AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-2911-2018Comparison of dust-layer heights from active and passive satellite sensorsComparison of dust-layer heightsKyllingArvearve.kylling@nilu.nohttps://orcid.org/0000-0003-1584-5033VandenbusscheSophiehttps://orcid.org/0000-0002-3966-3747CapelleVirginiehttps://orcid.org/0000-0002-9912-3742CuestaJuanhttps://orcid.org/0000-0001-9330-6401KlüserLarsLelliLucahttps://orcid.org/0000-0002-6698-1388PoppThomasStebelKerstinhttps://orcid.org/0000-0002-6935-7564VeefkindPepijnNILU – Norwegian Institute for Air Research, P.O. Box 100, 2027 Kjeller, NorwayRoyal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, BelgiumLaboratoire de Météorologie Dynamique (LMD), UMR8539, CNRS/IPSL, Ecole Polytechnique, Palaiseau, FranceLaboratoire Interuniversitaire des Systémes Atmosphériques (LISA), CNRS UMR7583,
Université Paris Est Créteil, Université Paris Diderot, Créteil, FranceDeutsches Zentrum für Luft-und Raumfahrt e.V. (DLR), Deutsches Fernerkundungsdatenzentrum (DFD),
82234 Oberpfaffenhofen, GermanyInstitute of Environmental Physics (IUP), University of Bremen, Bremen, GermanyRoyal Netherlands Meteorological Institute (KNMI), 3730 AE De Bilt, the NetherlandsGeosciences and Remote Sensing, Delft University of Technology, 2628 AA Delft, the NetherlandsArve Kylling (arve.kylling@nilu.no)18May2018115291129363October201717October201723April201823April2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/2911/2018/amt-11-2911-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/2911/2018/amt-11-2911-2018.pdf
Aerosol-layer height is essential for understanding the
impact of aerosols on the climate system. As part of the European Space
Agency Aerosol_cci project, aerosol-layer height as derived from passive
thermal and solar satellite sensors measurements have been compared with
aerosol-layer heights estimated from CALIOP measurements. The Aerosol_cci
project targeted dust-type aerosol for this study. This ensures relatively
unambiguous aerosol identification by the CALIOP processing chain. Dust-layer
height was estimated from thermal IASI measurements using four different
algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and
SCIAMACHY (IUP) measurements. Due to differences in overpass time of the
various satellites, a trajectory model was used to move the CALIOP-derived
dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height
pixels. It is not possible to construct a unique dust-layer height from the
CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative
extinction height defined as the height where the CALIOP extinction column is
half of the total extinction column, and the geometric mean height, which is
defined as the geometrical mean of the top and bottom heights of the dust
layer. In statistical average over all IASI data there is a general tendency
to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted
height for three of the four algorithms assessed, while the fourth algorithm
has almost no bias. When comparing geometric mean height there is a shift of
-0.5 km for all algorithms (getting close to zero for the three
algorithms and turning negative for the fourth). The standard deviation of
all algorithms is quite similar and ranges between 1.0 and 1.3 km.
When looking at different conditions (day, night, land, ocean), there is more
detail in variabilities (e.g. all algorithms overestimate more at night than
during the day). For the solar sensors it is found that on average SCIAMACHY
data are lower by -1.097 km (-0.961 km) compared to the
CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are
lower by -1.393 km (-0.818 km).
Introduction
Aerosol is identified as an essential
climate variable (ECV) by the Global Climate Observing System (GCOS,
http://www.wmo.int/pages/prog/gcos/, last access: 7 May 2018). The
aerosol-layer height (GCOS product A.10.3) is one of four aerosol parameters
which is needed to enhance our understanding of the aerosols' role in the
climate system. Furthermore a deeper insight is important for radiative
budget analysis, studying chemical and physical interactions in the
troposphere, weather forecast modelling, remote sensing and air quality
initiatives. Ground-based methods (lidar) offer high accuracy and calibration
benchmarks; however their geographical coverage is sparse. Hence, satellite
observations of the aerosol-layer height are warranted and the quality of
such a product needs to be assessed. As part of the European Space Agency
(ESA) Climate Change Initiative CCI, the Aerosol_cci
project has conducted a comparison between dust-type
aerosol-layer heights from passive and active sensors to identify strengths
and possible weaknesses in the estimate of this parameter.
Both active and passive methods may be used to estimate the aerosol-layer
height. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board
the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations
(CALIPSO) satellite and
https://www-calipso.larc.nasa.gov/ provides detailed
vertical information with a vertical resolution of 30 m below
8.2 km and a horizontal footprint of 335 m. Passive solar and
thermal infrared satellite instruments may provide global data on a daily
basis with horizontal resolution of the order of tens of kilometres. For
example, retrieved desert dust aerosol vertical
profiles from Infrared Atmospheric Sounding Interferometer (IASI)
measurements; described the three-dimensional
distribution of a dust outbreak over eastern Asia, including dust height and also
using IASI measurements; used the O2 A-band to
retrieve aerosol-layer height from the Global Ozone Monitoring Experiment-2A
(GOME-2A). Dust top height may also be estimated using stereo view techniques by
either utilizing instruments with multi-angle capabilities for
example the Advanced Along Track Scanning Radiometer, AATSR, or by
combining measurement from different sensors see for
example. For a comprehensive survey on the methodological
approaches, technical and scientific challenges of the retrieval of aerosol
height, we refer the reader to the recent review by .
The aim of this work is to assess the different aerosol-layer height products
from different algorithms for various solar (SCanning Imaging Absorption
SpectroMeter for Atmospheric CHartographY, SCIAMACHY, GOME-2) and thermal
(IASI) sensors by comparison with CALIOP. The Aerosol_cci project targeted
dust-type aerosol for this study. The relatively unambiguous classification
of dust by CALIOP and the availability of large dust events possibly avoid
any biases due to aerosol misclassification in the aerosol height comparison.
Earlier studies compared
monthly averaged and gridded data,
or investigated a specific episode . We perform a point-by-point
comparison for selected episodes. Furthermore we account for differences in
satellite overpass times using trajectory model analysis. Finally, for the
first time, by utilizing data from GOME-2, SCIAMACHY and IASI with their
respective spectral and spatial resolutions, results from the different
passive infrared and solar algorithms are compared for the same dust episodes
to identify strengths and weaknesses. Note that this work focuses on the
comparison of dust-layer heights retrieved from active and passive sensors.
Comparisons of aerosol dust amount are outside the scope of this work and is
discussed elsewhere .
The remainder of the paper is organized as follows: in Sect.
the data and data analysis methods are presented. The results from the
aerosol-layer height comparison are given in Sect. . The
results are discussed in Sect. and followed by the
conclusions.
Data and methodology
To allow the inclusion of data from the SCIAMACHY instrument that ceased
operation in 2012, four desert dust events in 2010 were selected (total 40
days):
18–27 March (10 days),
22 May–1 June (11 days),
1–12 July (12 days),
14–20 September (7 days).
The comparison focuses on the region between 0–40∘ N and
80∘ W–120∘ E (see
Fig. ) and is mainly affected by
dust from the Sahara but is also influenced by dust from the Middle East, India
and western China.
Active instrument dust height retrievals – CALIOP
CALIPSO is the fourth of the six satellites in the A-Train satellite
constellation. All six of the A-Train satellites cross the equator within a
few minutes of one another at around 13:30 local time. CALIOP is part of the
payload of the CALIPSO platform. The CALIOP laser produces simultaneous
co-aligned pulses at 532 and 1064 nm that are used to measure the
backscatter profile. The 532 nm pulse is linearly polarized. The
return signal is polarized parallel and perpendicular to the outgoing plane
and detected by two photomultiplier detectors. The CALIOP aerosol-typing
algorithm uses layer-averaged depolarization and the 532 nm attenuated
backscatter to classify the aerosol into one of six types: clean marine,
dust, polluted continental, clean continental, polluted dust, smoke
. Of the six types the dust aerosol is largely non-spherical,
implying a relatively large depolarization ratio and hence relatively
unambiguous classification. Numerous data products are available from CALIOP.
We use the 5 km profile product from CALIOP data version V4-10. Only dust
profiles with CALIOP cloud aerosol discrimination (CAD) values between -100
and -20 are included . Profiles containing polluted dust
and water and ice clouds are excluded. Furthermore we only include dust
layers that are continuous; thus, multilayered dust clouds are excluded from
the analysis. The dust-layer height is estimated from the extinction
coefficient at 532 nm (Extinction_Coefficient_532). The
extinction coefficient is a retrieved quantity and we only include profiles
for which the quality control flag Extinction_QC_Flag_532 equals
0 (unconstrained retrieval; initial lidar ratio unchanged during solution
process) or 1 (constrained retrieval).
