We present LIVAS (LIdar climatology of Vertical Aerosol
Structure for space-based lidar simulation studies), a 3-D multi-wavelength global aerosol and cloud
optical database, optimized to be used for future space-based lidar
end-to-end simulations of realistic atmospheric scenarios as well as
retrieval algorithm testing activities. The LIVAS database provides averaged
profiles of aerosol optical properties for the potential spaceborne laser
operating wavelengths of 355, 532, 1064, 1570 and 2050
A general methodology to test the ability of candidate future spaceborne
remote-sensing instruments to observe atmospheric quantities is the
application of their processing algorithms on simulated data sets. The
data sets are usually based on the instrument characteristics and a
description of the atmospheric state. Especially for active remote sensors
as lidars, the vertical dimension should be included in the simulations.
Global distributions of such data are available today due to the launch of
the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on
board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations
(CALIPSO) mission of NASA/CNES in June 2006 (Winker et al., 2009). Ever
since, CALIPSO provides global aerosol and cloud vertical distributions to
the scientific community through analysis of CALIOP backscatter observations
at the operating wavelengths of 532 and 1064
The technique of active remote sensing of the atmosphere by lidar has been
also chosen for two of the future ESA Earth Explorer Missions, namely the
Atmospheric Dynamics Mission Aeolus (ADM-Aeolus; Stoffelen et al., 2005) and
the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE; ESA, 2004; Illingworth et al., 2014), and was further proposed for the
Advanced Space Carbon and Climate Observation of Planet Earth (A-SCOPE), one
of the candidates for the 7th Earth Explorer mission. Atmospheric Laser
Doppler Instrument (ALADIN) on board ADM-Aeolus and ATmospheric LIDar
(ATLID) on board EarthCARE are two high spectral resolution lidars (HSRLs)
operating at 355
The ESA Reference Atmosphere Model (RMA) currently used for the design and
the performance validation of ALADIN and ATLID instruments is derived from
airborne lidar measurements performed at 10.6
Due to its spatial restrictions, the current ESA RMA is not representative
for global simulations. The correct performance assessment of current and
future ESA lidar instruments requires the development of a refined aerosol
and cloud optical database with high spatial resolution for the planetary
boundary layer (PBL), the free troposphere (FT) and the stratosphere. An
appropriate RMA should be representative of both statistical atmospheric
information (i.e., per atmospheric region, climate zone and season) and
deterministic information (i.e., extended atmospheric scenes with, e.g.,
Saharan dust events, biomass-burning aerosol events, volcanic eruption
events, polar stratospheric cloud events, convective cloud events).
Moreover, the RMA should include multi-wavelength parameters so as to cover
the spectral domain of future HSRL and IPDA lidar missions, specifically the
three harmonic operating wavelengths of Nd:YAG (neodymium-doped yttrium aluminium garnet) lasers (355, 532 and 1064
Over the recent years, the European Aerosol Research Lidar Network
(EARLINET,
In this paper we present the LIdar climatology of Vertical Aerosol
Structure for space-based lidar simulation studies (LIVAS), which is a RMA
aiming to provide profiles of aerosol and cloud optical properties on a
global scale that can be used for the simulation of realistic atmospheric
scenarios in current and future lidar end-to-end simulations and retrieval
algorithm testing activities. For HSRL and IPDA lidar applications, LIVAS
addresses the wavelength dependency of aerosol optical properties for the
following laser operating wavelengths: 355, 532, 1064
CALIOP, the principal instrument on board the CALIPSO satellite, part of the
NASA A-Train, is a standard dual-wavelength (532 and 1064
After calibration and range correction, cloud and aerosol layers are
identified and aerosol backscatter and extinction at 532 and 1064
EARLINET has been operating since 2000 and aims at establishing a quantitative and comprehensive database for the aerosol vertical, spatial and temporal distribution of aerosols on the European continental scale (Pappalardo et al., 2014). To date, EARLINET includes 27 stations in 16 countries performing lidar observations on a regular schedule of one daytime measurement per week around noon and two nighttime measurements per week with low background light in order to perform Raman extinction measurements (see Table 1 in Pappalardo et al., 2014). The first volumes of the EARLINET database have been published in biannual volumes at the World Data Center for Climate (The EARLINET publishing group 2000–2010, 2014a, b). In addition to the routine measurements, further observations are devoted to monitor special events such as Saharan dust outbreaks, forest fires and volcano eruptions (The EARLINET publishing group 2000–2010, 2014d, e). Moreover, since 14 June 2006 EARLINET has carried out collocated measurements with CALIPSO during nearby overpasses, following a strategy defined on the basis of the ground-track data analysis provided by NASA (Pappalardo et al., 2010; The EARLINET publishing group 2000–2010, 2014c).
