. REMAP includes
MODIS/Aqua and SEVIRI calibrated radiances collocated with the IIR Level 1B
radiances and remapped onto the IIR 69 km grid. MODIS calibrated radiances
are from MYD021KM Collection 5 (C5) with geolocation from MYD03 C5. SEVIRI
geolocated and calibrated radiances are from the Level 1.5 Image product,
which reports spectral blackbody radiances until 7 May 2008 and effective
blackbody radiances afterwards (EUMETSAT, 2007b). For each IIR pixel, the
collocated MODIS or SEVIRI radiance is from the closest pixel, at sea level.
So far, no spatial averaging of the IIR or MODIS 1 km pixels is performed in
order to get a better match with SEVIRI pixels. Thus, one 3 km resolution
sub-satellite SEVIRI pixel is collocated with at least nine different IIR
pixels, depending on the SEVIRI viewing angle. IIR and MODIS pixels are
collocated with the temporally closest SEVIRI image, which is up to 7 min 30 s before or after the companion observation. IIR and MODIS observations are
quasi-coincident and are therefore considered always temporally collocated.
Overall, IIR and MODIS observations are well collocated, whereas a naturally
occurring GEO–LEO “mismatch” between SEVIRI and IIR observations cannot be
ignored spatially, because of the difference in the pixel sizes and the
difference in the satellite zenith angles, nor ignored temporally, because
the time difference between the observations can be up to several minutes.
This spatial mismatch is minimized by comparing IIR and SEVIRI when SEVIRI
viewing angles are less than 10∘.
Relative approach: outputs and statistical analyses
Outputs of the relative approach are presented here showing daily means of
BTDs and standard deviations. They have been generated for each single day
since launch, with daytime and nighttime data either combined or separated, for
several 10 K ranges of observed brightness temperatures, from 290–300 down
to 200–210 K.
Statistical analyses of BTDs between pairs of channels over ocean are
performed for five latitude ranges: in the tropics (30∘ S–30∘ N), and at mid- (30–60∘) and polar
(60–82∘) latitudes in both hemispheres. Oceanic
scenes are identified using an index available from the Global Land
One-kilometer Base Elevation (GLOBE)
project (GLOBE Task Team and others,
1999). Thresholds are defined, which are based on the simulated
TIGR_BTDs and associated standard deviations (see Tables 3 and 4) and on the expected instrumental NedT (see Table 1). A “worst
case” standard deviation σ has been computed by taking 0.4 K for
TIGR_BTD, the IIR NedT specified before launch (0.5 K), and
NedT = 0.1 K for MODIS and SEVIRI, yielding σ= 0.7 K. Using
TIGR_BTDs corresponding to each latitude band, BTDs larger or
smaller than TIGR_BTD ± 3σ (i.e., ±2.1 K) are considered unrealistic values due to the fact that the instruments
presumably do not see the same scenes. The statistics are computed after
rejecting these unrealistic values. Because the collocations are at sea
level, these tests should minimize parallax issues in the case of elevated
clouds.
Stand-alone approachClear sky mask
After collocation of IIR, MODIS, and SEVIRI companion channels (Sect. 4.1), a
clear sky mask is applied to select the relevant pixels for direct
comparisons between observations and simulations. The mask is from the
Version 3 IIR Level 2 swath product (Vaughan et al., 2015). It is derived
from collocated IIR and CALIOP observations along the lidar track and
extended to the 69 km IIR swath by using radiative homogeneity criteria
(Garnier et al., 2012). In the Version 3 IIR Level 2 operational algorithm,
clear sky track pixels are defined as those pixels for which no cloud layers
could be detected by CALIOP and no depolarizing aerosol layers could be
detected after averaging the lidar signal up to 20 km along the track. This
information is extracted from the CALIOP Level 2 5 km cloud and aerosol
layer products (Vaughan et al., 2015). Initial analyses determined that the
Version 3 mask is contaminated by the presence of low cloud layers detected
by CALIOP at the finest 1/3 km resolution, but not reported in the 5 km
layer product, so they are ignored by the IIR algorithm. This issue will be
corrected in the next version (4) of the IIR operational algorithm. Because
the new operational product is not available at this time, a corrected mask
has been produced specifically for this study. We chose to process each
occurrence of January and July from mid-June 2006 to December 2015 to cover
the same 9.5-year time period as used in the relative approach for two
opposite seasons.
Clear sky brightness temperatures simulations
Clear sky simulations of the collocated observations (Sect. 4.1) are carried
out using the 4A/OP model (see Sect. 3.2.1) and the temporally and spatially
closest atmospheric profiles and ocean skin temperatures given by
ERA-Interim (ERA-I) reanalyses generated at the European Centre for Medium-Range Weather Forecast (ECMWF).
We have chosen to use outputs from
reanalyses over outputs based on radiosondes measurements (e.g., the LMD
Analyzed RadioSoundings Archive, ARSA, database)
because of the low density
of the radiosonde network over sea. ERA-I reanalyses are available every 6 h with a nominal resolution of 0.75∘ in latitude and
longitude. The 4A/OP-simulated radiances are computed for each clear pixel
found at a distance smaller than 5 km from the closest ERA-I input to ensure
the highest possible coherence for the comparisons with the observations.
This 5 km threshold was chosen by taking into account the specificity of
each of the three instruments (IIR, MODIS, and SEVIRI). The reanalyses outputs
give a 61-level description of the temperature, water vapor, and ozone
profiles as well as the skin temperature. A comprehensive documentation of
the current ERA-I reanalysis system used in this study may be found in Dee
et al. (2011). For other absorbers with a constant pressure-dependent mixing
ratio (CO2, N2O, CO, HNO3, SO2, CFCs, etc.), the most
plausible mixing ratio value is used.
The MODIS and SEVIRI viewing angles are outputs of the collocation step
(Sect. 4.1), and the IIR is considered a nadir viewing instrument.
Another essential variable for the simulation of the brightness temperatures
of these nine window channels is the ocean surface emissivity. As shown for
a long time, in many publications, the emissivity depends on wind speed,
polarization, temperature, emission angle, and wavenumber (Masuda et al.,
1988; Wu and Smith, 1996; Brown and Minnett, 1999; Hanafin and Minnett,
2005; Niclòs et al., 2007). In the present study, variations with wind
speed or polarization are not taken into account. Indeed, we chose to favor
the consistency with the prelaunch simulations so ocean surface
emissivity has been set to 0.98 for all the channels. Again, because MODIS
viewing angles are always smaller than 20∘ and SEVIRI-selected
angles are intentionally limited to 10∘, the emissivity
dependence on satellite viewing angles is neglected. It is worth pointing out
that problems requiring the highest possible absolute accuracy, such as the
retrieval of geophysical variables or the validation of radiative transfer
models, could not be approached with such approximations. Here, we are more
interested in comparing the behavior of the companion channels for each
pair of channels than in comparing the pairs. Because each companion channel
of each pair is processed under the same conditions, using this constant
value of the emissivity, there would be a negligible effect on their
relative behavior. However, in the planned future reprocessing of the data,
and since no limitation comes from the 4A/OP model itself, the required
dependencies will be taken into account, in detail, whenever the information
is available.