CALIOP dust-layer height
Aerosol-layer heights may be termed either “effective” or “real”
heights. The effective layer height represents the height at which the total
aerosol load should be placed in order to be representative of the radiative
properties of this aerosol. The thickness of effective layers are
typically assumed to be small, 500 m or 1 km. For climate
impact studies effective layer height is an important parameter, as it,
together with the aerosol optical depth, single-scattering albedo and phase
function, allows quantitative estimates of the aerosols' direct radiative
forcing. The real aerosol height may be described in terms of layer
boundaries or by the full vertical profile. It is required for
the understanding and characterisation of aerosol–cloud interactions, air
quality and flight safety.
There is no unique way to calculate the height of a dust layer from
CALIOP data. Possible methods include the following:
Threshold: Calculate the cumulative extinction and set the height to
where cumulative extinction is above a prescribed threshold.
Cumulative extinction: Calculate the cumulative extinction and set the height
to where the extinction column is half of the total extinction column.
Geometric mean: Identify the top and bottom heights of the dust layer
and set dust height to the mean of the two.
Extinction weighted: Weigh the dust-layer height zi for
layer i with an appropriate parameter and calculate the weighted average,
for example using the extinction coefficient βi as in
:zCALIOP=∑βizi∑βi.
In Fig. the extinction-weighted and
geometric mean CALIOP dust heights are plotted against the cumulative
extinction CALIOP dust height for the days and region under study. The
extinction-weighted and cumulative extinction methods (right plot) are fairly
similar except below about 2.5 km, where the extinction-weighted
method gives slightly larger heights. The geometric mean method (left plot)
generally gives larger heights than the cumulative extinction method. The
geometric mean method is purely geometrical. The cumulative extinction,
threshold and extinction-weighted methods use the profile extinction
information from CALIOP. Below we present results for one CALIOP height
method that includes extinction information and one that is purely
geometrical. The extinction-weighted and cumulative extinction methods are
nearly the same, R2=0.9942; right plot
Fig. . To avoid having to arbitrarily set a
threshold for the threshold method, we thus present results below for the
cumulative extinction and the geometric mean methods.
(a) The CALIOP cumulative extinction height versus the
CALIOP geometric mean height. Linear regression (y=ax+b) gives a=0.882,
b=0.6813, R2=0.941 and RMSE = 0.652 km. (b) The
CALIOP cumulative extinction height versus the CALIOP extinction weighted
height. Linear regression analysis gives a=0.9619, b=0.1597, R2=0.9942 and RMSE = 0.182 km.
It is noted that ambiguities in dust heights derived from CALIOP are larger
for thick and optically dense dust layers. In these cases, the inversion of
lidar profiles is less accurate for the lower part of these layers due to
uncertainties in the lidar ratio for dust and multiple-scattering effects
see e.g.. Multiple-scattering effects are
neglected in the CALIOP operational products used here.
Passive instrument dust height retrievals
The dust-layer height was estimated from measurements by IASI and GOME-2
on board the MetOP-A satellite and SCIAMACHY on board Envisat. MetOP-A orbits
in a sun-synchronous mid-morning orbit, crossing the equator at 09:30 local
solar time in the descending node. Envisat was in a sun-synchronous polar
orbit crossing the equator at 10:00 local solar time (MLST) in the
descending node. The various dust height retrieval algorithms used in this
study are summarized in Table and described in more detail
below.
Summary of dust height retrieval algorithms. See text for further
details, including definitions of acronyms.
InstituteRadiativeSatelliteAlgorithm specificsHeightReference(s)(algorithm name)transferinstrumentand aerosol typeBIRA-IASBLine-by-lineIASIOptimal estimation;Vertical profiles and(MAPIR)Lidort, Miedust, ashaveraging kernels,Vandenbussche and1 km resolution fromDe Mazière ()1 to 6 km, 1.5 to2 degrees of freedomDLRNo direct forwardIASIPCA, spectral matching,Effective layer,(IMARS)modelling, opticalBayesian probability;height fromproperties from Miedust, ice clouds, possibleemissionapplication to ashtemperatureLMD4A/OP-DISORTIASIRefractive indicesAverage weighted,from layer heightand LISALine-by-lineIASITikhonov–PhilipsVertical profiles and(AEROIASI)KOPRA, Mieauto-adaptiveaveraging kernels,regularisation;1 km resolution fromdust0 to 9 km, approx.1.5 degrees of freedomKNMILine-by-lineGOME-2Optimal estimation;Effective layerDISAMARall typesheight, accuracyHenyey–Greensteinof 0.5–1 km ifphase functionAOD > 0.3IUPLine-by-lineSCIAMACHYAdjoint RTE;EffectiveSCIATRANdustlayer heightT-matrixThe IASI algorithm at BIRA-IASB: MAPIR
The Mineral Aerosol Profiling from Thermal Infrared (MAPIR) retrieval
algorithm is an extensive technical and scientific improvement of the
algorithm first published by . Version 3.5 of the
algorithm, fully described in , is used in this
study.
The MAPIR retrieval scheme is based on the optimal estimation method
OEM,, which iteratively adjusts a state vector
composed of seven variables: the surface temperature
(Ts) and the vertical profile of dust aerosol concentration, from
1 to 6 km height in steps of 1 km. The retrieval is performed only
on cloud-free scenes (< 10 % cloud coverage in IASI level 2 cloud
product). Unfortunately, the IASI cloud product seems to mislabel some
intense dust clouds as meteorological clouds, removing that data from our
analysis.
The a priori vertical profile of desert dust concentration is obtained from
the LIdar climatology of Vertical Aerosol Structure for space-based lidar
simulation studies (LIVAS) monthly 1∘×1∘
climatology derived from CALIOP data . The conversion
from 532 nm extinction to particle number concentration is done using the
cross section of the aerosol particles used in MAPIR. To account for the fact
that CALIOP measurements are sparse, plausibly impacting the continuity of
the climatology amongst adjacent 1∘×1∘ (the mean
extinction in adjacent cells may come from measurements made for different
days), we use a horizontal running mean over 25 cells (5 in latitude, 5 in
longitude). The standard deviation for the dust aerosol vertical profile is
set to 100 % at all altitudes and locations.
The dust aerosols in MAPIR are parameterized with a log-normal particle size
distribution (PSD) with a median radius
of 0.6 µm, geometric standard deviation of 2 corresponding to an
effective size of 2 µm) and the refractive index from
GEISA-HITRAN dust-like,
.
After the retrievals, quality filters are undertaken. The retrievals are
marked to be of good quality when
the root mean square of spectral residuals is lower than 2 K
over land and 1 K over oceans,
the final fitted aerosol optical depth (AOD) at 10 µm is lower than 8 (otherwise the
probability is extremely high that the scene was cloudy and unflagged as
such).
The dust detection is computed a posteriori and based on the single
criterion that the retrieved 10 µm AOD must be higher than 0.01.
This is a very low threshold, and although it ensures that all dust scenes are
indeed flagged, it might flag scenes where the aerosol presence is
questionable. The AOD is obtained by vertical integration of the
concentration profile and multiplication of the extinction coefficient at
the desired wavelength. The mean height is obtained from the profile as a
linear interpolation of the height that would separate the aerosol column
into two identical partial columns (in other words, half the aerosols are below
the mean height and half are above it).
IASI DLR algorithm – IMARS
The Infrared Mineral Aerosol Retrieval Scheme (IMARS) for IASI
developed at DLR (Deutsches Zentrum für Luft-und Raumfahrt) combines dust
and ice cloud remote sensing using principal component analysis (PCA) of the
high-resolution IASI spectra (version 4.2). Bayesian inference is used for
differentiating between dust and ice clouds
. The method may also be
applied to volcanic ash retrievals , with a focus
on the very variable composition of the ash for which there is lack of
reliable reports in the literature.
The retrieval uses spectral pattern matching between 8 and 12 µm
in a subspace of the observation space, spanned by suited eigenvectors for
inferring dust/ash properties from the observations. With this approach
direct forward modelling of the infrared radiative transfer is avoided, as
this would be strongly underdetermined due to the lack of information on
surface emissivity (over deserts), atmospheric temperature, humidity profiles
as well as detailed information on dust/ash composition, particle size
and sphericity. The composition of dust/ash is assumed to be represented by
linear combinations of typical dust composition mixtures .
For extreme cases not represented by these mixtures (e.g. very high calcite
of gypsum content in dust aerosols), the retrieval will not be able to
correctly characterize the dust/ash load. Dust optical properties used here
have been calculated with traditional Mie theory, thus ignoring particle
non-sphericity see. One of the
outputs of the DLR algorithm is the dust/ash emission temperature. Using a
vertical temperature profile (standard atmosphere or model output), it is then
converted to effective layer height . De facto, the
emission temperature is retrieved relative to the background, which
implicitly delivers height information and not an absolute temperature.
The layer height is an effective emission height of a geometric thin (delta
shape) dust layer. This makes its interpretation with regard to an averaged
CALIPSO extinction profile non-intuitive. For optically thin dust layers the
effective layer height is similar to the mean extinction of the profile;
however, with growing dust AOD it moves further up in the profile (details
depend on dust properties). Thus we expect a positive bias of the IMARS-layer height to the cumulative
extinction height from CALIPSO.