EARLINET operation is coordinated such as to ensure instrument standardization and consistent retrievals within the network. This harmonization is achieved through the application of a rigorous quality-assurance program addressing both instrument performance (Matthias et al., 2004; Freudenthaler et al., 2010) and evaluation of the algorithms (Böckmann et al., 2004; Pappalardo et al., 2004).
The 14-year EARLINET database contains a large data set of the aerosol lidar ratio retrieved from simultaneous and independent lidar measurements of aerosol extinction and backscatter coefficients. Moreover, this multi-wavelength database facilitates the retrieval of extinction and backscatter spectral dependence for different aerosol types after a proper layer identification and characterization. The lidar ratio is of fundamental importance for the estimation of aerosol extinction from pure backscatter lidar measurements such as those conducted by CALIOP, and the extinction and backscatter spectral dependence is valuable for the spectral conversions between laser wavelengths.
AERONET (
The data and methods used for the derivation of LIVAS BAEs and EAEs in the UV and IR spectral ranges.
In this section we describe the methods developed for the derivation of the
multi-wavelength LIVAS database. LIVAS was developed based on CALIPSO
observations at 532 and 1064
An overview of the data and methods followed for the derivation of the aerosol-type-dependent BAEs and EAEs is schematically illustrated in Fig. 1 and described in Sect. 3.1. The methodology for the spectral conversion of the CALIPSO Level 2 product is demonstrated through an example presented in Sect. 3.2. The section closes with the description of the processing chain followed for quality filtering and averaging the CALIPSO observations, given in Sect. 3.3.
LIVAS aerosol model.
For the derivation of the BAEs and EAEs we used different methods and
data sets for the UV and IR spectral regions: BAEs and EAEs for the 532 to
355
The construction of representative size distributions and refractive indexes
corresponding to the CALIPSO aerosol types is not a straightforward task.
The ones used to estimate the optical properties of each type in the CALIPSO
classification scheme are retrieved by clustering AERONET data in
respective categories/aerosol types, as described in Omar et al. (2005,
2009). Although this CALIPSO aerosol model is assumed to correspond to the
independently derived CALIPSO aerosol types, this is not true for all cases,
mainly due to the different nature of AERONET sun photometer measurements
versus CALIPSO lidar measurements used for the categorization. The main
difference is that the sun photometer is incapable of providing measurements
at the backscattering angle of 180
A different point that needs to be highlighted for LIVAS conversions is that the CALIPSO classification used for the aerosol-type-dependent conversions possibly introduces some uncertainty in the LIVAS final product, due to inconsistencies with the observed aerosol types. CALIPSO classification is based on a threshold algorithm that takes into account the layer-integrated attenuated backscatter coefficient and an approximate particulate depolarization ratio as well as the surface type (either land or ocean; Omar et al., 2009). However, these properties do not provide all the information needed for unambiguously classifying the aerosol type and, as a result, misclassifications occur frequently (e.g., Burton et al., 2013). Since for LIVAS we need to calculate BAEs and EAEs assuming that the CALIPSO aerosol types are representative of the aerosols observed, any inconsistencies in the CALIPSO classification scheme introduce inaccuracies in our results.
Summarizing, LIVAS BAEs and EAEs were measured from EARLINET for the UV–VIS conversion and they were calculated for the VIS–IR conversion. For the latter we employed characteristic size distributions and refractive indexes from AERONET data classified into the respective aerosol types using different approaches and further validated using EARLINET measurements. Moreover, for aerosol types that are not probed by either EARLINET or AERONET (e.g., marine), we utilized typical properties from the Optical Properties of Aerosols and Clouds (OPAC) model (Hess et al., 1998) or other aerosol models from the literature. An elaborated description of our methodology for the UV–VIS and VIS–IR spectral regions is given in Sect. 3.1.1 and 3.1.2, respectively.