Outputs and statistical analyses
The stand-alone approach generates, for each channel, differences between
the 4A simulation and the clear sky observation, hereafter called
“residuals”. For this study, the 4A simulations have been processed for 10 days of each of the chosen months. Outputs are “monthly” mean residuals
and associated standard deviations, with daytime and nighttime data either
combined or separated. Statistics are built monthly instead of daily, as
done in the relative approach, because the number of samples is smaller due
to the severe collocation constraints described above. Final statistics are
given after removing individual residuals found outside the initial monthly
mean ± twice the initial standard deviation. This procedure is done to
prevent undetected cloudy pixels to enter the statistics as well as for
situations for which the instruments presumably do not see the same scenes.
Mean number of pixels per day (bold) and mean standard deviations
in Kelvin (from left to right: x= IIR1–MODIS29, y= IIR2–MODIS31, and
z= IIR3–MODIS32) associated with Figs. 3 to 7.
IIR–MODIS brightness temperature differences in Kelvin at the
beginning of the CALIPSO mission and associated uncertainty at the warmest
temperature range in each latitude band.
Latitudes and temperaturesIIR1–MODIS29IIR2–MODIS31IIR3–MODIS3230∘ S–30∘ N, 290–300 K0.336±0.0010.511 ± 0.002-0.736 ± 0.00160–30∘ S, 280–290 K0.176 ± 0.0010.228 ± 0.001–0.469 ± 0.00130–60∘ N, 280–290 K0.170 ± 0.0020.304 ± 0.003-0.445 ± 0.00382–60∘ S, 270–280 K0.121 ± 0.0020.255 ± 0.002-0.113 ± 0.00260–82∘ N, 270–280 K0.159 ± 0.0030.484 ± 0.0040.055±0.003Results and findings from the relative approach
Results from the relative approach are presented hereafter in terms of time
series of daily averaged (day and night combined) IIR–MODIS and IIR–SEVIRI
BTDs between mid-June 2006 and the end of December 2015. The findings derived
from the analysis of the various figures are then discussed.
Time series (x axis: year-month) of daily averaged (day and night
combined) IIR–MODIS brightness temperature differences (y-axis units:
Kelvin) for the three pairs of companion channels (red: IIR1–MODIS29, green:
IIR2–MODIS31, blue: IIR3–MODIS32) over ocean in the tropics at 30∘ S–30∘ N.
Each panel is for a given range in brightness temperature
from 290–300 K (top) down to 200–210 K (bottom). Added at the top of each
panel are the mean number of points per day (Nb pts) and the mean standard deviation
per day for each of the three pairs (see Table 5).
Results
Time series of IIR–MODIS BTD are shown in Figs. 3 to 7 for the five latitude
bands, namely 30∘ S–30∘ N (Fig. 3), 60–30∘ S (Fig. 4), 30–60∘ N
(Fig. 5),
82–60∘ S (Fig. 6), and 60–82∘ N (Fig. 7). Each of these figures includes several panels corresponding to
10 K brightness temperature domains (decreasing from top to bottom)
typically found in their respective latitude bands, and each panel shows the
BTD for the three pairs of channels: IIR1–MODIS29 (red), IIR2–MODIS31
(green), and IIR3–MODIS32 (blue). The mean number of pixels per day used to
build the statistics and the mean standard deviations for each pair of
channels are shown at the top of each panel and also in Table 5 for more
clarity. The mean number of daily pixels is always larger than 5 × 103
and up to 3.7 × 106 in the tropics at 290–300 K. For each latitude band,
the smallest standard deviations are found at the warmest temperatures, with
standard deviations ranging from 0.44 to 0.66 K. Standard deviations
increase up to 1.1 K in the tropics at the coldest temperatures, generally
associated with increased instrument noise, but perhaps also due in part to
larger inhomogeneity of cloudy scenes and to parallax effects at larger
MODIS viewing angles. The large daily variability seen at mid- and high
latitudes at the coldest temperatures is also attributed to the smaller
number of samples (see Table 5). Overall, the results show very stable
IIR–MODIS BTD since the CALIPSO launch, with some seasonal variations noted,
but with a remarkable year-by-year repeatability. These features will be
discussed in more detail in Sect. 5.2. It is confirmed that the switch from
0.3 to 3∘ of the CALIPSO platform pitch angle at the
end of November 2007 (see Sect. 2) has no significant impact, because no
discontinuity in the time series can be evidenced.
Time series (x axis: year-month) of daily averaged (day and night
combined) IIR–MODIS brightness temperature differences (y-axis units:
Kelvin) for the three pairs of companion channels (red: IIR1–MODIS29, green:
IIR2–MODIS31, blue: IIR3–MODIS32) over ocean at 60–30∘ S.
Each panel is for a given range in brightness temperature
from 280–290 K (top) down to 210–220 K (bottom). Added at the top of each
panel are the mean number of points per day (Nb pts) and the mean standard deviation
per day for each of the three pairs (see Table 5).
Time series of IIR–SEVIRI BTD are shown in Fig. 8 for comparison with the
IIR–MODIS time series. As explained in Sect. 3.1, only SEVIRI viewing angles
smaller than 10∘ are chosen. Consequently, the comparisons are
only between about 10∘ W and 10∘ E in longitude and
between 10∘ S and 10∘ N in latitude. The temperature
range is 290–300 K. The mean number of samples per day (5 × 104) is about
100 times smaller than for the IIR–MODIS comparisons. Moreover, the day-to-day
variability is more important and the standard deviations are slightly
larger (between 0.49 and 0.65 K). The black and grey arrows point to
discontinuities in the time series. The discontinuity of up to 0.4 K in May
2008 (black arrows) is explained by the change of definition in the SEVIRI
1.5 image product from spectral to effective blackbody radiances on 7 May
2008. For simplicity, this change is not accounted for in this analysis,
which assumes effective blackbody radiances. This discontinuity was already
evidenced in the initial analyses reported in Scott (2009). In addition,
discontinuities of smaller amplitude (grey arrows) are seen in April 2007,
which correspond to the switch from Meteosat 8 to Meteosat 9, and in
January 2013, which coincide with the switch to Meteosat 10. These small
discontinuities are explained by the fact that the SEVIRI brightness
temperatures are computed using the Meteosat 8 ISRFs for the entire period.
The discontinuities in the time series illustrate the sensitivity of the
technique to detect instrumental changes.
Same as Fig. 4 but at 30–60∘ N.
Findings
As seen in Sect. 5.1, considering the fact that the IIR and MODIS/Aqua both fly in the
A-Train, and considering no instrumental changes since CALIPSO launched,
monitoring differences between IIR and MODIS/Aqua observations turns out to
be a more fruitful approach for the assessment of the IIR calibration
stability than monitoring differences between IIR and SEVIRI. Thus, the
findings discussed in the following sections are based mostly on the
IIR–MODIS comparisons shown in Figs. 3 to 7. In this section, we discuss
first the consistency of the IIR–MODIS and IIR–SEVIRI BTD at warm
temperatures with our prelaunch evaluation from the five TIGR air mass
types. Then, we successively discuss the IIR–MODIS results at cold
temperatures, the long-term trends, and the seasonal variations.
