The EarthCARE (Earth Clouds, Aerosol and Radiation Explorer) mission will be equipped with four co-located instruments (three from ESA and one provided by JAXA) to obtain information on aerosol/clouds/radiation and their interaction through the processing of single instrument data as well as synergistic products.
The Payload Data Ground Segment (PDGS) is the component of the overall EarthCARE Ground Segment in charge to receive the housekeeping telemetry and instrument source packets via X-Band from the satellite, to process the packets for generating the different levels products and disseminate them to users in few hours from sensing. The main products include level 0 (corrected and time sorted packets), level 1B (instrument science data calibrated and geolocated), level 2A (geophysical parameters derived from a single instrument) and level 2B or synergistic products (geophysical parameters derived by merging information of several EarthCARE instruments). The PDGS is also in charge of the routine calibration and monitoring of the three ESA instruments, of products quality control as well as planning of payload operations To this purpose the EarthCARE PDGS is made of several components called facilities. In terms of functionality most components are similar to other PDGS developed by ESA for Earth Observation Missions, nevertheless some specific aspects of this mission have led to specific implementation. In particular, the synergistic nature of the mission results in a complex processing model which involves about 30 different processors. In order to streamline the integration of this large amount of processors and in anticipation of initially frequent updates, a formal modelling of the processing chain has been introduced to support automatic configuration of the processing facility in addition, a new facility called the Level 2 TestBed has been included in the PDGS in order to allow processor developers to test their code in quasi operational conditions and in an autonomous way including the possibility to upload new processor versions without assistance from PDGS operators. The presence of a Japanese Instrument on-board also imposes tight dependencies between the ESA and JAXA components in terms of processing as well as in terms of payload planning.
This poster presents the functional and architectural breakdown of the PDGS, external interfaces including FOS (Flight Operation Segment), ECMWF and JAXA. It details the main design drivers including data latency, production model, data volumes and network bandwidth. The current integration status of the PDGS and its underlying facility is also presented.

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The Earth Clouds, Aerosol and Radiation Explorer (EarthCARE), ESA's sixth Earth Explorer Mission, is a joint European-Japanese mission which aims at an improved understanding of cloud-aerosol-radiation interactions so as to include them correctly and reliably in climate and numerical weather prediction models. The required measurements will be acquired in a synergistic way by the high spectral resolution backscatter cloud/aerosol lidar (ATLID) operating at 355 nm, the 94 GHz Doppler cloud profiling radar (CPR) contributed by JAXA, the multi-channel spectral imager (MSI), and the total-wave and short-wave broad-band radiometer (BBR). The nominal duration of the EarthCARE mission is three years, including a six-months commissioning phase.

This paper presents the current development status of the EarthCARE flight segment and the instrument and system performance. Specific emphasis will be given to the outcome of recent on-ground instrument performance test and calibration campaigns for the three ESA instruments ATLID, MSI, and BBR.

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The Earth Clouds, Aerosol and Radiation Explorer (EarthCARE), ESA's sixth Earth Explorer Mission, is a joint European-Japanese mission which aims at an improved understanding of cloud-aerosol-radiation interactions so as to include them correctly and reliably in climate and numerical weather prediction models. The required measurements will be acquired in a synergistic way by the high spectral resolution backscatter cloud/aerosol lidar (ATLID) operating at 355 nm, the 94 GHz Doppler cloud profiling radar (CPR) contributed by JAXA, the multi-channel spectral imager (MSI), and the total-wave and short-wave broad-band radiometer (BBR). The nominal duration of the EarthCARE mission is three years, including a six-months commissioning phase.

The synergistic use of data results in a rather complex processing system with a large number of interconnected data processors and their data products. These processors and products are being developed by several institutions and companies, with common definitions for interfaces applied across them. This paper presents the current development and integration status of the European/Canadian EarthCARE level 1 and level 2 data processors for use in the mission end-to-end simulator E3SIM and the Payload Data Ground Segment.