IASI LMD algorithm
The LMD (Laboratoire de Météorologie Dynamique) method for the
retrieval of dust characteristics from IASI observations was originally
developed for application to the Atmospheric Infrared Sounder (AIRS)
and then slightly modified as described
in detail in and in
for application to IASI. The method used to derive dust characteristics from
IASI observations is a three-step physical algorithm based on a look-up table
(LUT) approach. The first step constrains the atmospheric state (temperature
and water profile) using 18 channels selected in the spectral range
4.5–14.5 µm and mostly sensitive to temperature and water
profiles between 900 and 200 hPa and not, or almost not, sensitive to
surface characteristics (temperature, emissivity). The second step
simultaneously determines the 10 µm AOD, the dust-layer mean
height and the surface temperature using eight channels localized in three
window regions: 8–9, 10–12 and 4.6–4.7 µm. This selection of
channels, both at short and long wavelengths, is aimed at decorrelating the
contribution of AOD, height and surface temperature to the observed signal.
The dust coarse-mode particle effective radius can be determined in a third
step.
For each step, LUTs of IASI-simulated brightness temperatures are calculated
using the forward coupled radiative transfer model 4A/OP-DISORT
available from http://4aop.noveltis.com, . Entries
to the model include AOD, height, surface pressure, surface temperature and
emissivity, viewing angle, two refractive indices from
and and a set of 2311
atmospheric situations. These were selected using statistical methods from
80 000 radiosonde reports and stored in the Thermodynamic Initial Guess
Retrieval (TIGR) climatological database .
The PSD is modelled by a monomodal log-normal distribution described by
the effective radius (Reff) and the standard deviation of the
distribution σg. Following the results of previous
sensitivity studies Appendix A,, fixed
values are taken for the effective radius (Reff=2.3µm) and for the standard deviation of the size distribution
(σg=0.65).
The aerosol vertical distribution is supposed to be concentrated within a
single homogeneous layer. While this assumption cannot correctly describe
observations that are in general more complex, the height retrieved here can
be defined as an average weighted height for which half of the dust optical
depth is below and half of the optical depth is above. This infrared optical
equivalent to the real vertical profile is therefore appropriate for
computing dust infrared forcing. It is worth noting that the resulting mean
layer height corresponds to height above sea level. Several aspects of the
retrieval algorithm, e.g. robustness in the aerosol model (size distribution,
shape and refractive indices), possible contamination by other aerosol
species, radiative transfer model bias removal or cloud mask including
discrimination between clouds and aerosols, were investigated and details may
be found, for example, in and . The
surface emissivity spectrum is supposed to be known and is read from a
0.5∘ monthly grid retrieved from IASI .
IASI LISA algorithm – AEROIASI
The IASI algorithm from LISA (Laboratoire Interuniversitaire des Systémes
Atmosphériques), called AEROIASI, has been conceived to observe the
three-dimensional distribution of desert dust plumes for each overpass of
IASI, both over land and ocean . It derives vertical
profiles of desert dust in terms of the extinction coefficient at
10 µm from individual thermal infrared spectra measured by IASI.
It is a constrained least-squares fit method, based on explicit radiative
transfer calculations, in which the vertical distribution and abundance of
dust are iteratively adjusted in order to fit IASI observations. This
approach uses auto-adaptive constraints for simultaneously adjusting the dust
profile and surface temperature in order to offer particularly good
adaptability for different atmospheric and surface conditions. This
flexibility makes the aerosol retrieval possible for most cloud-free IASI
pixels, both over ocean and land (even for bright surfaces and relatively low
aerosol loads). The information on the vertical distribution of dust is
mainly provided by their broadband radiative effect, which includes aerosol
thermal emission depending on the height of the vertical profile of
temperature (assuming local thermal equilibrium).
AEROIASI uses an a priori desert dust model (including dust microphysical
properties) and meteorological profiles provided as inputs to the radiative
transfer model. The line-by-line Karlsruhe Optimized and Precise Radiative
transfer Algorithm KOPRA, is used to simulate thermal
infrared radiance spectra and the inversion module KOPRAFIT is used to compare them to those measured by IASI,
for 12 selected spectral micro-windows in the atmospheric window between 8
and 12 µm. KOPRA accounts for light absorption, emission and
single scattering by aerosols, using dust optical properties derived at each
wavelength with a Mie code , which is optimized as
described in and . The vertical
grid of all profiles in the simulations is set between the surface and 9 km
height a.s.l. (above mean sea level), with 1 km increments. For each pixel,
we use atmospheric temperature profiles and first guesses of surface
temperatures and water vapour profiles from ECMWF ERA-Interim reanalysis
. For all seasons and locations, AEROIASI uses a unique a
priori vertical profile of dust derived from CALIOP average dust profiles
over the Sahara in the summer of 2011.
Once IASI spectra are fitted, a series of quality checks are performed in
order to screen out cloudy measurements and aberrant retrievals. We exclude
IASI pixels with derived surface temperatures below their ERA-Interim
reanalyses counterparts by more than 10 K and those pixels exhibiting
too-high root-mean-squared spectral residuals or horizontal variability with
respect to their closest pixels. For each quality-checked retrieval, we
derive a vertical profile of dust extinction coefficient (α10 in
km-1) at 10 µm, the associated AOD (by vertical
integration of the extinction profile), and mean and top heights of the
observed dust layer. The mean height of the observed dust layer (the product
used in the current paper) is defined as the height below which the integral
of the extinction coefficient profile reaches 50 % of the AOD. Likewise,
the height of the top of the dust layer is defined as the height below which
the integral of the extinction coefficient profile reaches 95 % of the
AOD. Indeed, AEROIASI provides valuable information, not only on the mean
height of the dust layers but also on their vertical extent (i.e. layer-top
heights and whether the layers reach the ground or are elevated).
In this study AEROIASI retrievals from version 2 of the algorithm are used.
They mainly differ from the previous version described by
in the a priori desert dust model and the surface emissivity database. Using
these new databases, we obtain lower spectral residuals with respect to IASI
measurements than with the previous version and higher adaptability for
covering the large region analysed in this paper (i.e. the tropical dust
belt). The climatological desert dust model consists of refractive indices, a
single-mode log-normal particle size distribution and an a priori vertical
profile of dust. Refractive indices are taken from field measurements of
Saharan dust analysed by . The modal radius and width of
the single-mode distribution are prescribed from average volume effective
radius and width for the coarse mode derived from Saharan ground-based
stations in June 2011 for radii > 0.6 µm of the AERONET
size distributions, http://aeronet.gsfc.nasa.gov,. A
unique first guess of dust vertical distribution (the same profile for all
pixels and all seasons) is considered in the inversion, which is obtained
from an average of CALIOP extinction vertical profiles for dust over the
Sahara (during large dust outbreaks in late June 2011), scaled to particle
concentration units (in order to set an a priori AOD at 10 µm of
0.03). Forward simulations include surface emissivity from a global monthly
IASI-derived climatology over land and a
surface-temperature-dependent model over ocean .
GOME-2 KNMI algorithm
The deep oxygen lines (A band and/or B band) in the near infrared of the
shortwave spectrum have traditionally been used for retrieval of the cloud
height. In the absence of clouds, these bands contain information on the
aerosol height . The algorithm developed at KNMI within the
TROPOMI/Sentinel-5 Precursor programme is based on the
optimal estimation method and aims to derive the aerosol-layer height
. This method has also been applied to Greenhouse gases
Observing SATellite (GOSAT) and GOME-2 data within the ongoing ESA AeroPro
study, in support of the Sentinel-4 development. The algorithm is sensitive
to all aerosol types, including dust, biomass burning and industrial
pollution plumes. Sensitivity analyses performed for the TROPOMI/Sentinel-5
Precursor Algorithm Theoretical Basis Document (ATBD) indicate that the
aerosol-layer height can be derived with an accuracy of 0.5–1 km if
the AOD is 0.3 or larger.
The algorithm uses the Determining Instrument Specifications and Analyzing
Methods for Atmospheric Retrieval (DISAMAR) and simulation package
developed at KNMI. In the set-up that is used in this work, the aerosol is
modelled as a 50 hPa thick layer, for which the height and the aerosol
optical depth are fitted. The single-scattering albedo of the aerosol
particles is assumed to 0.95 and we apply a Henyey–Greenstein phase function
with an asymmetry parameter of 0.7. A climatological value is used for the surface
reflectance. Pressure-temperature profiles are obtained from the
operational ECMWF forecast. The algorithm uses a fit window between 758 and
762 nm.
The algorithm is only applied to cloud-cleared scenes for which the UV
aerosol index has a value exceeding 1.0, indicating the presence of absorbing
aerosol layers. For the GOME-2 data used in this work, the cloud clearing is
done based on the GOME-2 data itself, which may result in undetected
subpixel cloudiness. It is noted that the size of the GOME-2 ground pixels
is much larger than for the TROPOMI instrument, for which the algorithm has
been designed.