Time series (x axis: year-month) of daily averaged (day and night
combined) IIR–MODIS brightness temperature differences (y-axis units:
Kelvin) for the three pairs of companion channels (red: IIR1–MODIS29, green:
IIR2–MODIS31, blue: IIR3–MODIS32) over ocean at 82–60∘ S.
Each panel is for a given range in brightness temperature
from 270–280 K (top) down to 220–230 K (bottom). Added at the top of each
panel are the mean number of points per day (Nb pts) and the mean standard deviation
per day for each of the three pairs (see Table 5).
Warm scenes
The first step of the analysis is to compare the mean BTD from the relative
approach with the simulated TIGR_BTDs (see Sect. 3.2.3).
Because the TIGR simulations are for clear sky conditions, the comparisons
are conducted for the warmest temperature range at each latitude band.
Indeed, the clear sky scenes are a priori the warmest ones, although the
warmest scenes could also contain clouds of weak absorption or thicker
clouds located near the surface. After application of the
(TIGR_BTD ± 2.1 K) thresholds introduced in Sect. 4.2,
more than 95 % of the pixels contribute to the statistics, and the mean
BTDs are changed by less than 0.15 K, confirming that the thresholding method
is appropriate. The IIR–MODIS BTDs at the beginning of the mission derived
from linear regression lines are reported in Table 6 for comparison against
the TIGR_BTDs reported in Table 3. The observed IIR–MODIS BTDs
in the tropics at 290–300 K and the TIGR_BTDs for tropical
air mass types differ by less than 0.1 K for IIR1–MODIS29, 0.25 K for
IIR2–MODIS31, and 0.3 K for IIR3–MODIS32. The observed mean BTD at
30–60∘ and at 280–290 K in the Northern and Southern Hemispheres and the TIGR_BTDs at mid-latitude (mid-lat1 and
mid-lat2) also agree within about 0.1 K for IIR1–MODIS29 and IIR3–MODIS32
and are within 0.2 to 0.3K for IIR2–MODIS31. The same conclusions apply for
the mean BTD at 60–82∘ S and at 270–280 K when
compared with the TIGR_BTDs for polar1 and polar2
atmospheres. At 60–82∘ N, IIR2–MODIS31 and
IIR3–MODIS32 BTDs are larger than at 60–82∘ S by about
0.2 K, which degrades the comparisons against the TIGR_BTDs.
Overall, these results demonstrate good consistency between observed
IIR–MODIS BTDs and simulated TIGR_BTDs. Direct comparisons
between observations and simulations in clear sky conditions will be
discussed in Sect. 6 with the stand-alone approach.
Same as Fig. 6 but at 60–82∘ N.
Even though the following is based on comparisons with MODIS/Aqua, it is
interesting to compare the observed IIR–SEVIRI BTDs and the
TIGR_BTDs in the tropics (Table 4). After May 2008, when the
radiances reported in the SEVIRI products are effective blackbody radiances,
the TIGR_BTDs are in fair agreement with the differences
plotted in Fig. 8, keeping in mind that the SEVIRI observations are from
Meteosat 9 and 10 after May 2008, whereas the TIGR simulations are for
SEVIRI/Meteosat 8.
Time series (x axis: year-month) of IIR–SEVIRI brightness
temperature differences (y-axis units: Kelvin) for the three pairs of companion
channels (red: IIR1–SEVIRI8.7, green: IIR2–SEVIRI10.8, blue: IIR3–SEVIRI12)
over ocean at 290–300 K for SEVIRI viewing angles smaller than 10∘.
Added at the top of each panel are the mean number of points per day (Nb pts) and
the mean standard deviation per day for each of the three pairs. The black
arrows indicate the change in the definition of the SEVIRI product in May
2008. The grey arrows indicate the switch from Meteosat 8 to 9 in April 2007
and from Meteosat 9 to 10 in January 2013.
Cold scenes
As the scene temperature decreases, the clouds are denser and colder, and the
contribution from such absorbing clouds increases while the influence of the
surface and near-surface atmosphere decreases. The fraction of pixels
retained after application of the thresholding scheme described in Sect. 4.2
is found to decrease progressively from 95 % for the warm scenes to
30 %, with the smallest fraction observed at 200–210 K in the tropics for the
IIR3–MODIS32 pair. This is partly due to the fact that the BTDs are
distributed over a broader range of values than anticipated, so that the
mean BTD seen in Figs. 3 to 7 may be significantly, but systematically,
biased for the cold scenes. For a better quantification as temperature
decreases, IIR–MODIS BTDs are evaluated using the median values of the whole
distributions, without thresholding. The median value is preferred to the
mean value to minimize the impact of presumably unrealistic values. Median
IIR1–MODIS29 (red), IIR2–MODIS31 (green), and IIR3–MODIS32 (blue) BTDs are
shown in Fig. 9a by temperature ranges from 190–200 to 280–290 K in the
tropics during 2008 for representative months of the four seasons. Mean
absolute deviations from the median value are between 2.5 and 5 K. For
further evaluation, IIR and MODIS inter-channel BTDs have been analyzed.
Following the same approach as in Fig. 9a, Fig. 9b shows median IIR1–IIR3
(red, solid), IIR2–IIR3 (green, solid), MODIS29–MODIS32 (red, dashed), and
MODIS31–MODIS32 (green, dashed) BTDs. Mean absolute deviations from the
median value are between 0.5 and 3 K. The variations of both IIR and MODIS
inter-channel BTDs with temperature are due to the changing optical and
microphysical properties of absorbing ice and water clouds located at
various altitudes. The analysis of arches as seen in Fig. 9b is the essence
of the well-known split-window technique for the retrieval of cloud
microphysical properties (Inoue, 1985; Ackerman et al., 1990). IIR and MODIS
arches are not of the same amplitude because IIR and MODIS measurements are
spectrally different. The BTDs at the coldest temperatures (190–200 K)
provide useful information regarding the calibration. Indeed, the coldest
temperatures (190–200 K) correspond a priori to elevated dense ice clouds, which, if
they behave as blackbody sources, should lead to quasi-identical brightness
temperatures for all channels, assuming a negligible contribution from the
atmosphere above the cloud. This is what we observe in Fig. 9b, where the
IIR and MODIS inter-channel BTDs are close to zero, showing internal
consistency of the calibration within each instrument. However, Fig. 9a
shows that the IIR–MODIS BTDs are about 1.6 K on average at 190–200 K for the
three pairs of channels. This indicates a warm bias of 1.6 K of IIR with
respect to MODIS at 190–200 K. According to Fig. 9a, the warm bias seems to
increase progressively as temperature decreases. An increasing IIR
calibration bias as temperature decreases could be explained by a drift of
the gain with respect to the gain measured in flight at warm temperature.