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The Broadband Radiometer (BBR) instrument on EarthCARE will provide accurate measurements of the outgoing SW (0.2 - 4 µm) and TW (0.2 - >50 µm) radiances at the Top of the atmosphere at three viewing angles (+55º, 0º and -55º) in an along track configuration. These radiances are filtered by the spectral responses of the 3 telescopes and must be converted into unfiltered radiances in the Unfiltering process in order to correct for the limited and non-uniform spectral responses of the instrument.

In practice, the SW and TW measurements of the BBR must be converted into solar and thermal unfiltered radiances. First, the LW radiance is estimated from the SW and TW measurements. Secondly, the inter-channel contaminations, i.e, the parts of the LW signal due to reflected solar radiation and of the SW signal due to planetary radiation, are accounted for. Finally, multiplicative factors are computed in order to estimate the unfiltered solar and thermal radiances from the SW and LW channels, respectively.

This poster describes the EarthCARE BBR BM-RAD Level 2b product. The main variables provided are the TOA solar and thermal unfiltered radiances for each of the BBR telescopes at 6 different spatial resolutions (namely: Standard, Small, Full, Assessment Domain, Joint Standard Grid, and Joint Standard Grid PSF corrected). BM-RAD also contains filtered radiances as well as the scene type characterisation, illumination and observation geometries, and geolocation.

As regards to the algorithm, two unfiltering algorithms are used: the Stand-alone (for Shortwave (SW) and Longwave (LW) channels) and the MSI based (for the SW channel). The stand-alone algorithm estimates the unfiltered radiances from the filtered ones without using information from the MSI instrument and the unfiltering is done according to the measured broadband radiances and land use classification. The MSI based algorithm makes use of the MSI cloud mask and cloud phase in the unfiltering.

BM-RAD is a key product in the EarthCARE production chain, since the unfiltered radiances produced will be used by the BMA-FLX, ACM-RT and ACM-DF processors to verify the EarthCARE radiative closure.

The examples shown of the BM-RAD solar and thermal radiances at different resolutions are obtained using the BM-RAD processor fed with the EarthCARE Halifax simulated scene.

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Active observations from profiling instruments such as cloud radar or lidar contain a wealth of information on the vertical structure of clouds and precipitation. However, most NWP centres only assimilate observations of cloud with limited vertical information, such as radiances or near-surface rain rates. The immense detail provided by such active sensors, of great benefit to enhancing our understanding of clouds, paradoxically increases the difficulty in their use to initialize a forecast or generate a re-analysis, which we explore here. For example, any mis-match in the positioning of clouds between observation and model is difficult to reconcile. For radar, the forward model is extremely sensitive to assumptions on hydrometeor size. For lidar, any errors in the forward model can compound quickly where the signal is attenuated.

In this presentation we will outline developments to the ECMWF observation operator for cloud radar and lidar in preparation for the inclusion of EarthCARE observations of radar reflectivity and lidar backscatter into the ECMWF 4D-Var assimilation system. These include updates to the microphysical assumptions of clouds and precipitation, including a careful consideration of sources of error in terms of scattering properties and multiple scattering. We will also show a new `double-column’ approach for parameterizing cloud overlap to increase the impact of lidar backscatter, whose signal is typically more affected by attenuation than radar. Using the new observation operators, we will then show how EarthCARE observations will be monitored to detect any problems with either the observations or model and provide rapid feedback to ESA. In a monitoring system that combines information from observations and model, a statistically significant drift in the measurements can be detected faster than monitoring observations alone. In addition to data assimilation, the work presented is also very relevant to the development of instrument simulators (e.g., COSP) used to evaluate climate models in observation space.