For the conversion of CALIPSO aerosol backscatter and extinction from 532 to
355
BAE and EAE for each aerosol type used in LIVAS for the
conversion from 532 to 355
For the derivation of the UV–VIS (355 from 532
The EARLINET measurements included in ESA-CALIPSO regarding clean marine, clean continental and stratospheric aerosol particles were limited for a reliable statistical analysis. The calculation of BAEs was possible, but for EAEs this was not the case (mainly due to Raman lidar constraints regarding the overlap that prohibits extinction retrievals for lower marine atmospheric layers and regarding inadequate Raman returns from the stratosphere). For the aforementioned types, aerosol models provided in the literature were used in order to calculate the EAEs. Specifically, we used the maritime model introduced in Sayer et al. (2012) for clean marine aerosols, the OPAC model for clean continental aerosols and the stratospheric model of Wandinger et al. (1995) and Deshler et al. (1993) for stratospheric aerosols. From these models, typical size distributions and refractive indexes were retrieved and the BAEs and EAEs were calculated via the application of the Mie theory (Mie, 1908; Van de Hulst, 1957). The results are provided in Table 2 (left column).
ESA-CALIPSO is mainly limited to the VIS–UV spectral region. For the VIS–IR
conversions in LIVAS, we used typical size distributions and refractive
indexes for each aerosol type derived from AERONET data or models, i.e., OPAC
or other aerosol models in the literature. Scattering simulations were then
applied for each aerosol type for the complete spectral range of LIVAS
(i.e., 355, 532, 1064, 1570, 2050
The different approaches for the derivation of the typical microphysical properties in the LIVAS aerosol model are described in the following.
For the scattering calculations, the well-known Mie code (Mie, 1908; Van de
Hulst, 1957) was applied for all the aerosol types except the non-spherical
particles of dust and polluted dust, where the T-matrix code and the
geometric-optics–integral-equation technique were utilized instead. More
specifically, for the non-spherical scattering calculations we employed the
code of Dubovik et al. (2006), which utilizes the T-matrix method for
particles of size parameter (
BAEs (upper) and EAEs (bottom) calculated with different approaches (i.e., AERONET-Omar (red triangles), AERONET-CALIPSO (green triangles), Sayer et al. (2012) (cyan triangles), OPAC (pink triangles)) and validated against the ESA-CALIPSO BAEs and EAEs in VIS and UV spectral ranges (black circles). The BAEs and EAEs selected and ingested in the LIVAS aerosol model for the VIS–IR conversions, are denoted with symbols of larger size.
It should be highlighted here that for this method there was no distinction between spherical and non-spherical aerosol types, instead all types were considered to contain both spherical and non-spherical particles, in accordance with the AERONET product. The calculations for the spherical part were performed with the Mie code and for the non-spherical part with the Dubovik et al. (2006) code, following the methodology described above. For each type, all the collocated cases were averaged and from those measurements we derived the average values of BAEs and EAEs.
The data set was not constrained with ESA-CALIPSO as in the AERONET-Omar approach for the UV–VIS wavelengths. This was due to the fact that the specific approach aimed to deliver typical BAEs and EAEs for the aerosol types classified by the CALIPSO classification scheme itself; thus, no correspondence to the nature of the atmospheric aerosol loads was required.
As already mentioned, LIVAS BAEs and EAEs need to be consistent with ESA-CALIPSO, a reference database of measured lidar-related aerosol properties. While the UV–VIS BAEs and EAEs were derived directly from the ESA-CALIPSO database, the VIS–IR BAEs and EAEs were calculated using the data sets and methods described in Sect. 3.1.2. To ensure consistency of our calculations with measured data, for each aerosol type we selected the VIS–IR methodology that provided compatible results with the ESA-CALIPSO for the UV–VIS BAEs and EAEs. In this way we ensured the best possible consistency of BAEs and EAEs for the entire spectral range.
Comparison of the mean volume–size distributions for each aerosol type in the LIVAS (blue line) and CALIPSO (pink line) aerosol models.
Comparison of the mean real part of the refractive index for each aerosol type in the LIVAS (blue line) and CALIPSO (pink line) aerosol models.
Comparison of the mean imaginary part of the refractive index for each aerosol type in the LIVAS (blue line) and CALIPSO (pink line) aerosol models.
Comparison of the mean spectral SSA for each aerosol type in the LIVAS (blue line) and CALIPSO (pink line) aerosol models.