Because IIR has only one sensor, observing such a similar bias for all three
channels is conceivable.
(a) Median IIR1–MODIS29 (red), IIR2–MODIS31 (green), and
IIR3–MODIS32 (blue) brightness temperature differences against brightness
temperature. (b) Median IIR1–IIR3 (red, solid), IIR2–IIR3 (green, solid),
MODIS29–MODIS32 (red, dashed), and MODIS31–MODIS32 (green, dashed)
brightness temperature differences against temperature. Plus sign: January
2008, star: April 2008, diamond: July 2008, triangle: October 2008. Latitude
band: 30∘ S–30∘ N, ocean.
Long-term trends
IIR–MODIS BTDs are very stable year-by-year since mid-June 2006. In order to
quantify the trends over the first 9.5 years of the CALIPSO mission, linear
regression lines have been computed for each of the time series shown in
Figs. 3 to 7. The slopes of these lines well approximate the trend of the
IIR–MODIS brightness temperature differences since the beginning of the
CALIPSO mission. These trends are plotted against temperature in Fig. 10 for
each pair of channels and for each of the five latitude bands. An
unambiguous trend is seen for IIR1–MODIS29 (red) at any temperature and at
any latitude, varying between -0.01 and -0.03 K yr-1. It is -0.019 ± 0.0002 K yr-1
at 290–300 K in the tropics and -0.02 ± 0.0004 K yr-1
on average. This trend, which represents -0.19 K over the 9.5-year period,
can also be seen directly in Figs. 3 to 7. However, the trend of the order
of 0.005 K yr-1 or less in absolute value for IIR2–MODIS31 (green) and
IIR3–MODIS32 (blue) is deemed not significant. Notwithstanding the small
trend evidenced for IIR1–MODIS29, which is further investigated in Sect. 6
using the stand-alone approach, the long-term stability of the IIR
instrument with respect to MODIS/Aqua between June 2006 and the end of 2015
is remarkable.
Trends of IIR–MODIS brightness temperature differences and
associated uncertainties against temperature for the three pairs of companion
channels (red: IIR1–MODIS29, green: IIR2–MODIS31, blue: IIR3–MODIS32) as
derived from Figs. 3 to 7. Latitude bands: diamond, 30∘ S–30∘ N; square, 60–30∘ S;
circle,
30–60∘ N; inverse triangle, 82–60∘ S; triangle, 60–82∘ N.
Time series (x axis: year-month) of IIR–MODIS brightness
temperature differences (y-axis units: Kelvin) for the three pairs of companion
channels (a IIR1–MODIS29, b IIR2–MODIS31, c IIR3–MODIS32)
over ocean at 30–60∘ N and 280–290 K. Red: day only,
blue: night only, black: day and night.
Seasonal variations
Seasonal variations of the IIR–MODIS BTD are sometimes observed at mid- and
high latitude in Figs. 4 to 7. More specifically, it can be noted by
comparing Figs. 4 and 5 on one hand (mid-latitudes) and Figs. 6 and 7 on the
other hand (polar latitudes) that at any scene temperature, including the
warmest temperatures, with presumably the smallest influence from clouds, a
seasonal variability is clearly seen in the Northern Hemisphere but barely
in the Southern Hemisphere. It was found that at mid-latitudes, where
observations during both daytime and nighttime are available year-round, the
larger seasonal variability in the Northern Hemisphere is related to
significant differences between nighttime and daytime IIR–MODIS BTDs. This is
illustrated in Figs. 11 and 12, where the IIR–MODIS BTDs are shown for each
pair of channels at 30–60∘ N (Fig. 11) and at
60–30∘ S (Fig. 12), at 280–290 K, and by
distinguishing daytime data (in red) from nighttime data (in blue). The BTDs
obtained by combining daytime and nighttime data, as in Figs. 4 and 5, are
plotted in black for reference. At 30–60∘ N (Fig. 11), seasonal night/day biases are seen for the three pairs of IIR–MODIS
channels, whereas no night/day biases are seen in the Southern Hemisphere at
60–30∘ S (Fig. 12). The largest bias at 30–60∘ N is during June and July, with a night-minus-day difference
equal to +0.4 K for IIR2–MODIS31 (middle) and IIR3–MODIS32 (bottom) and
equal to about +0.2 K for IIR1–MODIS29 (top). In the opposite season, no
night/day bias is seen for IIR1–MODIS29, whereas the night–day difference is
about -0.1 K for the other pairs. This behavior is further discussed in
Sect. 6 after the presentation of additional information from the
stand-alone approach.
Results and further findings from the stand-alone approach
The stand-alone approach has been applied to each of the three IIR channels
and each of their three MODIS and SEVIRI companion channels to directly
compare clear sky simulations and clear sky measurements. Here, results are
shown for IIR and MODIS only, for each month of January and July from
mid-2006 to the end 2015, with the corrected clear sky mask processed as
described in Sect. 4.3.1. From the relative approach (Sect. 5.2.3), a trend
of -0.02 K yr-1 on average is detected for IIR1–MODIS29, which could
originate from one channel or from both. The stand-alone approach allows the
asserting of which channel deviates from the other. Similarly, the night/day
biases evidenced for each pair of channels in the Northern Hemisphere at
30–60∘ (Sect. 5.2.4) are investigated.
Same as Fig. 11, but at 60–30∘ S.
Time series (x axis: year-month) of residuals (y-axis units:
Kelvin) for the three pairs of IIR and MODIS channels (a IIR1 and MODIS29 in
red, b IIR2 and MODIS31 in green, c IIR3 and MODIS32 in blue,
with IIR in full circles and MODIS in open circles) over ocean in the tropics at
30∘ S–30∘ N, day and night. Superimposed are linear
regressions lines with temporal origin at the beginning of the mission.
Slopes (in K yr-1) and intercepts (in K) are given on each panel.
Results
The fraction of clear sky IIR pixels over ocean is the largest in the
tropics at 30∘ S–30∘ N, with 20 % of the ocean pixels
on average at nighttime and 25 % daytime.
The slightly larger daytime
fraction could be in part due to the smaller signal to noise ratio of the
lidar signal and therefore to a reduced ability to detect clouds. Figure 13
shows the mean monthly residuals obtained over ocean in the tropics for IIR1
and MODIS29 (top), IIR2 and MODIS31 (middle), and IIR3 and MODIS32 (bottom)
night and day. Each monthly value is obtained from typically 4 × 103
simulations. Linear regression lines with temporal origin at the beginning
of the mission are also plotted, with slopes and intercepts given on each
panel. The residuals are found between 0.2 and 0.6 K, which is deemed
reasonable, keeping in mind that they are sensitive to the auxiliary data
(including the clear sky mask) and that the surface emissivity is made
constant and equal to 0.98 for all channels. The standard deviations are
found between 0.4 and 0.6 K for the IIR channels and between 0.5 and 0.7 K
for the MODIS channels. Values from IIR and MODIS are comparable, showing
the importance of uncertainties in ancillary inputs as compared to the
instrumental noise. Using the Version 3 non-corrected IIR clear sky mask
leads to significantly larger standard deviations, up to 1.2 K. Moreover,
these residuals are larger by about 0.5 K, with season- and
latitude-dependent biases, which is fully consistent with the presence of
unwanted clouds and therefore too-cold observations in these supposedly
clear sky data samples.