In addition to the operator, data assimilation algorithms require an estimate of the observation error, which weights the observation relative to other observations and the model prior. Characterising the observation error correctly is particularly problematic for radar and lidar measurements, which tend to be highly situation-dependent. Diagnostic methods, which often rely on static observations errors, are likely to struggle. We therefore take an `error inventory’ approach, where we quantify the contribution from different sources of error. In particular, the narrow field-of-view of the instruments relative to the model scale means that the representativity error often dominates. We will demonstrate that, by treating this as a sampling error, we can use the local variance of the measurement to provide a flow-dependent estimate of the representativity error. The method also predicts the correlations in representativity error, which can be large in the vertical. The error from uncertainties in the forward models are also accounted for, using a Monte Carlo approach where parameters are perturbed within physical bounds. Mundane-sounding, but crucial practical elements, such as bias correction and screening of observations will also be discussed.

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The Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) Mission is the 6th Earth Explorer Mission. The EarthCARE mission aims to improve the understanding of cloud-aerosol-radiation interactions, so as to include them correctly and reliably in climate and numerical weather prediction models.

Atmospheric cloud and aerosol properties will be retrieved from EarthCARE’s 355nm lidar with a high spectral resolution (HSR) receiver with co- and cross-polar channels (ATLID), 94GHz Doppler cloud profiling radar (CPR) and a multi-spectral imager (MSI). Radiative transfer models will be used to derive atmospheric radiative properties and heating rates from the retrieved cloud and aerosol observations. Reflected solar and emitted terrestrial thermal radiation will be observed by EarthCARE’s broad-band radiometer (BBR).
The EarthCARE mission will be implemented in collaboration with the Japanese Aerospace Agency (JAXA) who will provide one of the core Instruments, namely the CPR, including the associated ground facilities in Japan to process their instrument data. The nominal duration of the EarthCARE mission is three years, including a 6-months commissioning phase.
With both new sensor technology and retrieval approaches, there is the need to carefully assess the quality and validity of the generated data products before releasing them to the wider user community and to continuously monitor them after this release. This is even more relevant for the specific case of EarthCARE where the ultimate objective is to enable synergistic retrievals.
In June 2017 ESA called for proposals in response to its Announcement of Opportunity (AO) for the External Calibration and the Validation of EarthCARE. This AO resulted in 32 proposals, several of which are coordinated proposals involving numerous institutions and investigators under one ‘umbrella’. The responses included more than 16 airborne platforms and numerous groundbased instruments of 23 different types. In addition, many teams proposed to perform satellite intercomparisons and a several teams will use models in their validation approach.
This poster will also address the various intercomparison and analysis methods involved. The 1st EarthCARE Validation Workshop was held in June 2018 in Bonn and the present poster will summarise the main conclusions from that workshop. Finally, this poster will describe the overall context of the validation programme in the EarthCARE Project context, including the collaboration with JAXA on EarthCARE Validation, and the high-level timeline for the pre-launch, commissioning, and long-term tasks related to Validation.

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An ongoing coordinated effort to take advantage of available instrumentation and know-how from 3 Italian atmospheric observatories in Central Mediterranean to provide high quality correlative data for validating EarthCARE products validation is presented. The observatories are located in the Island of Lampedusa (35.5°N, 12.6°E), in the Rome city center (BAQUNIN 80 aslm 41.90°N,12.50°E) and in its outskirts (CIRAS 110 aslm, 41.84°N,12.65°E), thus allowing the sampling of different regimes/processes of interest for EarthCARE validation and applications.
The Lampedusa observatory (www.lampedusa.enea.it), located south of Sicily at more than 100 km from mainland, is composed by atmospheric and oceanographic instrumentations, the latter represented by an instrumented buoy in open ocean, about 15 km SW of the atmospheric observatory. Data are collected since 1997 and can be considered as mainly representative of clean maritime regime. The location of Lampedusa allows to investigate several specific processes, relevant for aerosol-radiation, and aerosol-cloud-radiation interactions (e.g. Saharan dust transport, ship emissions, long range transport of anthropogenic aerosols).
The two sites in Rome are representative of typical near-coastal Mediterranean conditions and are equipped with twin active and passive remote sensing instrumentation.
The impact of urban environment on the EarthCARE products is an important aspect that will be investigated thankes to configuration of the observatories.
All sites are characterized by high probability of clear sky during summer season. The different longitude of observatories will increase the probability of a match-up with satellite observations.
Preparatory activities for match-up, as well as alternative approaches to compare statistical properties of the variables are being explored based on the analysis of acquired long term datasets. Rigorous estimation of ground based observation uncertainties, including product/site dependent estimation of spatio-temporal representativity, are being investigates for the planned EarthCARE validation. Moreover synergistic processing algorithms could be developed to take full advantage of the information carried by the different instruments.