SCIAMACHY IUP algorithm
The IUP (Institute of Environmental Physics) algorithm determines the aerosol layer height using top-of-atmosphere (TOA) reflectances R (defined as the
sun-normalized radiances, weighted by the cosine of the solar zenith angle)
in the oxygen A-band, that is, in the range 758–772 nm at the
nominal spectral sampling 0.21 nm of SCIAMACHY, for a Gaussian
instrument response function of 0.48 nm. The retrieval is based on
the calculation of the weighting functions Wh,b,τ=∂Rh,b,τ/∂h,b,τ, i.e.
the Jacobians of R as function of the top and bottom altitude h, b and
optical thickness τ of the aerosol layer. Upon linearization of the
problem, the measured R in a gaseous absorption band can be written as a
function of the desired h. Given that τ is inferred from an
independent source, such as a non-absorbing channel outside the oxygen
A-band, typically λ=758nm, the retrieval can be further
simplified assuming that either the aerosol layer originates at the ground
(b=0km) or is elevated (b≠0km). The latter
assumption implies that the prior geometrical thickness is preserved when
retrieving h. Either way, the problem is reduced to the calculation of
W(h) and the minimization of the
difference between the forward-modelled and the measured reflectance,
converging after ∼ 4 iterations on average, which delivers the height
of the layer.
Information on the local non-spherical dust optical properties, encoded in
the spectral scattering T-matrix , as well as on the
single-scattering albedo and the aerosol extinction (box) profiles, are
embedded in W(h). It has been assumed that these quantities are independent
of height inside the aerosol layer. The HITRAN 2008 edition
is used for the line intensities of the absorbing
species (oxygen and water vapour) included in the forward problem. The full
retrieval chain is implemented and carried out within the radiative transfer
model SCIATRAN .
The selection of cloud-free SCIAMACHY pixels (60×40km2
of nominal footprint size) relies on the analysis of joint histograms of
geometric cloud cover (CC < 0.1, from co-located
1×1km2 MEdium Resolution Imaging Spectrometer (MERIS)
observations, ) and aerosol absorbing index
(AAI > 1.0, ) for the area of interest. Surface
reflectivity is taken from the MERIS-derived black-sky data set
, which is the critical parameter for the accuracy of the
retrieved h. An error of ±10 % in the a priori value of surface
reflectivity can cause a bias of up to ±1 km for τ=0.25
and h>3.0km. More details are given in about
the IUP algorithm as well as validation with independent measurements when
it is applied to an elevated ash layer.
The IASI dust-layer height from the BIRA-IASB (a),
DLR (b), LMD (c) and LISA (d) analysis. The CALIOP
profiles identified as dust within 500 km and 5 h time
differences from nearest IASI pixel are overlaid (red dots). The location of
the CALIOP height after shifting to IASI overpass time is shown by the black
dots. The time range (UTC) in the title gives the times of the first and last
CALIOP points plotted.
Data selection and comparison methodology
The selection of data and the comparison between CALIOP and the other
satellite instrument estimates of dust heights proceed through the following
steps for each date listed at the beginning of Sect. :
Identify the CALIOP swaths that are within the region of interest.
Identify the closest CALIOP swath and IASI, GOME-2 and SCIAMACHY dust
pixels in time and space. Due to the difference in the CALIPSO equator
crossing time (13:30) and MetOP-A and Envisat equator crossing times (09:30
and 10:00), a maximum time difference of 5 h is allowed between
CALIOP and IASI, GOME-2 and SCIAMACHY dust pixels in this step. To allow for
possible movement of dust pixels between overpasses, pixels within
500 km were included for subsequent analysis. This allows for a
maximum wind speed of 100 kmh-1.
For the CALIOP swaths from step 2, identify CALIOP dust profiles using
the CALIOP dust flag and the CAD score. Calculate CALIOP cumulative
extinction and geometric mean dust-layer heights.
Move CALIOP dust-layer heights from the previous step backward in
time to the Metop-A and Envisat overpass times using the FLEXTRA trajectory
model.
After moving the CALIOP dust heights backward in time they may still
be at locations that are different from the IASI, GOME-2 and SCIAMACHY dust
heights. A second co-location is thus made to co-locate the moved CALIOP dust
heights with IASI, GOME-2 and SCIAMACHY dust heights. The maximum difference
in distance is set to 20 km for IASI and 100 km for SCIAMACHY
and GOME-2, reflecting the larger footprints of the latter two instruments.
Analysis of height differences including statistics.
In Fig. examples of data from
steps 1–5 are shown. The pixels identified as dust from IASI data by the
BIRA-IASB (Royal Belgian Institute for Space Aeronomy; top plot,
Fig. ), DLR (second plot,
Fig. ), LMD (third plot,
Fig. ) and LISA (bottom plot,
Fig. ) algorithms are overlaid by
CALIOP cumulative extinction heights, which are derived from profiles
identified as dust (step 3, red dots) that are within the temporal and
spatial requirements. CALIOP data are recorded after the IASI overpass. To
account for possible movements of dust between the overpasses, the CALIOP
dust heights were moved in longitude, latitude and height using the FLEXTRA
model step 4, . FLEXTRA calculated mean wind trajectories with meteorological input data from the ECMWF. Here operational data with a 1∘ latitude ×1∘
longitude resolution, 91 vertical levels and a time resolution of 3 h
were used. FLEXTRA does not include turbulence or loss processes. Quantification of trajectory errors is always difficult due to a
general lack of ground-truth data. However, FLEXTRA has been quantitatively
evaluated in the past. Comparisons of FLEXTRA trajectories driven with ECMWF
data with balloon trajectories have revealed typical horizontal transport
errors of about 20 % of the travel distance but with large variability
from case to case . Evaluation
against meteorological tracers such as potential vorticity suggests errors of
a similar magnitude . Thanks to improvements in the
meteorological analysis data, slightly smaller errors may be assumed for more
recent years, but the order of magnitude of the errors is likely still
similar.
Curtain plot of the CALIOP extinction coefficient for heights
identified as dust. The black dots are the column optical depth at
532 nm from CALIOP. The curtain is for the CALIOP data between 40 and
60∘ E in the top plot of
Fig. .
IASI dust-layer heights co-located to CALIOP cumulative extinction
heights (black circles) and CALIOP geometric mean heights (red circles) for
the same time and location as in
Fig. . Also shown are shifted
(upward triangles) and unshifted (downward triangles) CALIOP cumulative
extinction and geometric mean
heights.
The black dots in Fig. are CALIOP
dust height pixels that have been moved from their original location (red
dots) to the nearest IASI pixel (step 5). As the cumulative extinction and
geometric mean CALIOP dust heights are different they will be moved by
FLEXTRA to different locations. An example of this is seen in
Fig. , where the cumulative
extinction (black circles) and geometric mean (red circles) heights from the
passive instruments sometimes overlap (not moved or moved to same location
and height) and sometimes do not overlap (moved to different location and/or
height). It is also seen in the difference in the number of co-located
points (Table ). For
the full data period the CALIOP dust heights were on average moved upwards by
0.015 (cumulative extinction) and 0.020 km (geometric mean), both
with standard deviations of 0.25 km.
Results
The analysis steps 1–5 in Sect. were performed for all
days and algorithms. The number of dust pixels identified by the various
algorithms after step 1 is given in Table . The number of
pixels identified as dust by the various IASI algorithms vary by a factor of
4.6. The differences reflect the differences in dust detection methods and it
is outside the scope of this study to further investigate the reasons for
these differences. As expected the solar algorithms detect far fewer dust
pixels due to only daytime coverage (factor of 2) and larger pixels size
(factor of about 16). The difference between the two solar algorithms (KNMI
and IUP) are due to differences in the constraints set to detect dust. In
step 2 dust pixels are selected within a given time and distance from the
CALIOP-detected dust pixels. This step reduces the number of IASI data points
to between 0.58 and 1.8 % of those in step 1. The number of GOME-2 and
SCIAMACHY points are reduced to 17.3 and 73.0 % respectively. The
movement of CALIOP dust heights to the MetOp-A and Envisat overpass times and
the final co-location of CALIOP heights and dust pixels gives the final
number of dust heights to be compared to CALIOP dust heights; see values for
step 5 in Tables and .
The number of data points (dust heights) at the data reducing step of the
data analysis chain described in Sect. . Step number refers
to the analysis steps as described in
Sect. .
a Numbers in parenthesis are data points in
percentage relative to the total number in the previous analysis step.
b Numbers in parenthesis are data points in percentage
relative to the total number in analysis step number 2, for example
18.0=2408/13377 (column 2, row 4).