The IIR1 (0.412 K) and MODIS29 (0.392 K) residuals shown in Fig. 13 differ by
only 0.02 K at the beginning of the mission, and IIR2 (0.208 K) and MODIS31
(0.249 K) residuals differ by only 0.04 K. This indicates that for these
pairs, the differences between the observations are well reproduced by the
simulations, suggesting an excellent accuracy of the IIR calibration.
However, the IIR3 residuals (0.324 K) are smaller than the MODIS32 residuals
(0.579 K) by -0.26 K. Because residuals are differences between simulations
and observations, this means that the simulated IIR3–MODIS32 differences are
smaller than the observed differences by -0.26 K.
Further findingsIIR1–MODIS29 trend
Similar temporal variations of the monthly residuals in Fig. 13, of the
order of less than 0.1 K, are seen for all the channels of the two
instruments, indicating that they originate from the simulations rather than
from the observations. The slope of the MODIS29 residuals, -0.019 K yr-1, is
much larger than that of IIR1, 0.0017 K yr-1, meaning that MODIS29
observations have increased with respect to the simulations at a rate of
+0.019 K yr-1, whereas IIR1 ones have barely changed. This is in perfect
agreement with the decrease of IIR1–MODIS29 BTD at a rate of -0.019 K yr-1
seen in the relative approach at 290–300 K in the tropics. For the
IIR2–MODIS31 and IIR3–MODIS32 pairs, the absence of a detectable trend in
the relative approach suggests that none of these four channels has been
drifting. As expected, the slopes of the four residuals are quasi-identical
and do not exceed -0.0037 K yr-1. Overall, this indicates that the much
larger slope of the MODIS29 residuals is driven by MODIS observations and
not by the simulations. In conclusion, none of the IIR channels exhibit a
detectable trend, whereas MODIS29 exhibits a positive trend of about
+0.019 K yr-1 since the beginning of the CALIPSO mission. It is recalled
that MODIS C5 products are used for this analysis, so this assessment
may not be applicable to the most recent Collection 6 (C6).
IIR–MODIS night/day bias at 30–60∘ N
A night/day bias has been evidenced for each pair of IIR–MODIS observations
in the Northern Hemisphere at 30–60∘, which varies
seasonally and has its maximum amplitude in June and July (see Sect. 5.2.4 and
Fig. 11). For further assessment, Fig. 14 shows the IIR and MODIS residuals
for the three pairs of channels against latitude for the month of July 2007
by separating nighttime (top) and daytime (bottom) clear sky observations.
Again, the results have to be interpreted in a relative sense. At night
(top) and south of 25∘ N, the IIR1 and MODIS29 residuals (red
curves) and the IIR3 and MODIS32 residuals (blue curves) exhibit
quasi-identical latitudinal variations, which are therefore attributed to
the simulations. IIR2 and MODIS31 residuals (green curves) are very close
from 35∘ S to 25∘ N and slightly depart from each other
by up to 0.2 K south of 35∘ S. Similar results are obtained during
daytime (bottom) south of 25∘ N. At night, from 25 to
45∘ N, the three MODIS residuals (open circles) and the IIR1
residuals (red, full circle) have similar latitudinal variations, whereas
IIR2 (green, full circle) and IIR3 (blue, full circle) residuals
unambiguously decrease more rapidly than the others, by about 0.5 K.
However, during daytime, no distinct behavior of the IIR2 and IIR3
residuals is seen between 25 and 45∘ N. The sudden
decrease from 25 to 45∘ N seen for the IIR2
(respectively IIR3) nighttime residual, but not for the residual of its
companion channel MODIS31 (respectively MODIS 32), indicates that this
phenomenon originates from the observations. Furthermore, the fact that this
sudden decrease is seen only for the IIR2 and IIR3 residuals and is seen at
night but not during the day strongly suggests a calibration bias in IIR2
and IIR3 observations at night. A sudden decrease of the residuals means a
sudden increase of the IIR2 and IIR3 brightness temperatures, by up to 0.5 K
from about 30 to 45∘ N. Thus, the stand-alone
approach shows that the night-minus-day differences of +0.4 K seen for
IIR2–MODIS31 and IIR3–MODIS32 in June and July in the Northern Hemisphere at
30–60∘ (Fig. 11) using the relative approach are due
to the fact that IIR2 and IIR3 channels are biased and too warm at night. It
is recognized that the smaller night–day differences seen for IIR1–MODIS29
(+0.2 K) in Fig. 11 are not clearly evidenced from the stand-alone
approach. No issue is evidenced in the Southern Hemisphere and south of
25∘ N from the stand-alone approach, which is consistent with the
fact that nighttime and daytime IIR–MODIS BTDs at 60–30∘ in the Southern Hemisphere are nearly identical (Fig. 12).
Residuals against latitude in July 2007 for the three pairs of IIR
and MODIS channels (IIR1 and MODIS29: red, IIR2 and MODIS31: green, IIR3 and
MODIS32: blue, IIR: full circles, MODIS: open circles). (a) Night. (b) Day.
It is noted that south of 25∘ N, all nighttime residuals tend to
be larger than daytime ones, by about 0.2 K. However, differences due to the
clear sky mask are expected to lead to larger daytime residuals because of
the a priori larger probability for the lidar to miss clouds during daytime.
Thus, even though the clear sky mask may partly explain these differences,
it is likely not the only contributor. Nevertheless, these small differences
do not impact the previous discussion.
The IIR calibration biases evidenced at mid-latitude in the Northern Hemisphere are being investigated in collaboration with CNES. Our current
understanding is that the rapidly changing thermal environment of the
instrument at the end of the daytime portion and at the beginning of the
nighttime portion of the orbits in the Northern Hemisphere would not be
perfectly accounted for through the blackbody source used for the in-flight
calibration. The flaw is synchronized with the elapsed time since the
night-to-day transition along the orbit. Because the latitude of the
night-to-day transition depends on the season, the flaw appears at
season-dependent latitudes but always in the Northern Hemisphere (T. Trémas, personal communication, 2012),
which in turn explains the observed
seasonal variations at fixed latitudes (30–60∘ N).
These calibration biases are expected to impact the IIR1–IIR3 inter-channel
BTD by less than 0.2 K on average and to have no significant impact on
IIR2–IIR3 on average.