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The operational EarthCARE L2 product on top-of-atmosphere (TOA) radiative fluxes is based on a radiance-to-flux conversion algorithm fed mainly by unfiltered broad-band radiances from the BBR instrument, and auxiliary data from EarthCARE L2 cloud products and modelled geophysical databases. The conversion algorithm models the angular distribution of the back-scattered solar radiation and thermal radiation emitted by the Earth-Atmosphere system, and provides flux estimates to be used for the radiative closure assessment of the Mission.

Different methods are employed for the solar and thermal BBR angular distribution models (ADMs). Models for SW radiances are created for different scene types and constructed from Clouds and the Earth’s Radiant Energy System (CERES) data using a feed-forward back-propagation artificial neural network (ANN) technique. LW ADMs are based on correlations between BBR radiance field anisotropy and the spectral information provided by the narrow-band radiances. Both retrieval algorithms exploit the multi-viewing capability of the BBR (forward, nadir and backward observations of the same target) providing flux estimates for every view and checking their integrity before being combined into the optimal TOA flux of the observed target. The reference height where the three BBR measurements are co-registered corresponds to the height where most reflection or emission takes place and depends on the spectral regime. LW observations are co-registered at the cloud top height, but the most radiatively significant height level on SW radiances is very dependent on the cloud. This reference height is instead selected by minimizing the flux differences between nadir, fore and aft fluxes.

The flux retrieval algorithms were empirically assessed by comparing results against fluxes obtained from CERES and Geostationary Earth Radiation Budge (GERB) ADMs. The utility of this exercise is limited because of the difficulties to find appropriate data to mimic EarthCARE products.
The study presented here shows a new exercise to evaluate the BBR radiance-to-flux conversion algorithms. Using the Environment Canada and Climate Change’s Global Environmental Multiscale (GEM) model the EarthCARE team has simulated three EarthCARE frames (1/8 of orbit). After applying the ESA instrument models to these test scenes a set of L1 EarthCARE data have been released to the team for testing. These cases include a cold-front near Halifax, dense overcast south of Halifax, scattered shallow convection south of Bermuda, clear and cold conditions at the northern extremity, scattered cloud through the Canadian Prairies, overcast over the Rocky Mountains, clear through Utah, and cirrus in Arizona and Mexico. These ample variety of realistic samples offer a great opportunity to compute the fluxes using EarthCARE L1 and L2 and compare results against modelled radiance fields.

The BBR solar and thermal flux retrieval algorithms were successfully employed to retrieve radiative fluxes over the test scenes. The details of this comparison are shown paying special attention to the highest differences between flux estimates and “true” fields.

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One of the largest uncertainties in climate change predictions is still caused by ice clouds. They have a large direct effect on Earth’s radiation budget since they reflect solar radiance into space and can trap thermal radiation within the atmosphere. The quantification of this process can be significantly improved by active remote sensing, which can explore vertical profiles of ice cloud microphysics. However, strong biases in ice water content and ice water path are to be expected for individual cloud regimes since most radar-lidar retrieval algorithms rely heavily on one mass-size relationship to parameterize the prevalent ice particle shape. In many cases, these biases can be identified and corrected when a vertically integrated quantity, like the solar reflectance, is considered simultaneously.