Inspection of IASI-retrieved dust heights shown in
Fig. reveal differences in dust
detection and dust height between the various algorithms. While differences
in dust detection is not the subject of this paper, we do, however, note that
there are substantial differences in the pixels identified as containing dust
by the various algorithms. In particular the DLR algorithm detects very
little dust over the ocean regions; the BIRA-IASB and LISA algorithms detect
dust over the ocean west of 40∘ W and north of 20∘ N,
whereas the DLR and LMD algorithms do not detect dust in this region; for the
example swath plots in Figs.
and all algorithms except LMD
detect dust north of about 28∘ N. The BIRA-IASB algorithm's
detection of dust over the Himalaya is due to retrievals being undertaken for
all non-cloudy scenes, and the final result may never be a true zero due to
the method used. These retrievals have a low AOD and their inclusion
indicates that the AOD threshold for the dust flag may be too permissive. The
differences in dust detection are the reason for the different number of
pixels available for comparison with CALIOP.
For the example CALIOP swath shown in
Fig. the BIRA-IASB algorithm (red
and black circles, Fig. ) agrees
reasonably with the CALIOP geometric mean heights (red triangles) and gives
higher dust heights compared to the CALIOP cumulative extinction heights
(black triangles). For the DLR algorithm the situation is similar, but the
DLR algorithm generally gives larger dust heights. The LMD algorithm heights
are generally similar to CALIOP cumulative extinction heights, while LISA
algorithm heights are in better agreement with the geometric mean heights.
For this transect, BIRA-IASB and LISA algorithms capture the rather
monotonous decrease of dust-layer heights from about 4 km in altitude
near 30∘ N to 2 km in altitude at 19∘ N depicted by
CALIOP geometric mean heights. The LMD algorithm retrieves dust heights near
1.5 km at
24–27∘ N, similar to the CALIOP cumulated extinction estimates. The behaviour
for this single overpass is also present in the full IASI data set as shown
in Figs. –
and Table . However, note that there are substantial
differences when comparing the passive methods with the CALIOP cumulative
extinction and geometric mean methods. Overall, the CALIOP geometric mean
method gives a larger CALIOP dust height (Table ).
Thus, the CALIOP minus passive instrument difference is smaller for the
geometric mean method compared to the cumulative extinction method. The
geometric mean method also gives slightly smaller standard deviations and
more dust heights from the passive instrument within the CALIOP dust layer;
see Table . This may point to a non-symmetrical vertical
distribution of the aerosols, with more aerosol in the lower part of the
layer, where IASI algorithms usually have less sensitivity (depending on
surface temperature).
(a–d) The probability density, using kernel density
estimation, of the CALIOP cumulative extinction height versus height from the
various algorithms. Also given are the Pearson's correlation coefficient and
root mean square error (RMSE). (e–h) The probability density, using kernel density estimation, of the difference between the passive algorithm and the CALIOP
cumulative extinction heights versus the CALIOP column extinction.
(i–l) Frequency distribution of the difference between the height
from the various algorithms and the CALIOP cumulative extinction height. The
mean and standard deviation (σ) together with the number data points
are given in each plot. This information is also provided in
Table . Data are shown for the
BIRA-IASB (a, e, i), DLR (b, f, j), LISA (c, g, k)
and LMD (d, h, l) algorithms.
Similar to Fig. but for the CALIOP
geometric mean height.
The mean ± the standard deviation of the dust height difference
between the passive sensors and CALIOP, and the number of co-located
points. The inlay is the percentage of heights that are within the CALIOP
layer. For BIRA-IASB, DLR, LISA and LMD statistics are given for all data and
subgroups of data recorded during daytime and night-time and over land and ocean. For
KNMI-GOME2 only day comparisons are possible, hence the lack of comparisons
with CALIOP night overpasses. Also note that for KNMI-GOME2 the number of
land and ocean pixels does not add up to the total due to some pixels
covering coastal regions (mixed pixels).
InstituteBIRA-IASBDLRLMDLISAKNMIIUPInstrument/algorithmIASI/MAPIRIASI/IMARSIASIIASI/AEROIASIGOME-2SCIAMACHYCALIOP day and night, cumulative extinction heights Height difference (km)0.590±1.2130.785±1.281-0.053 ± 1.3390.507 ± 1.126-0.818 ± 1.455-0.961 ± 1.708No. of points26201420748220321534Inlay (%)83.178.377.585.863.745.7CALIOP day and night, geometric mean heights Height difference (km)0.078±1.1080.243±1.181-0.607 ± 1.187-0.045 ± 1.029-1.393 ± 1.204-1.097 ± 1.574No. of points2408129670419789121Inlay (%)81.177.575.984.067.040.9CALIOP day, land, cumulative extinction heights Height difference (km)0.357±1.6650.405±1.660-0.102 ± 1.448-0.225 ± 1.454-0.229 ± 1.339No. of points605377319440117Inlay (%)58.561.070.271.468.4CALIOP day, land, geometric mean heights Height difference (km)0.087±1.572-0.044 ±1.526-0.496 ± 1.322-0.635 ± 1.357-0.893 ± 0.930No. of points59839332242550Inlay (%)57.759.870.570.178.0CALIOP day, ocean, cumulative extinction heights Height difference (km)0.783±0.9130.913±1.539-0.501 ± 1.4090.172±1.389-1.477 ± 1.296No. of points1722211818085Inlay (%)74.459.158.562.262.4CALIOP day, ocean, geometric mean heights Height difference (km)0.340±1.1870.184±1.174-0.922 ± 1.142-0.285 ± 1.187-2.015 ± 1.262No. of points1702210917034Inlay (%)72.468.255.059.458.8CALIOP night, land, cumulative extinction heights Height difference (km)0.567±1.0200.906±1.0620.073±1.0920.663±0.896No. of points15019962061226Inlay (%)91.084.892.291.2CALIOP night, land, geometric mean heights Height difference (km)0.038±0.9030.358±0.964-0.579 ± 1.0580.170±0.855No. of points13308541771064Inlay (%)89.485.489.889.6CALIOP night, ocean, cumulative extinction heights Height difference (km)1.008±0.7411.599±1.1270.352±1.1801.043±0.637No. of points34225105357Inlay (%)96.596.092.497.2CALIOP night, ocean, geometric mean heights Height difference (km)0.094±0.6780.835±0.720-0.674 ± 0.8780.152±0.486No. of points3102795319Inlay (%)95.896.391.796.9
CALIOP mean cumulative and geometric mean dust-layer height ±
standard deviation together with the dust-layer thickness ± standard
deviation. Statistics are given for the full data set and for subsets divided
into land and ocean for day and night
overpasses.
QuantityCALIOP allCALIOP land CALIOP ocean DayNightDayNightCumulative extinction height (km)2.32±1.392.75±1.772.47±1.132.07±1.501.05±0.63Geometric mean height (km)2.86±1.263.04±1.703.02±1.022.52±1.332.01±0.61Thickness (km)3.20±1.352.35±1.333.55±1.202.54±1.393.70±1.09
In the upper row of Fig. the CALIOP cumulative
extinction height is plotted against the dust heights from all IASI
algorithms for all dates. Figure is
similar but for the CALIOP geometric mean height method. Also included in the
plots are the Pearson's correlation coefficient and root mean square error
(RMSE). In the centre rows of
Figs. –
the differences between the passive algorithms and CALIOP heights are shown
against the CALIOP column extinction. In the upper and centre rows the colour
indicates the density of points. The bottom rows of
Figs. –
show the frequency distribution of the difference between the dust
heights from the various IASI algorithms and the CALIOP heights. Similar
plots for the KNMI and IUP algorithms are shown in
Fig. . For the IASI algorithms ocean-day,
ocean-night, land-day and land-night data subsets are presented in
Figs. –
for the CALIOP geometric mean heights and in
Figs. –
for the CALIOP cumulative extinction heights. The mean and standard deviation
and the number of data points are also listed in Table .
It is noted that an analysis in terms of “bias” is only correct as a mean
analysis when the difference distribution is at least symmetrical (if
not Gaussian). This is not always the case, as shown, for example, for the ocean
day subset in Fig. . Thus, while the mean
of the difference may appear good the histogram sometimes shows something
very different.
For the BIRA-IASB, LISA and LMD algorithms versus the CALIOP dust cumulative
extinction (geometric mean) height, the Pearson's correlation coefficient is
between 0.408 and 0.510 (0.414–0.518). It is smaller for the DLR, KNMI and
IUP algorithms, being -0.115 to 0.120 (-0.238 to 0.137). For the IASI
algorithms the RMSE is between 1.030 and 1.334 km when compared with
the CALIOP geometric mean dust heights. It increases to
1.235–1503 km for the CALIOP cumulative extinction dust heights. For
the KNMI and IUP algorithms the RMSE is larger, at 1.670–3.439 km.
The rather large RMSE indicates the difficulty and uncertainty involved when
comparing dust heights from very different sensors and data recorded at
different times with large differences in footprint size. There appears to be
no dependence on height differences on dust column extinction as shown in the
centre rows of
Figs. –.
For the IASI algorithms both day and night-time data are included in
Figs. –.