Summary and conclusions
An assessment of the IIR calibration after 9.5 years of nearly continuous
operation has been presented. IIR channels IIR1 (8.65 µm), IIR2 (10.6 µm), and IIR3 (12.05 µm)
have been primarily compared against
MODIS/Aqua C5 companion channels MODIS29, MODIS31, and MODIS32,
respectively, both on the A-Train with no instrumental changes since the
CALIPSO launch. The choice of the companion instruments and channels was
based on criteria such as spectral range and BTD between IIR and the
candidate channels, quality and frequency of the spatial and temporal
coincidences with the IIR 69 km swath, and spatial resolution. The BTD
between IIR and its companion channels had been evaluated before launch
using the 4A radiative transfer model and five air mass types determined
from the TIGR climatic data base (TIGR_BTD, Table 3). The
simulation, collocation, and statistical tools have also been developed to
perform comparisons with SEVIRI companion channels SEVIRI8.7, SEVIRI10.8,
and SEVIRI12.
Two complementary approaches have been applied, which aim at characterizing
deviations between the pairs of companion channels as well as the behavior
of each individual channel both qualitatively and quantitatively. The
relative approach is based on statistical analyses of BTD between IIR and
the relevant companion channels in controlled conditions, over a wide range
of brightness temperatures and over ocean. This approach uses only
calibrated and geolocated radiances and does not require additional
information from the lidar, such as the clear sky mask. The stand-alone
approach is based on direct comparisons between simulations and observations
(residuals) in clear sky conditions, using the 4A/OP model and inputs from
time–space collocated ERA-Interim reanalysis products. The clear sky mask is
derived from the Version 3 IIR Level 2 swath product and has been corrected
for this study to account for additional clouds detected by CALIOP at 1/3 km
resolution but not accounted for in the current IIR operational algorithm.
Overall, a remarkable stability of IIR with respect to MODIS companion
channels is seen in the Southern Hemisphere and in the tropics since launch
using the relative approach. No long-term trend could be detected for the
IIR channels, MODIS31, and MODIS32. However, a trend of -0.02 K yr-1 on
average is seen for IIR1–MODIS29 at any latitude. The complementary
stand-alone approach showed that it originates from a long-term positive
trend of MODIS29. A seasonal and systematic nighttime IIR calibration bias
has been evidenced at mid-latitude in the Northern Hemisphere. It was first
detected using the relative approach through surprising differences between
nighttime and daytime IIR–MODIS BTDs and was further assessed by comparing
latitudinal variations of MODIS and IIR residuals of the stand-alone
approach for the month of July 2007. The worst bias at 30–60∘ N is in June and July, where IIR2 and IIR3 night brightness
temperatures are on average too large by 0.4 K, and the IIR1 temperatures
are too large by 0.2 K. IIR calibration with respect to MODIS has been
assessed by comparing the residuals of the stand-alone approach for each
pair of companion channels. In the tropics, IIR1 and MODIS29 residuals
differ by less than 0.02 K, and IIR2 and MODIS31 ones are within 0.04 K.
However, IIR3–MODIS32 BTDs are larger than simulated by 0.26 K. This is
deemed again a remarkable agreement when compared to the specifications for
the IIR instrument (accuracy better than 1 K). At the coldest temperatures
(190–200 K), for which similar brightness temperatures are expected, each
IIR channel is unambiguously warmer than its MODIS companion channel, by
about 1.6 K, whereas both IIR and MODIS inter-channel BTDs are close to zero,
showing internal consistency within each instrument. This could be explained
by a systematic bias in the IIR calibration at very cold temperatures.
Initial comparisons between MODIS C5, used for this analysis, and the most
recent C6 for several months in 2008 show little change at warm
temperatures. However, MODIS31 and MODIS32 are colder in C6 than in C5 by at
least 1 K at 190–200 K, so the internal consistency between the three
MODIS channels is not seen in C6 at the coldest temperatures. A more
detailed assessment for cloudy scenes will be conducted in the future using
the description of the cloud vertical structure provided by CALIOP
measurements and cloud microphysical models, following the same approach as
in the IIR Level 2 algorithm (Garnier et al., 2012, 2013). The analysis will
be carried out using both MODIS C5 and C6.
Overall, IIR on-orbit calibration over 9.5 years is excellent and very
stable in the Southern Hemisphere and in the tropics, within specifications,
showing no sign of instrumental aging. Corrections for the residual
biases identified in the Northern Hemisphere are being developed in
collaboration with CNES for implementation in a future version of the IIR
Level 1B products. The monitoring of the IIR instrument will be continued in
collaboration with AERIS/ICARE, will be updated as new versions of the IIR
products become available, and will use MODIS C6. The stand-alone approach
will be completed for every month since the CALIPSO launch using the corrected
mask, which will be implemented in the next version (4) of the IIR Level 2
products. The clear sky simulations will be refined to include more accurate
estimates of ocean surface emissivity.
It is believed that this assessment reinforces the value of the collocated
IIR and CALIOP data record from the CALIPSO mission, which has now reached
almost 11 years in orbit as part of the A-Train. This work is part of the
efforts made in the international research community and space agencies
(approximately 15 contributors in GSICS for these latter ones) to provide
improved and consistent level 1 and level 2 products from various
space-borne instruments for atmospheric science or climate monitoring.
Post-processed satellite data are available upon request from the authors.
The authors declare that they have no conflict of interest.
Acknowledgements
The authors are deeply grateful to CNES, the NASA Langley Research Center, and
SSAI (Science Systems and Applications, Inc.) for their support. This work benefited from the support of the Centre
National de la Recherche Scientifique (CNRS) and of Institut National des
Sciences de l'Univers (INSU).
Numerous people have contributed to this work over the years. The experience
gained at the occasion of the NOAA/NASA Pathfinder Program and related
research since then has paved the way for this study. Our warmest thanks go to
Alain Chédin and colleagues from LMD for stimulating discussions at all
the stages of this work. We thank Thérèse Barroso and
Pascale Ferrage, former and current CNES CALIPSO mission coordinators, for
their helpful discussions about the IIR instrument and the CALIPSO team for
their encouragement. We thank Jacques Descloitres, Jean-Marc Nicolas, and
Fabrice Ducos from AERIS/ICARE for their assistance and Olivier Chomette
(LMD) for his contribution at the early stage of this work.
We are thankful to Didier Renaut, Denis Blumstein, and Denis Jouglet from
CNES for giving us the opportunity to present our results at the occasion of
GSICS Meetings.
CALIPSO data are processed and available at the NASA Langley Research
Center and are also available at the AERIS/ICARE data center. The REMAP
product is processed and available at AERIS/ICARE. For the post-processing
of the satellite data and for the archiving, we also benefited from the
large facilities of the Institute for Development and Resources in Intensive
Scientific computing (IDRIS) of CNRS and of the Ensemble de Services Pour la
Recherche à l'IPSL (ESPRI)/AERIS data and computing center at IPSL. The authors are thankful to ECMWF
for making the ERA-I outputs available through the ECMWF Data Server.
The authors thank the anonymous reviewers for their constructive
and helpful comments.