The upcoming ESA/JAXA satellite mission EarthCARE will give the opportunity to implement this synergistic use of radar, lidar and solar reflectance measurements on a single platform. Within the last years, great efforts were made to acquire airborne datasets and to develop synergistic cloud retrievals in anticipation for EarthCARE. Within this context, the German research aircraft HALO was equipped with a EarthCARE-like payload consisting of a high spectral resolution lidar (HSRL) system at 532 nm with additional polarization sensitive channels at 532 and 1064 nm, a high-power (30kW peak power) cloud radar at 35 GHz, a microwave radiometer package and passive radiation measurements with the hyper-spectral imager specMACS.

In this study, we analyze two different airborne measurement cases from HALO to retrieve ice cloud properties using the variational radar-lidar retrieval VarCloud. In addition, we use the retrieved ice cloud properties to simulate solar radiances, which we then compare to measured solar radiances from specMACS. The retrieval and forward simulations are done for different assumptions about the prevalent ice particle shape. The objective of this closure study is the identification of inconsistencies of the used ice particle shape. With respect to the radiative closure, modifications to the ice particle shape assumptions are devised and checked with simultaneously acquired in-situ measurements. In doing so, this study will improve the understanding of ice cloud microphysics with respect to the prevalent ice particle shape, which better replicates radar, lidar and solar radiance measurements.

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The future earth explorer mission Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) not only provides vertical information about aerosol and cloud properties through its active instruments Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), but also delivers columnar information in the across track dimension of the satellite via its passive instrument Multi-Spectral Imager (MSI). Aerosol properties based on the imager observations will be operationally derived with the Level 2A algorithm M-AOT. Aerosol optical thickness (AOT) will be estimated at 670 nm and over ocean additionally at 865 nm. Further, the Ångström parameter, based on the retrieved AOT at 670 nm and 865 nm, will be available over ocean surfaces.
The algorithm itself relies on measurements in the four MSI channels VIS (670 nm), NIR (865 nm), SWIR1 (1650 nm) and SWIR2 (2200 nm). It includes separate approaches for the AOT retrieval above ocean and land surfaces. Its inversion technique relies on Gauss-Newton optimal estimation. The forward model used in the algorithm is based on pre-calculated look-up tables (LUT) of modelled normalized radiance for the respective MSI channels. LUT simulations have been carried out by using radiative transfer code MOMO applying the EarthCARE Hybrid End-To-End Aerosol Classification (HETEAC) model [Wandinger et al. 2016] to ensure consistency between aerosol products from the active and passive instruments on EarthCARE.
First tests of the algorithm already have been accomplished by using EarthCARE Simulator (ECSIM) test scene and MODIS data justifying the retrieval approach. Hence, first product examples will be presented together with the algorithm in order to already understand the product and its limitations before the foreseen launch of EarthCARE in 2021.