The mean height difference between the various algorithms and the CALIOP
heights are given in Table . The BIRA-IASB mean height
difference is 0.078 km (0.590 km) when compared with the
CALIOP geometric mean (cumulative extinction) height. The DLR algorithm mean
height difference of 0.243 km (0.785 km) is larger. However,
it is noted that the DLR algorithm generally gives the altitude at two
distinct modes (Fig. ). For LMD the magnitude of
the mean height difference is smallest when compared with the CALIOP
cumulative extinction height, -0.053 km. It increases to
-0.607 km when compared with the CALIOP geometric mean height. For
the LISA algorithm the behaviour is similar to the BIRA-IASB and DLR
algorithms with mean height differences of -0.045 km (geometric
mean) and 0.507 km (cumulative extinction). Scatter plots in
Figs. –
(upper panels) reveal rather elongated clouds of points along (parallel to)
the 1:1 straight line for BIRA-IASB and LISA with respect to the geometric
mean (cumulative extinction) heights from CALIOP, whereas the point cloud is
mainly localized below 2 km in altitude for LMD and above
2.5 km for DLR (this last one presents maxima of occurrences). The
standard deviations are similar for the BIRA-IASB, DLR, LMD and LISA
algorithms between 1.029–1.187 km (geometric mean) and
1.126–1.339 km (cumulative extinction), but are slightly lower for
LISA, intermediate for BIRA-IASB and DLR, and to some extent greater for LMD.
Due to the larger footprint size of the solar sensors, fewer data points are
available for dust height comparison of CALIOP with GOME-2 and SCIAMACHY. The
statistics for all co-located GOME-2 and SCIAMACHY with CALIOP points are
summarized in Fig. and
Table . Both algorithms give lower dust heights compared
to CALIOP, with IUP being on average lower by -1.097 km
(-0.961 km) compared to the CALIOP geometric mean (cumulative
extinction) height and KNMI lower by -1.393 km
(-0.818 km).
Similar to
Figs. –
but for the KNMI (a, e, i, c, g, k) and IUP (b, f, j, d, h, l)
algorithms versus the CALIOP cumulative extinction (a, e, i, b, f, j,)
and geometric mean (c, g, k, d, h, l) dust
heights.
The features seen in the upper rows of
Figs. – reveal
that height differences may depend on region and time of day or other
variables. It is well known that CALIOP daytime measurements are more noisy
due to stray light from the sun. Thus, to investigate possible differences
between night-time and daytime data the differences between CALIOP heights
and the passive algorithm heights were calculated separately for night and
day and also for land and ocean. For each IASI algorithm, plots similar to
those in
Figs. –
for ocean-day, ocean-night, land-day and land-night data subsets are shown in
Figs. –
for the CALIOP geometric mean heights and in
Figs. –
for the CALIOP cumulative extinction heights. The results are also summarized in Table .
For BIRA-IASB the mean difference is similar over land during the day
(0.087 km) and at night (0.038 km) when compared with the
CALIOP geometric mean height. For the cumulative extinction height the mean
difference increases from 0.357 km during the day to 0.567 km
at night over land. Over the ocean the mean difference is somewhat larger
during the day (0.340 km) than at night (0.094 km) for the
geometric mean height, while it is the opposite for the cumulative extinction
height, being 0.783 km (day) and 1.008 km (night). For DLR
few data points are available over the ocean. Over land the mean difference
is smaller for the daytime data than the night-time data, being
-0.044 km (0.405 km) and 0.358 km
(0.906 km) respectively for the geometric mean (cumulative
extinction) height. For LMD the dust heights over land are found to be
smaller than the CALIOP geometric mean (cumulative extinction) height during
the day than at night, -0.496 km (-0.102 km) versus
-0.579 km (0.073 km). Over the ocean the behaviour is
similar, but the differences are somewhat larger; see
Table . The magnitude of the mean LISA difference is
larger during the day (-0.635 km) than at night (0.170 km)
over land for the geometric mean height. For the cumulative extinction height
the behaviour is the opposite, being -0.225 km during the day and
0.663 km at night. Over the ocean similar behaviour is observed. For
nearly all comparisons the RMSE is smaller for the night-time data than the
daytime data, most likely reflecting the lower noise in the CALIOP night-time
data. These findings are further discussed and illustrated with plots in
Appendix . The KNMI-GOME-2 dust heights compare better with
the CALIOP cumulative extinction (geometric mean) dust heights over land,
with a difference of -0.229 km (-0.893 km), than over
ocean, for which it is -1.477 km (-2.015 km;
Table ).
The four dust episodes investigated may have dust with different optical
characteristics that may have an effect on the retrieved dust heights. The
comparison was therefore further subdivided into four time periods
representing the episodes. Investigations into the difference between the
height from the various algorithms and the CALIOP cumulative extinction and
geometric mean heights for the four episodes reveal no clear temporal
variations.
For the full data set mean height differences vary between -0.607 and
0.243 km (geometric mean) and -0.053 and 0.785 km
(cumulative extinction) for the IASI algorithms and -1.393 and
-1.097 km (geometric mean) and -0.961 and -0.818 km
(cumulative extinction) for the solar algorithms (Table ).
The percentage of retrieved heights from the passive sensors that are within
the dust layer as seen by CALIOP, are given in Table .
Here the CALIOP dust layer is the lowermost and uppermost
heights identified as dust. For the IASI algorithms, between 75.9 and
85.8 % of the retrieved heights are within the CALIOP dust layer. The
highest percentage is achieved by LISA at night over the ocean with up to
96.9 % (geometric mean) and 97.2 % (cumulative extinction) dust
heights located within the CALIOP dust layer (for a subset of respectively
319 and 357 points).
The average CALIOP dust-layer thickness is 2.35 km over land during
the day and 2.54 km over the ocean
(Table ). For the night the layer thickness is
3.55 km over land and 3.70 km over ocean. Thus, over land the
dust-layer thickness is larger at night than during the day by about 1.12 and
1.16 km over the ocean. The dust layer over land is about
0.680–1.42 km higher than over ocean. It is lower by
0.52–1.02 km at night than during the day over the ocean. This is
mainly caused by different regions being sampled at night-time and daytime
overpasses. Most of the concurrent IASI and CALIOP night-time data are from
the Persian Gulf and the Red Sea (lower dust height), while the daytime data
are more evenly distributed over the study area.
Discussion
We have compared dust-layer heights from various passive sensors with
CALIOP-derived heights. The CALIOP heights are considered the “true”
values. However, the CALIOP heights are not unique as described in
Sect. ; thus we have used two different CALIOP-derived
heights. For the cumulative extinction CALIOP height method, the lidar ratio
is involved. This may be different for different regions and time of year,
thus adding to the uncertainty in the comparison. The CALIOP analysis may
also misclassify aerosol as discussed by . The latter is
largely avoided in this study by focusing on dust aerosol which has a
relatively large depolarization ratio. Different methods used to calculate
CALIOP heights are compared in Fig. . The
RMSEs for the height methods are 0.652 and 0.182 km. These numbers
should be kept in mind for the comparison results presented above.
While comparable, the heights retrieved from CALIOP and IASI are not the same
quantities due to the instruments different sensitivities to various aerosol
particle sizes and the assumptions of aerosol optical properties (lidar
ratio, refractive index, particle shape) used in the retrieval. A full
understanding of the reason for the differences requires a detailed algorithm
comparison which is beyond the scope of this study. It is noted that infrared
sensors have lower sensitivity to low dust height caused by the small
temperature difference between the temperature of the surface and the
temperature of the dust. For example, for the BIRA-IASB algorithm the lowest
possible retrieval height is around 1.2 km due to low sensitivity to
dust at a lower height. For the DLR algorithm a positive bias with respect to
the CALIOP cumulative extinction height was predicted (Sect. ).
A positive bias between 0.0405 (day) and 0.906 km (night) is indeed
found over land surfaces; see Table .
Overall the standard deviation of the difference between CALIOP heights and
the passive sensor heights is smaller for the night-time data than for the
daytime data (Table ). This is most likely due to less
noise in the CALIOP night-time data. Standard deviations are generally
similar for ocean and land data, but there are differences for individual
algorithms, indicating opportunities for future improvements.
There is quite a large difference between day and night over the ocean and
all algorithms overestimate more at night than during the day over ocean
(Table ). Due to the differences in satellite overpass
times, different regions are sampled for night-time and daytime overpasses.
Most of the concurrent IASI and CALIOP night-time data are from the Persian
Gulf and the Red Sea (lower dust height), while the daytime data are more
evenly distributed over the study area. The differences seen between
night-time and daytime data may thus be caused by differences in optical
properties of the dust between the two regions, which is not accounted for by
the retrieval algorithms.