Comparison of LIVAS and CALIPSO with ESA-CALIPSO values
for BAE at 355/532
Our final results are presented and discussed herein: Fig. 2 shows the calculated BAEs and EAEs using all the approaches described in Sect. 3.1.2 and their comparison with ESA-CALIPSO at UV–VIS. The selected approach for each aerosol type is denoted in Fig. 2 with large size symbols. Starting from the AERONET-Omar approach, we found that it performed better when compared to ESA-CALIPSO for the polluted continental type, resulting in a very good agreement for the EAE and best performance regarding the BAEs. For the other types this approach reproduced well the EAEs but the BAEs could not be reproduced such as to fit the ESA-CALIPSO acceptable range of values. Dust and polluted dust aerosols are most likely classified correctly by CALIPSO due to its polarization sensitivity (e.g., Burton et al., 2013; Amiridis et al., 2013). For this reason, we chose the AERONET-CALIPSO approach for the calculation of their BAEs and EAEs. The approach showed a relatively better agreement with ESA-CALIPSO compared to the AERONET-Omar approach, especially for the BAEs, maybe due to better filtering of the AERONET data used in the calculations for the AERONET-CALIPSO approach (Fig. 2). Overall though, we believe that the discrepancies in backscatter spectral dependence observed for most of the aerosol types are most likely due to the fact that AERONET lacks the capability to directly measure in the backscattering direction. Comparisons found in the literature between Raman-lidar-measured and photometer-retrieved lidar ratios support this argument (e.g., Mueller et al., 2007).
Moreover, it should be noted that the evaluation of the retrieved values with ESA-CALIPSO for polluted dust is only indicatory. This is because CALIPSO assumes the same properties for any kind of dust mixture (e.g., dust–smoke, dust–marine) while ESA-CALIPSO shows that the optical properties are highly variable for different dust mixtures. Specifically, ESA-CALIPSO provides intensive properties for mixtures of dust with polluted continental, smoke and marine aerosol separately and what we used here in order to compare with CALIPSO is an average of these properties.
For smoke aerosols the AERONET-CALIPSO approach showed similar results as AERONET-Omar, performing well for EAE but failing to reproduce the ESA-CALIPSO BAEs (Fig. 2). For this aerosol type we used the calculated BAEs and EAEs from the AERONET-CALIPSO approach for LIVAS conversions. This decision was based on the fact that the classification of smoke by CALIPSO is the most uncertain compared to the other aerosol types, as reported by Burton et al. (2013). The authors of this study reported a percentage agreement of 13 % for smoke classification when comparing with airborne HSRL classification results. Smoke misclassification was also found to be the reason of the discrepancies between CALIPSO and AERONET reported in Schuster et al. (2012) in terms of AOD measurements. These findings indicate that the CALIPSO smoke classification may not correspond to real smoke presence. Thus, it may not be comparable with real smoke detections by EARLINET in ESA-CALIPSO. This is because the ESA-CALIPSO classification model is based on source–receptor analysis based on model simulations of air mass advection over the stations, together with the aerosol optical properties measured by the lidar. Thus, for the smoke type we avoided using the ESA-CALIPSO smoke statistics.
For clean marine and clean continental aerosol, the ESA-CALIPSO database does not contain an adequate number of measurements to provide statistically significant averages. Thus, for clean marine aerosol we used the size distribution and refractive index provided in the maritime model of Sayer et al. (2012) and for clean continental we used the ones provided in the OPAC database. Note that the size distribution and refractive index for clean continental aerosol from the OPAC database were considered at ambient conditions of 70 % relative humidity.
Finally, for the stratospheric aerosol type we used the model introduced in Deshler et al. (1993) and Wandinger et al. (1995). BAEs and EAEs were found to be in good agreement with ESA-CALIPSO values (not shown in Fig. 2).
The final aerosol-type-dependent VIS–IR BAEs and EAEs used in LIVAS are presented in the right side of Table 2 for extinction (ext) and backscatter (bsc). Overall, as seen in Fig. 2, LIVAS is compatible with ESA-CALIPSO in the VIS–UV spectral region regarding EAEs. However, the agreement with regard to the VIS–UV BAEs is not that satisfactory. For the BAEs and EAEs in the IR, another point of concern could be the extrapolation of the refractive index at the longer wavelengths, since this information is not provided from AERONET.
LIVAS and CALIPSO LR, SSA and effective radius.