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Knowledge of the spatiotemporal variability of the different aerosol types present in the atmosphere can improve our understanding of the direct and indirect aerosol effect on climate. Vertically resolved aerosol classification is of great importance for improved measurement retrievals, quantification of the anthropogenic and the natural radiative effect, radiative closure and aerosol-cloud interaction studies, as well as data assimilation and validation purposes. Since lidars are the best available instruments for vertically resolved aerosol profile measurements, we will develop a typing algorithm applicable to both ground-based and spaceborne lidar systems.
Based on the Hybrid End-To-End Aerosol Classification model (HETEAC) developments for EarthCARE (Wandinger et al., 2016), our typing methodology relies on measured intensive, i.e. concentration-independent, particle optical properties. Similar to HETEAC, but for more than the 355-nm wavelength, we created experimental bases based on ground-based lidar measurements at 355, 532 and 1064 nm. More specifically, the lidar ratio, the particle linear depolarization ratio and the Ångström exponent at the aforementioned wavelengths are used to create the experimental bases.
As an example, Figure 1 shows a collection of ground-based observations of lidar ratio and particle linear depolarization ratio at 355 nm acquired in different field campaigns. In addition to the experimental data set published in Illingworth et al. (2015), measurements from recent campaigns and new aerosol types have been added to consolidate the basis of HETEAC. More specifically, we introduced a new category of smoke measurements, which corresponds to smoke particles present in the stratosphere, and additional smoke measurements from the region of Central Europe (Leipzig). Measurements of smoke, pollution and dust-smoke mixture from South Africa (Giannakaki et al., 2016) have been also added to our basis. Apart from the existing Saharan dust measurements, we introduced measurements of Central Asian dust from the Central Asian Dust Experiment in Tajikistan (Hofer et al., 2017). Additionally, marine boundary-layer measurements from PollyXT, on board of the German research vessel Polarstern, have been added, including measurements of dried maritime aerosol particles (Bohlmann et al., 2018). The ‘Central European background aerosol’ category has been introduced and includes measurements of the background aerosol load in a typical central European region (made at Leipzig, Germany). Finally, we included measurements of a new mixture of Saharan dust and African biomass-burning aerosols. The presented database is regularly updated with new measurements from the international network of Polly lidars (PollyNET) and from mobile long-term stations (Baars et al., 2016), and if necessary new aerosol types are being introduced.
The different aerosol types (smoke, dust, marine aerosol, pollution, and volcanic ash) and mixtures of them spread over the 2D domain of the intensive particle parameters lidar ratio and linear depolarization ratio. Thus, a separation between small absorbing particles (e.g. smoke), large spherical particles (e.g. marine) and large non-spherical particles (e.g. dust) is possible based on these observations. The desert dust aerosols originating from different deserts can be distinguished from each other due to the fact that Asian dust tends to have lower lidar ratio values than the dust from the Saharan desert. The Ångström exponents (not shown here) of the observations shown in Fig. 1 are an additional source of information for our classification scheme and will be used as well. Mixtures of the aforementioned aerosol types show intermediate values of lidar ratio and depolarization ratio and make, along with their corresponding measurement uncertainties, the classification process more difficult. A way to overcome this difficulty is to estimate the aerosol mixing state by combining the lidar-measured quantities with the most representative microphysical description, in terms of size and shape distribution and spectral complex refractive index. In order to develop such an automatic algorithm, we will use the optimal estimation technique.
We will present a first approach of an extended HETEAC algorithm applicable to both ground-based and spaceborne lidar. The algorithm will exploit the multispectral information from sophisticated ground-based lidars (i.e., 3 backscatter, 2 extinction and 2 depolarisation measurements at 355, 532 and 1064 nm), but will also be able to retrieve an aerosol typing from less information as, e.g., in case of EarthCARE.
The aim is to provide multispectral aerosol-type-separated backscatter and extinction profiles depending on the capabilities of the used lidar. Ground-based lidar networks such as PollyNET and the European Aerosol Research Lidar Network (EARLINET) could then implement this aerosol typing algorithm and provide a near-real-time aerosol typing product. As the algorithm is aimed to work also for lidars with less spectral information, harmonization of long-term space-borne observations (e.g., CALIOP, ALADIN and ATLID) shall be possible. Our final product aims also to be used for data assimilation and model evaluation.


Figure 1: Aerosol classification from measurements of lidar ratio and particle linear depolarization ratio at the ATLID wavelength of 355 nm. Ground-based observations were performed with the Raman-polarization lidars POLIS (University of Munich, corresponds to all the SAMUM-2 measurements and the Eyjafjallajökull) and PollyXT (Leibniz Institute for Tropospheric Research, the rest of the measurements) at Cape Verde (Saharan dust, Clean marine, Dust and smoke; Groß et al. 2011), South Africa (Smoke, Pollution, Dust and smoke; Giannakaki et al. 2016), at the EARLINET stations of Leipzig (Pollution, Central European background aerosol, Stratospheric smoke, Boreal biomass burning aerosols and soil dust) and Munich, Germany (Eyjafjalla ash; Groß et al. 2012), in the Amazon Basin (Smoke; Baars et al. 2012), over the North Atlantic (Saharan dust, Dust and smoke from Polarstern cruise; Kanitz et al. 2013 and Clean marine, Dried marine, MBL mixture from Polarstern; Bohlmann et al. 2018) and in Tajikistan (Central Asian dust, Hofer et al. 2017).