The CALIOP heights are moved to the SCIAMACHY and GOME-2 overpass times. On
average the vertical shift is small, being between 0.015 and 0.020 km
with a standard deviation of 0.25 km. For individual data points the
shift may be larger (compare shifted and unshifted black and red triangles in
Fig. ). This suggests that, when
comparing data sets from satellite sensors with different overpass times,
transport processes should be accounted for in the analysis. Moreover, the
spatial resolutions of the IASI, GOME-2 and SCIAMACHY instruments are much
coarser than CALIOP. The impact of differences in spatial resolution has not
been investigated, but it is assumed to be small within large dust clouds as
studied here.
It is not straightforward to estimate an uncertainty for the IASI height
retrievals as this would require a sensitivity study that is beyond the scope
of this work. A best-guess estimate would be that the uncertainty is of the
order of 1–1.5 km. found that for low dust
loads, the BIRA-IASB algorithm placed the aerosol layer 1–2 km above
the CALIOP-retrieved layer. The algorithm has since undergone several
revisions and improvements and the average overestimate for all data is
0.078 km (0.590 km) when compared with the CALIOP geometric
mean (cumulative extinction) height. Possible reasons for this overestimate
are discussed above. reported better agreement for
moderate to higher dust loads compared to low dust loads. In the present
study no effect of the dust load on dust height agreement appears to be
present; see centre row plots of
Figs. and
.
For monthly mean 1∘×1∘ gridded IASI data covering
the period July 2007–June 2013, reported a systematic
IASI-CALIOP bias of 0.4 km with a standard deviation of
0.48 km over the ocean. reported similar values
for the same data set. In this study for LMD over ocean, a bias of
-0.922 km (-0.501 km) against the CALIOP geometric mean
(cumulative extinction) height is found for data recorded during the day
overpasses with a standard deviation of 1.142 km (1.409 km).
For the night overpasses the differences are -0.674 km
(0.352 km) and the standard deviation 0.878 (1.180)
(Table ). One reason for the larger spread in this study
may be the use of monthly and spatially averaged data by
and , while here the comparison is
made on a pixel-by-pixel basis. Hence, extreme values are not averaged out.
Overall, for the IASI algorithms, two algorithms (BIRA-IASB and LISA) agree
better with the CALIOP geometric mean height, while LMD agrees better with
the CALIOP cumulative extinction height (Table ). The DLR
algorithm generally gives altitudes at two distinct modes but agrees better
overall with the geometric mean height. This may indicate that the IASI
algorithms do not provide the same height information. The BIRA-IASB and LISA
algorithms retrieve an aerosol profile from which dust height is calculated.
The LMD algorithm, however, uses single-layer aerosol in the retrieval, while
DLR estimates the altitude from the retrieved dust layer temperature. The
comparison with the two CALIOP heights suggests that the profile retrieval is
generally more sensitive to the actual dust-layer vertical location. Both the
BIRA-IASB and LISA algorithms use 1 km vertical steps, but with 1.5–2
degrees of freedom there is a significant correlation between the layers and
therefore a low sensitivity to the actual high-resolution vertical
distribution represented in the cumulative extinction height. Mean altitudes
from those retrievals would then be something resembling geometric mean
height. Contrary, the LMD algorithm, which places the aerosol in a single
homogeneous layer, is more sensitive to the aerosol layer radiative effective
height. It is also important to note that the sensitivity of the IASI
algorithms does not only depend on aerosol load but also on the temperature
profile.
The BIRA-IASB algorithm use CALIOP profiles as a priori which implies that
the BIRA-IASB altitude data include information about the CALIOP data to
which it is compared. However, the (monthly) a priori profile is averaged
over a large spatial area (5∘×5∘), therefore
including measurements from different days and most probably even different
dust events. Furthermore, the retrievals usually differ significantly from
the a priori profile. Thus the a priori profile used for a single retrieval
is only vaguely related to the exact CALIOP profile used for the validation.
The LISA algorithm also uses an a priori profile of dust derived from a
CALIOP climatology, but it is a unique a priori profile for all retrievals.
Therefore, it is not related to the CALIOP measurements used in the
validation.
It is noted that all IASI algorithms assume the dust particles to be
spherical. compared optical properties of spherical and
non-spherical dust particles. They found the values of the dust
single-scattering albedo to be different for spherical and non-spherical dust
particles. This may potentially affect the dust height retrieval. It is
beyond this study to investigate and quantify this effect.
The heights from the passive solar IUP-SCIAMACHY and KNMI-GOME-2 algorithms
are generally low compared with the CALIOP height
(Table ). While IASI is mainly sensitive to the aerosol
coarse mode, SCIAMACHY and GOME-2 are sensitive to both the fine and coarse
modes. The height retrieved from these sensors depends on whether the surface
albedo is retrieved simultaneously or not as shown by .
They found that fixing the albedo in the retrieval gave a lower dust height
than when retrieving both the albedo and the dust height. Fixing the albedo
also gave better agreement with lidar measurements for the 16 scenes they
analysed. made sensitivity studies for the retrieval of
aerosol height from POLarization and Directionality of the Earth's
Reflectances (POLDER) and MERIS oxygen A-band
measurements. They showed that aerosol height estimates vary with AOD,
single-scattering albedo, aerosol phase function, aerosol-layer height, and
the underlying surface albedo. For low surface albedo theoretical analysis
gave errors of about ±0.5 and ±0.2 km for POLDER and MERIS
respectively. A comparison between POLDER and CALIOP gave standard deviations
less than about 0.55 km, consistent with the theoretical analysis.
However, for parts of the three cases along the coast of Africa, the aerosol
height was underestimated by up to 1–2 km.
attributed this to either a more complex vertical aerosol structure,
including a layer near the surface, or the presence of low clouds under the
aerosol layer. The theoretical sensitivity results of
was confirmed by , whose modelling sensitivity study
indicated aerosol heights within ±0.5 km if the aerosol
single-scattering albedo used in the retrieval deviated by less then 0.01
from the actual value of 0.99. They also reported that the error increased
with dust-layer-top height.
It must be stressed that the reported errors on dust height, based on
synthetic sensitivity studies, are estimated with the assumption that either
AOD, the optical model or the surface reflectance are perfectly known
beforehand or are derived from independent instrument channels. For instance,
make first use of the official POLDER and MERIS AOD
products, while focusing on dark ocean surfaces only. Then, they find the
most accurate aerosol model (pure dust or a mixture with sea salt or biomass
burning) by perturbing the reflectance in a non-absorbing channel.
Conversely, the solar algorithms of this work have been designed to fit the
oxygen spectrum to concurrently infer dust height and optical thickness
together, so that AOD and height uncertainties cannot be decoupled and
deviations of the assumed optical model and climatological surface
reflectivity from the actual ones contribute to the overall error budget. As
such, the evaluation of the presented dust cases can be regarded as a more
comprehensive test bed for operational dust height retrievals.
combined oxygen A and B-band measurements from the Earth
Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory
(DSCOVR) to retrieve AOD and height, finding that 71.5 and 98.7 % aerosol
heights were respectively within ±0.5 and ±1.0 km envelopes when
compared to CALIOP for two overpasses of Saharan dust events over water only.
Their reported rms error, in this case, amounts to 0.45 km, pointing
to the advantage of adding the information concealed in the B-band to the
retrieval .
Generally, we found the IUP-SCIAMACHY and KNMI-GOME-2 algorithm retrieved
heights to be lower by -1.097 km (-0.961) and -1.393 km
(-0.818) respectively when compared with CALIOP geometric mean (cumulative
extinction) heights. For the KNMI-GOME-2 algorithm the underestimate is
larger over ocean -2.015 km (-1.477) than over land
-0.893 km (-0.229; Table ). These differences
are larger than those reported in the above-cited studies. Still, it is found
that for KNMI-GOME-2 (IUP-SCIAMACHY) between 63.7–67–0 %
(40.9–45.7 %) of the aerosol heights are within the CALIOP aerosol
layer. For KNMI more of the retrieved heights are within the CALIOP aerosol
layer over land than ocean (Table ).
In general, possible reasons for the underestimation of layer height by the
solar sensors are the local optical dust properties and the surface
reflectivity assumed in the forward model. While it has been already
demonstrated that a positive deviation of the true surface albedo from the
assumed prior value leads to an underestimation of layer height
, the similar tendency of lower retrievals by GOME-2 and
SCIAMACHY suggests that the influence of a wrongly prescribed aerosol model
can be ruled out. This is because the KNMI/GOME-2 algorithm uses a
Henyey–Greenstein phase function, whereas IUP-SCIAMACHY ingests spectrally
resolved T-matrix calculations of the phase matrix representing aspherical
dust particles (see Table ).
To this end, we note that the algorithms of the solar spectrometers assume
that satellite pixels are fully covered by dust. Because of their coarse
footprint sizes, this condition frequently cannot be satisfied. The EPIC
pixel size is 12×12km2 compared to
80×40km2 for GOME-2 and 30×60km2
for SCIAMACHY. Thus the results of are likely to be less affected
by cloud contamination and aerosol inhomogeneities. A situation of partially
aerosol-covered pixels implies that fewer oxygen molecules are shielded by the
intervening scatterers, with the effect of increasing absorption inside the
A-band sensed by the instruments. This effect is even more pronounced closer
to the ground, where the majority of oxygen molecules reside. Since most of
the information content on the height of the aerosol layer is carried by the
in-band wavelengths of the A-band (about 760 nm) ratioed to the
continuum (758 nm), it can be deduced that a dust pixel fraction
smaller than 1 will lead to an additional underestimation of layer
height.