The microphysical properties used for calculating the VIS–IR BAEs and EAEs
are compared in this section with the ones of the CALIPSO aerosol model (Omar et
al., 2005, 2009). Figure 3 shows the comparison of LIVAS versus CALIPSO size
distributions for each aerosol type, while Figs. 4, 5 and 6 show the
spectral dependence of the complex refractive index and the SSA,
respectively, at LIVAS wavelengths for the two models. Figure 7 shows the
BAE and EAE at 355/532
In Fig. 3 the best agreement between the LIVAS and the CALIPSO model size distributions is found for the polluted continental type. For smoke particles the CALIPSO model considers the same volume for fine and coarse particles, whereas the LIVAS model presents a domination of the fine mode. The latter agrees well with the averaged size distribution of smoke type provided in the Dubovik et al. (2002) AERONET 8-year climatology and is considered more typical as it is supported by other studies as well (Reid et al., 2005; Eck et al., 1999, 2003). For the dust type, the LIVAS size distribution has fewer fine particles than the CALIPSO model, in agreement with the AERONET climatology of Dubovik et al. (2002) and findings of experimental campaigns dedicated to mineral dust characterization (e.g., McConnell et al., 2008; Weinzierl et al., 2009; Müller et al., 2011; Toledano et al., 2011). For the polluted dust type both models seem to fall within the range of the large variability reported in the literature for dusty mixtures (Eck et al., 1999; Jung et al., 2010). The more pronounced fine mode in the LIVAS model resembles the size distributions of dust and pollution mixtures (Kim et al., 2007). However, an extensive discussion on the polluted dust type is avoided here since there is no clear definition of the non-dust components for this type in the CALIPSO model. LIVAS size distribution for the clean marine type is based on the maritime model of Sayer et al. (2012). Similar size distributions for marine particles are provided in other studies as well (e.g., Dubovik et al., 2002; Smirnov et al., 2002). The largest disagreement is seen for the clean continental type. We believe that the pronounced fine mode in the LIVAS size distribution from OPAC is due to the hygroscopic growth of the hydrophilic fine particles in ambient relative humidity of 70 %. However, the clean continental type in global CALIPSO records has a contribution on the order of 2 %, making this type of less importance for the LIVAS database. However, for the aerosol model, a better definition of the aerosol components of this type should be considered.
Regarding the differences on the refractive index assumed by the LIVAS and
CALIPSO aerosol models, these are presented in Figs. 4 and 5,
respectively, for the reader's reference. We also present a comparison of
the LIVAS and CALIPSO SSA in Fig. 6. The comparison shows an overall
disagreement in the SSA for the two aerosol models. We should note here that
Omar et al. (2009) provide the refractive index values at 532 and
1064
In Fig. 7, a final comparison between ESA-CALIPSO, LIVAS and CALIPSO is
given in terms of BAE and EAE, lidar ratio at 532
We need to highlight here that our focus is evaluating LIVAS BAE and EAE consistency with the ESA-CALIPSO measurements. The lidar ratio and effective radius are not used in generating the LIVAS database and are only provided here for reasons of completeness. We should make a comment though about the large LIVAS dust lidar ratio, which may be an artifact due to the aspect ratio distribution used in the non-spherical particle scattering calculations. As shown in the recent paper of Koepke et al. (2015), in order to reproduce successfully the dust optical properties, the aspect ratio distribution needs to change with particle size. This is something that indicates that more work is needed to develop a dust model oriented for spaceborne lidar applications.
Concerning the BAEs and EAEs at 355/532
Depolarization spectral conversions were not applied in LIVAS since
multi-wavelength depolarization measurements are rare and available only
during experimental campaigns (Freudenthaler et al., 2009; Groß et al.,
2011a, b); thus, the data set was not considered statistically significant. A
single-wavelength depolarization database is provided in LIVAS using CALIPSO
Level 2 particle depolarization ratio averages at 532
Furthermore, a global cloud database is given based on CALIPSO observations
at 532
In addition, a database for the stratospheric features detected by CALIPSO
is provided, separated to cloud and aerosol features. Specifically, the
stratospheric features detected by CALIPSO were separated in polar
stratospheric clouds and stratospheric aerosols using the temperature
threshold technique proposed by Pitts et al. (2009). In brief, we classified
the stratospheric features as polar stratospheric clouds (PSCs) for
temperatures lower than 198
Finally, a set of selected scenes of specific atmospheric phenomena (e.g., dust outbreaks, volcanic eruptions, wild fires, polar stratospheric clouds) was produced. BAEs and EAEs for the selected scenes were delivered after thorough investigation of each case study, based on CALIPSO-collocated ground-based measurements that are reported in the literature. Whenever this was not possible (as for the IR conversion), the LIVAS BAEs and EAEs were used.