Edited by: B. Kahn
Reviewed by: three anonymous referees
References
Achard, V.: Trois problèmes de l'analyse 3D de la structure
thermodynamique de l'atmosphère par satellite: mesure du contenu en
ozone; classification des masses d'air; modélisation “hyper-rapide” du
transfert radiatif, Thèse de doctorat en Terre, océan, espace, Paris
7 University, Paris, 1991.Ackerman, S. A., Smith, W. L., Spinhirne, J. D., and Revercomb, H. E.: The
27–28 October 1986 FIRE IFO cirrus case study: Spectral properties of
cirrus clouds in the 8–12 µm window, Mon. Weather Rev., 118, 2377–2388, 1990.Anthony Vincent, R. and Dudhia, A.: Fast radiative transfer using
monochromatic look-up tables, J. Quant. Spectrosc. Ra.,
186, 254–264, 10.1016/j.jqsrt.2016.04.011, 2017.Armante, A., Scott, N. A., Crevoisier, C., Capelle, V., Crépeau, L.,
Jacquinet, N., and Chédin, A.: Evaluation of spectroscopic databases
through radiative transfer simulations compared to observations. Application
to the validation of GEISA 2015 with IASI and TCCON, J. Mol. Spectrosc.,
327, 180–192, 10.1016/j.jms.2016.04.004, 2016.Bériot, N., Scott, N. A, Chédin, A., and Sitbon, P.: Calibration of
geostationary-satellite infrared radiometers using the Tiros-N vertical
sounder: application to Meteosat-1, J. Appl. Meteor., 21,
84–89, 10.1175/1520-0450(1982)021<0084:COGSIR>2.0.CO;2, 1982.
Brown, O. B. and Minnett, P. J.: MODIS infrared sea surface temperature
algorithm – Algorithm Theoretical Basis Document. Products: MOD28, ATBD
Reference Number: ATBD-MOD-25, 1999.
Chédin, A., Scott, N. A., Wahiche, C., and Moulinier, P.: The improved
initialization inversion method: a high-resolution physical method for
temperature retrievals from satellites of the TIROS-N series, J. Clim. Appl. Meteorol., 24, 128–143, 1985.
Chédin, A., Fischer, H., Kunzi, K., Spankuch, D., and Scott, N. A.: ITRA (Intercomparison of Transmittance and
Radiance Algorithms) campaigns and workshops. A report of the International
Radiation Commission Joint Meeting ITRA- ICRCCM, University of Maryland,
March 1986, 1988.Chédin, A., Scott, N. A., Claud, C., Bonnet, B., Escobar-Munoz, J.,
Dardaillon, S., Cheruy, F., and Husson, N.: Global scale observation of the
Earth for climate studies, Adv. Space Res., 14, 155–159,
10.1016/0273-1177(94)90364-6, 1994.
Chéruy, F., Scott, N. A., Armante, R., Tournier, B., and Chédin, A.:
Contribution to the development of radiative transfer models for high
spectral resolution observations in the infrared, J. Quant. Spectrosc. Ra., 53, 597–611, 1995.
Chevallier, F., Chéruy, F., Scott, N. A., and Chédin, A.: A neural
network approach for a fast and accurate computation of longwave radiative
budget, J. Appl. Meteorol., 37, 1385–1397, 1998.Corlay, G., Arnolfo, M.-C. Bret-Dibat, T., Lifermann, A., and Pelon, J.: The
Infrared Imaging Radiometer for PICASSO-CENA, CNES Tech. Doc., 14 pp.,
available at:
https://calipso.cnes.fr/sites/default/files/migration/smsc/calipso/IIR_ICSO00_S2-06.pdf, (last access 13 October 2016), 2000.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G.,
Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M.,
Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M.,
McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F.:
The ERA-Interim reanalysis: configuration and
performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137,
553–597, 10.1002/qj.828, 2011.
EUMETSAT: Typical radiometric accuracy and noise for MSG–1/2, Rep. EUM/OPS/TEN/07/0314, 4 pp.,
Darmstadt, Germany, 2007a.
EUMETSAT: A planned change to the MSG Level 1.5 image product radiance
definition, Rep. EUM/OPS-MSG/TEN/06/0519 issue v1A, Darmstadt, Germany, 9 pp., 2007b.Garnier A., Pelon, J., Dubuisson, P., Faivre, M., Chomette, O., Pascal, N.,
and Kratz, D. P.: Retrieval of cloud properties using CALIPSO Imaging
Infrared Radiometer, Part I: effective emissivity and optical depth, J. Appl. Meteorol. Climatol., 51, 1407–1425, 10.1175/JAMC-D-11-0220.1, 2012.Garnier, A., Pelon, J., Dubuisson, P.,Yang, P., Faivre, M., Chomette, O.,
Pascal, N., Lucker, P., and Murray, T.: Retrieval of cloud properties using
CALIPSO Imaging Infrared Radiometer, Part II: effective diameter and ice
water path, J. Appl. Meteorol. Climatol., 52, 2582–2599,
10.1175/JAMC-D-12-0328.1, 2013.GLOBE Task Team and Hastings, D. A., Dunbar, P. K., Elphingstone, G. M., Bootz, M., Murakami, H., Maruyama, H., Masaharu, H.,
Holland, P., Payne, J., Bryant, N. A., Logan, T. L., Muller, J.-P., Schreier, G., and MacDonald, J. S.: The Global Land One-kilometer Base Elevation
(GLOBE) Digital Elevation Model, Version 1.0, National Oceanic and
Atmospheric Administration, National Geophysical Data Center, 325 Broadway,
Boulder, Colorado 80305-3328, USA, Digital data base on the World Wide Web,
available at:
http://www.ngdc.noaa.gov/mgg/topo/globe.html (last access: 7 April 2017), 1999.
Goldberg, M., Ohring, G., Butler, J., Cao, C., Datla, R., Doelling, D., Gärtner, V., Hewison, T., Iacovazzi, B., Kim, D., Kurino, T., Lafeuille, J.,
Minnis, P., Renaut, D., Schmetz, J., Tobin, D., Wang, L., Weng, F., Wu, X., Yu, F., Zhang, P., and Zhu, T.: The global space-based inter-calibration system
(GSICS), B. Am. Meteorol. Soc., 92, 468–475, 2011.
GSICS Global Space-based Inter-Calibration System: Vision of GSICS in the
2020s: shaping GSICS to meet future challenges, WMO/GSICS-RD002 v1.1, 12 pp., 2015.
Hanafin, J. A. and Minnett, P. J.: Measurements of the infrared emissivity
of a wind-roughened sea surface, Appl. Opt., 44, 398–411, 2005.Hu, Y., Winker, D., Vaughan, M., Lin, B., Omar, A., Trepte, C., Flittner,
D., Yang, P., Sun, W., Liu, Z., Wang, Z., Young, S., Stamnes, K., Huang, J.,
Kuehn, R., Baum, B., and Holz, R.: CALIPSO/CALIOP Cloud Phase Discrimination
Algorithm, J. Atmos. Ocean. Technol., 26, 2293–2309,
10.1175/2009JTECHA1280.1, 2009.