We end the discussion by listing several questions left open by this study.
These questions may broadly be divided into two sets: (1) questions requiring
analysis of a larger data set to consolidate findings and (2) questions
requiring a more detailed analysis to better understand the reasons for the
differences. Some specific open questions are as follows:
How will the results change when including other types of aerosol
in the analysis?
How will a larger data set in time and space affect the results?
Could an optimal aerosol height algorithm covering all situations be
developed?
What are the physical reasons for the differences between the
IASI algorithms?
What are the physical reasons for the differences between the
solar algorithms?
What are the physical reasons for the differences between the
quantities estimated by the IR and solar algorithms?
How may synthetic data be used to understand and evaluate the
various algorithms?
Which pixel-level uncertainties can we estimate to the layer height
results of each algorithm (based on studies 2a–2d)?
Conclusions
As part of the ESA Aerosol_cci project dust
aerosol heights retrieved from passive infrared and solar sensors using
different algorithms have been compared with two different CALIOP-derived
dust-layer heights. The comparison was made on a pixel-by-pixel basis for the
IASI, GOME-2 and SCIAMACHY sensors for four dust episodes in 2010. Time
differences between the overpass of CALIOP and the passive sensors were
accounted for by shifting the CALIOP heights to the location of the pixels of
the passive sensors using the FLEXTRA trajectory model.
As it is not possible to construct a unique dust-layer height from CALIOP
data, two CALIOP-derived layer heights were used: the cumulative extinction
height, which is set to the height at which the CALIOP extinction column is half
of the total extinction column, and the geometric mean height, which is
defined as the geometrical mean of the top and bottom heights of the dust
layer.
Four algorithms (BIRA-IASB, DLR, LMD, LISA) retrieved dust heights from IASI
spectra. The mean difference between the IASI heights and the CALIOP
geometric mean (cumulative extinction) heights was found to vary between
-0.635 and 0.087 km (-0.225 and 0.405 km) over land
during the day. For night-time overpasses the values were between -0.579 and
0.358 km (0.073 and 0.906 km). Over the ocean day differences
were between -0.922 and 0.340 km (-0.501 and 0.913 km)
and night-time differences were between -0.674 and 0.835 km (0.352 and
1.599 km). Standard deviations were between 1.322 and 1.572 km
(1.448–1.665 km) over land during the day and decreased to
0.855–1.058 km (0.896–1.092 km) at night. Over the
ocean the standard deviation decreased from 1.142 to 1.187 km
(0.913–1.539 km) during the day to 0.486–0.878 km
(0.637–1.180 km) at night.
Two of the IASI algorithms (BIRA-IASB and LISA) were found to agree better
with the CALIOP geometric mean height (BIRA-IASB: 0.078 km,
cumulative extinction 0.590 km; LISA: -0.045 km,
0.507 km), while the LMD algorithm agreed better with the CALIOP
cumulative extinction height of -0.053 km (geometric mean:
-0.607 km). This is believed to be caused by the differences in the
aerosol profile used for the radiative transfer simulations: BIRA-IASB and
LISA use and retrieve vertically extended and resolved profiles, while LMD
place all the aerosols in one single homogeneous layer.
Far fewer data points were available for the solar sensors due to their
larger pixel size and lack of night-time data. The heights retrieved from the
solar sensors on average underestimate the CALIOP geometric mean (cumulative
extinction) heights by -1.393 km (-0.818 km) (KNMI,
GOME-2) and -1.097 km (-0.961 km) (IUP, SCIAMACHY). This
may be caused by the large pixel size and the assumption in the retrieval
that the pixels are fully covered by aerosol.
The IASI instrument was first flown in 2006 and was the first of several to
be launched. Thus data from IASI have the potential to provide long global
time series of ECVs. There is considerable variation between the
IASI-retrieved dust heights. Nevertheless, if careful consideration is taken
for differences in temporal and spatial characteristics of the observations,
it might be feasible to construct a global data set of quality-controlled
IASI-retrieved heights against CALIOP. The quality control will allow
uncertainties on a pixel-by-pixel basis, which again may be used for
sensitivity studies. This dust height data set may be used to further our
understanding of dust on the climate system. However, several open questions
should be answered to have a better understanding of the quantities measured
and their accuracy. A list of open questions are given at the end of the
discussion section and includes both studies requiring large data sets and
time periods, and studies looking at algorithm specifics.
Finally, the various algorithms and instruments are different in their
approaches to retrieving dust height. In the comparison with CALIOP no
single algorithm is found as the best overall. Different
methodologies may give best results in different locations and situations.
Thus it seems fruitful to continue the development of all algorithms and
encourage comparison exercises.
All data are available to registered users from
http://www.icare.univ-lille1.fr/. The FLEXTRA model is
available from https://www.flexpart.eu/.
Additional figures
In
Figs. –
statistics are shown for all data, and land-day, ocean-day, land-night and
land-day subsets for all IASI dust height retrieval methods compared with
the CALIOP geometric mean heights.
Figures –
show similar data but using the CALIOP cumulative extinction heights.
The plots in
Figs. –
reflect the findings presented in Table . The BIRA-IASB
algorithm agrees well with the CALIOP geometric mean height over land for day
and night (Fig. ). Over ocean the
agreement is better at night. It is noted that for the ocean-day
subset the histogram is bimodal. When compared with the cumulative extinction
height (Fig. ), the BIRA-IASB dust height is
overestimated over ocean during both day and night; the ocean day subset
appears to be bimodal; and the agreement appears to be better over land during the day than
at night, but this may in part be due to a bimodal histogram for the land day
subset. This is reflected in the spread in the difference, which is smaller
at night than during the day. For DLR (Figs.
and ) there are few data points available
over the ocean. Over land the DLR height data are clumped at a single height
for the day subset and at two heights for the night-time data subset. For LMD
(Figs.
and ) the agreement is mono-modal for the
land day, land night and ocean night subsets when compared with both the
CALIOP cumulative extinction and geometric mean heights. For the ocean day
subset a bimodal distribution may be present. Overall the agreement is better
when compared with the cumulative extinction height. The LISA data
(Figs.
and ) also have a bimodal ocean day
distribution compared with the CALIOP heights. For the ocean the mean
difference with the CALIOP cumulative extinction height is significantly
larger at night than during the day. This difference is nearly a factor of 2 smaller
when compared with the geometric mean height. For land the magnitude of the
difference is smallest when compared with the cumulative extinction height
during the day and with the geometric mean height at night.
(a–e) Scatter plots of the CALIOP geometric mean height
versus height from the BIRA-IASB algorithm. (f–j) Scatter plot of
the difference between the BIRA-IASB and CALIOP heights versus the CALIOP
column extinction. (k–o) Frequency distribution of the difference
between the height from the BIRA-IASB algorithm and the CALIOP height. The
mean and standard deviation of the normal distribution are given in each
plot. (a, f, k) All data points. (b, g, l) CALIOP daytime data
and IASI land pixels. (c, h, m) CALIOP daytime data and IASI ocean
pixels. (d, i, n) CALIOP night-time data and IASI land pixels.
(e, j, o) CALIOP night-time data and IASI ocean
pixels.
Similar to Fig. but for the
DLR algorithm.
Similar to Fig. but for the
LMD algorithm.
Similar to Fig. but for the
LISA algorithm.
(a–e) Scatter plots of the CALIOP cumulative extinction
height versus height from the BIRA-IASB algorithm. (f–j) Scatter
plot of the difference between the BIRA-IASB and CALIOP heights versus the
CALIOP column extinction. (k–o) Frequency distribution of the
difference between the height from the BIRA-IASB algorithm and the CALIOP
height. The mean and standard deviation of the normal distribution are given
in each plot. (a, f, k) All data points. (b, g, l) CALIOP
daytime data and IASI land pixels. (c, h, m) CALIOP daytime data and IASI
ocean pixels. (d, i, n) CALIOP night-time data and IASI land pixels.
(e, j, o) CALIOP night-time data and IASI ocean
pixels.
Similar to Fig. but for the DLR
algorithm.
Similar to Fig. but for the LMD
algorithm.
Similar to Fig. but for the LISA
algorithm.
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the European Space Agency as part of the
Aerosol_cci project (ESA contract no. 4000109874/14/I-NB). Comments on the
manuscript by Andreas Stohl is greatly appreciated. Thanks to Sabine Eckhardt
for help with running the FLEXTRA model. The anonymous referees are thanked for their constructive
comments.
Edited by: Alexander Kokhanovsky
Reviewed by: three anonymous referees
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