CALIPSO Level 2 extinction coefficient profile at
532
The obtained aerosol-type-dependent BAEs and EAEs for UV–VIS and VIS–IR were
applied to the CALIPSO Level 2 product at 532
For the production of the final LIVAS products, we used the methodology
developed by the CALIPSO team for the Level 3 aerosol product, as described
in Winker et al. (2013). Our algorithm was tested for reproducing the
CALIPSO Level 3 product, which is an aggregation onto a global
As input to the averaging algorithm, we used the Version 3 CALIOP Level 2
aerosol profile product, applying quality screening prior to averaging, to
eliminate samples and layers that were detected or classified with very low
confidence or that contained untrustworthy extinction retrievals. In brief,
the filters concerned the cloud-aerosol discrimination (CAD) score,
extinction quality control (QC) flag, aerosol extinction uncertainty,
isolated 80
In the CALIPSO Level 3 product, four types of products were generated each
month, depending on sky condition and temporal coverage, and were separated
into day/night segments. In LIVAS, only the “combined” product was used
(Winker et al., 2013) in order to achieve a better quality of the aerosol
data set regarding cloud discrimination and measurement accuracy. Moreover,
beyond the mean extinction profiles for the total aerosol load, LIVAS
provides mean extinction profiles at 532
Schematic diagram of LIVAS processing chain.
LIVAS aerosol extinction products. Upper panel: vertical
distribution of the averaged aerosol extinction coefficient at 355, 532,
1064, 1570, and 2050
Additional LIVAS products. Upper panel: vertical
distribution of the averaged particle depolarization at 532
The final LIVAS aerosol/cloud database contains multi-wavelength 4-year
averaged vertical distributions and statistics for a global grid of
In the upper panel of Fig. 10 the aerosol extinction is given for the
LIVAS lidar wavelengths, i.e., 355, 532, 1064, 1570, 2050
Additional LIVAS products are provided for particle depolarization at 532
Finally, for each grid cell a number of statistical parameters are provided
in LIVAS regarding the mean, minimum and maximum surface elevations, the
number of overpasses for each cell, the number of examined profiles, the
samples averaged after filtering (total, aerosol, clear air), the subtype
occurrence in the aerosol total observations (in percentages) and the AOD at
532
Spatial distribution of the 532
Example of high-slope terrain on CALIPSO overpasses for the case of the ND_Marbel_Univ AERONET station. Left panel: vertical distribution of the averaged aerosol extinction coefficient. Right panel: number of observations used in averaging.
Percentiles of the number of overpasses in LIVAS global grid cells.
Spatial distribution of the 532
Upper panel: scatter plot comparisons of LIVAS AODs at
532
The LIVAS web portal.
Global distribution of LIVAS AOD at 532
In this section an evaluation of the LIVAS climatological AOD mean values at
532
Large elevation differences may cause large AOD biases since in such cases
the optical path lengths monitored by AERONET and CALIPSO instruments can
vary. Moreover, when CALIPSO overpasses high-slope terrains, the sampling
may become inadequate for heights lower than the maximum elevation. An
example of such a case is given in Fig. 13 for the AERONET station of
ND_Marbel_Univ in the Philippines. CALIPSO
overpasses this station over elevations ranging from 0 to 1.46 The elevation difference between the AERONET site and CALIPSO mean ground
track elevation was kept below 100 The elevation slope in the CALIPSO overpass was constrained to be less than
400 CALIPSO sampling was controlled by constraining the comparison over grid
cells with a large number of overpasses, i.e., over 150.