Hunt, W., Winker, D., Vaughan, M., Powell, K., Lucker, P., and Weimer, C.:
CALIPSO lidar description and performance assessment, J. Atmos. Ocean. Technol., 26, 1214–1228, 2009.Inoue, T.: On the temperature and effective emissivity determination of
semitransparent cirrus clouds by bi-spectral measurements in the 10 µm
window region, J. Meteorol. Soc. Jpn., 63, 88–98, 1985.Jacquinet-Husson, N., Crépeau, L., Armante, R., Boutammine, C., Chédin, A., Scott, N. A., Crevoisier, C., Capelle, V.,
Boone, C., Poulet-Crovisier, N., Barbe, A., Campargue, A., Benner, D. Chris, Benilan, Y., Bézard, B., Boudon, V., Brown, L. R.,
Coudert, L. H., Coustenis, A., Dana, V., Devi, V. M., Fally, S., Fayt, A., Flaud, J.-M., Goldman, A., Herman, M., Harris, G. J.,
Jacquemart, D., Jolly, A., Kleiner, I., Kleinböhl, A., Kwabia-Tchana, F., Lavrentieva, N., Lacome, N., Xu, Li-Hong, Lyulin, O. M.,
Mandin, J.-Y., Maki, A., Mikhailenko, S., Miller, C. E., Mishina, T., Moazzen-Ahmadi, N., Müller, H. S. P., Nikitin, A., Orphal, J.,
Perevalov, V., Perrin, A., Petkie, D. T., Predoi-Cross, A., Rinsland, C. P., Remedios, J. J., Rotger, M., Smith, M. A. H., Sung, K.,
Tashkun, S., Tennyson, J., Toth, R. A., Vandaele, A.-C., and Vander Auwera, J.: The 2009 edition of the GEISA
spectroscopic database, J. Quant. Spectrosc. Ra., 112,
2395–2445, 10.1016/j.jqsrt.2011.06.004, 2011.
Jouglet, D., Scott, N. A., Pernin, J., Crepeau, L., Armante, R., Ben Sassi, M., and Chédin, A.: Short performance status of IASI on MetOp-A and
MetOp-B, Radiometric and spectral inter-comparison of IASI,
Validation of Level1c at LMD : An interactive intercalibration and
stand-alone approaches for IASI on board MetopA and MetopB and IIR on board
CALIPSO, GSICS meeting, 24–28 March 2014, EUMETSAT,
Darmstadt, 2014.
Liu, Q. and Schmetz, J.: On the problem of an analytical solution to the
diffusivity factor, Beitr. Phys. Atmos., 61, 23–29, 1988.
Luther, F. M., Ellingson, R. G., Fouquart, Y., Fels, S., Scott, N. A., and
Wiscombe, W.: Intercomparison of Radiation Codes in Climate Models (ICRCCM):
longwave clear sky results, B. Am. Meteorol. Soc., 69, 40–48, 1988.
Masuda K., Takashima, T., and Takayama, Y.: Emissivity of pure and sea
waters for the model sea surface in the infrared window regions, Remote
Sens. Environ., 24, 313–329, 1988.
Niclòs, R., Caselles, V., Coll, C., and Valor, E.: Determination of sea
surface temperature at large observation angles using an angular and
emissivity dependent split-window equation, Remote Sens. Environ., 111,
107–121, 2007.
Rodgers, C. D. and Walshaw, C. D.: The computation of the infrared cooling
rate in planetary atmospheres, Q. J. Roy. Meteor. Soc., 92, 67–92, 1966.Scott, N. A.: A direct method of computation of transmission function of an
inhomogeneous gaseous medium: description of the method and influence of
various factors, J. Quant. Spectrosc. Ra., 14, 691–707, 1974.
Scott, N. A.: Assessing CALIPSO IIR radiance accuracy via stand-alone
validation and a GEO/LEO inter-calibration approach using MODIS/Aqua and
SEVIRI/MSG, GSICS Quarterly, 3, available at:
http://www.star.nesdis.noaa.gov/smcd/GCC/documents/newsletter/GSICS_Quarterly_Vol3No3_2009.pdf, (last access 8
July 2016), 2009.
Scott, N. A. and Chedin, A.: A fast line-by-line method for atmospheric
absorption computations: The Automatized Atmospheric Absorption Atlas, J. Appl. Meteorol., 20, 802–812, 1981.
Scott, N. A., Chédin, A., Armante, R., Francis, J., Stubenrauch, C.,
Chaboureau, J.-P., Chevallier, F., Claud, C., and Chéruy, F.:
Characteristics of the TOVS Pathfinder Path-B data set, B. Am. Meteorol. Soc., 80, 2679–2701, 1999.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B.,
Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team: The CloudSat mission and the A-Train: A new
dimension of space-based observations of clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, 2002.
Tinto, F. and Trémas, T.: IIR Level 1 Status, 2nd CALIPSO
Exploitation Review, Norfolk (VA), USA, 2008.
Tournier, B., Armante, R., and Scott, N. A.: STRANSAC-93 et 4A-93:
Développement et validation des nouvelles versions des codes de
transfert radiatif pour application au projet IASI, Internal Rep. LMD, No.
201, LMD/CNRS, Ecole Polytechnique, Palaiseau, France, 1995.
Trémas, T.: Rapport de recette en vol – Radiométrie IIR-Calipso,
CNES Tech. Doc CAL-IIR-RP-1189-CNES, Toulouse, France, 30 pp., 2006.
Turner, D. S.: Systematic errors inherent in the current modeling of the
reflected downward flux term used by remote sensing models, Appl. Opt., 43,
2369–2383, 2004.Vaughan, M., Pitts, M., Trepte, C., Winker, D., Detweiler, P., Garnier, A.,
Getzewitch, B., Hunt, W., Lambeth, J., Lee, K.-P , Lucker, P., Murray, T.,
Rodier, S., Trémas, T., Bazureau, A., and Pelon, J.: CALIPSO data
management system data products catalog, document No. PC-SCI-503, Release
3.8, available at:
http://www-calipso.larc.nasa.gov/products/CALIPSO_DPC_Rev3x8.pdf (last access: 13 October 2016), 2015.Winker, D. M., Pelon, J., Coakley Jr, J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R.,
Kittaka, C., Kubar, T. L., LeTreut, H., McCormick, M. P., Megie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and
Wielicki, B. A.: The CALIPSO mission: A global 3D view of
aerosols and clouds, B. Am. Meteorol. Soc., 91, 1211–1229,
10.1175/2010BAMS3009.1, 2010.
Wu, X. and Smith, W. L.: Sensitivity of sea surface temperature retrieval to
sea surface emissivity, Acta Meteorol. Sin., 10, 376-3-84, 1996.Xiong, X., Wu, A., Wenny, B. N., Madhavan, S., Wang, Z., Li, Y., Chen, N.,
Barnes, W. L., and Salomonson, V. V.: Terra and Aqua MODIS thermal emissive
bands on-orbit calibration and performance, IEEE T. Geosci.
Remote, 53, 5709–5721, 10.1109/TGRS.2015.2428198, 2015.