The third constraint is considered crucial for the representativeness of the
LIVAS database. As shown in Fig. 14, in approximately 30 % of the global
Figure 15 presents the absolute bias of the means for our constrained
data set (i.e., LIVAS mean AOD
We have to mention here that the LIVAS validation presented in Fig. 15
cannot be conclusive on the aforementioned possible issues. Overall, the
global LIVAS AOD agreement with AERONET AOD within 0.1 is considered a very good
result for a 4-year product. Keeping the constrained data set for a
quantitative comparison, we present in Fig. 16 scatter plots for AOD
averages at the different LIVAS wavelengths. In the upper panel we show the
comparison for the averaged AOD at 532
The LIVAS database is freely available under the URL:
We presented LIVAS, a 4-year multi-wavelength global aerosol and cloud
optical database that has been developed to complement existing data sets
used by the ESA for instrument performance simulation of current and future
spaceborne lidars as well as retrieval algorithm testing activities based
on realistic atmospheric scenarios. In order to cover the different spectral
domains for HSRL and IPDA lidars, the compiled database addresses the three
harmonic operating wavelengths of Nd-YAG lasers (355, 532 and 1064
When compared to AERONET, the LIVAS AOD values appeared to be realistic and representative for VIS wavelengths but also for UV wavelengths, making this database appropriate for use by ADM-Aeolus and EarthCARE. Regarding the IR conversion, however, LIVAS is not considered representative when compared to AERONET, especially for AODs higher than 0.1. We believe that LIVAS is representative in the UV due to the fact that the UV–VIS BAEs and EAEs were provided by ground-based lidar measurements of high quality, as those provided by EARLINET. Moreover, the methodology used for the application of the conversions was based on aerosol classification advances developed within the ESA-CALIPSO project. For IR ,however, the BAEs and EAEs were not measured but instead retrieved from scattering simulations using typical size distributions and refractive indexes assumed for each CALIPSO aerosol type, deduced from AERONET data and aerosol models provided in the literature. Even though EARLINET was used to constrain the IR simulations, the final results were not satisfactory and more work is needed that would benefit from potential future IR ground-based measurements. However, LIVAS BAEs and EAEs were found to be more consistent with ESA-CALIPSO but also with the relative literature than the ones calculated with CALIPSO aerosol model.
In the future, we plan to expand LIVAS in monthly-averaged aggregations in order to provide time series for UV lidar products. In this way, LIVAS time series could be homogenized with future EarthCARE products for the consolidation of a multi-year aerosol/cloud multi-wavelength 4-D data set appropriate for climate studies. However, the challenges for this task are significant, due to a number of open scientific questions and related knowledge gaps. Specifically, the homogenization scheme envisaged cannot be realized without defining a common aerosol/cloud model that will be applicable to all the missions. This includes also the definition of a common aerosol/cloud classification scheme for the spaceborne products and ancillary ground-based data sets and the derivation of aerosol/cloud-type-dependent AEs for all lidar-related properties, i.e., extinction, backscatter and depolarization. It is believed that the well-established EARLINET network offers a unique opportunity to support such an effort. Several EARLINET stations operate multi-wavelength Raman lidars, with most of them measuring particle depolarization as well. The network's so-called “core stations” deliver the entire CALIOP/ALADIN/ATLID parameter set, so that the BAEs and EAEs for a variety of aerosol types can be derived experimentally over a comparably long time period.
This work has been developed under the auspices of the ESA-ESTEC project: Lidar Climatology of Vertical Aerosol Structure for Space-Based LIDAR Simulation Studies (LIVAS) contract no. 4000104106/11/NL/FF/fk. The publication was supported by the European Union Seventh Framework Programme (FP7-REGPOT-2012-2013-1), in the framework of the project BEYOND, under grant agreement no. 316210 (BEYOND – Building Capacity for a Centre of Excellence for EO-based monitoring of Natural Disasters). The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 262254 (ACTRIS), grant agreement no. 606953 and grant agreement no. 289923 – ITaRS. This research has been financed by EPAN II and PEP under the national action Bilateral, multilateral and regional R&T cooperations (AEROVIS Sino-Greek project). This work was performed in the framework of the PROTEAS project within GSRT's KRIPIS action, funded by Greece and the European Regional Development Fund of the European Union under the O.P. Competitiveness and Entrepreneurship, NSRF 2007-2013 and the Regional Operational Program of Attica.
The authors acknowledge EARLINET for providing aerosol lidar profiles
available under the World Data Center for Climate (WDCC) (The EARLINET
publishing group 2000–2010, 2014a, b, c, d, e). We thank the AERONET PIs
and their staff for establishing and maintaining the AERONET sites used in
this investigation. CALIPSO data were obtained from the ICARE Data Center
(