References
1. Wandinger, Ulla, et al., 2016: "HETEAC: The Aerosol Classification Model for EarthCARE." EPJ Web of Conferences. Vol. 119. EDP Sciences.
2. Illingworth, Anthony J., et al., 2015: "The EarthCARE satellite: The next step forward in global measurements of clouds, aerosols, precipitation, and radiation." Bulletin of the American Meteorological Society 96, 1311-1332, https://doi.org/10.1175/BAMS-D-12-00227.1.
3. Giannakaki, Elina, et al., 2016: "Optical and microphysical characterization of aerosol layers over South Africa by means of multi-wavelength depolarization and Raman lidar measurements." Atmospheric Chemistry and Physics 16, 8109-8123.
4. Hofer, Julian, et al., 2017: "Long-term profiling of mineral dust and pollution aerosol with multiwavelength polarization Raman lidar at the Central Asian site of Dushanbe, Tajikistan: case studies." Atmospheric Chemistry and Physics 17, 14559.
5. Bohlmann, Stephanie, et al., 2018: "Ship-borne aerosol profiling with lidar over the Atlantic Ocean: from pure marine conditions to complex dust-smoke mixtures." Atmospheric Chemistry & Physics 18.
6. Baars, Holger, et al., 2016: "An overview of the first decade of Polly NET: an emerging network of automated Raman-polarization lidars for continuous aerosol profiling." Atmospheric Chemistry and Physics 16, 5111-5137.

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EarthCARE aims at improving our understanding of aerosol/cloud interactions on the global scale by providing accurate vertical distributions of key aerosol and cloud properties, in order to assess their impact on the Earth’s radiative budget and climate. Optical properties needed for radiative-transfer calculations are mainly related to the particle load and its vertical distribution (extinction coefficient) as well as the particle type (indicative of radiative properties as single-scattering albedo, asymmetry parameter, extinction spectral dependencies). In order to retrieve the required information from EarthCARE observations, the Hybrid End-To-End Aerosol Classification (HETEAC) model is being developed for common use in the Level 2 (L2) algorithms. It will allow aerosol typing based on measurements of extinction coefficient, lidar ratio and particle linear depolarization ratio at 355 nm with ATLID and provides related radiative properties of the detected aerosol types for radiative-transfer models and closure studies. This novel approach necessitates highly reliable input, from both the instrument and the underlying aerosol model, and thus requires extensive verification and validation efforts under various atmospheric conditions.

To advance our understanding of atmospheric feedback processes in a changing climate, it is crucial to better characterize aerosol-cloud interaction. Our goal is to extract from ATLID aerosol measurements properties that are relevant for cloud processes, such as the number concentrations of cloud condensation nuclei (CCN) and ice nucleating particles (INP).

The Eastern Mediterranean is an optimum testbed for investigating atmospheric mixtures of dust, pollution, smoke, and marine particles. Moreover, because of the enhanced ice-nucleating ability of mineral dust particles, ice formation often occurs in the aerosol layers. During the CyCARE campaign (Oct 2016 – Mar 2018) a state-of-the-art multiwavelength polarization lidar PollyXT was deployed at Limassol, Cyprus (34°N, 33°E).
In order to achieve the above-mentioned objectives, two flights with a sum of 10 flight hours were performed during the A-LIFE field experiment (April 2017). These flights include overpasses over the lidar sites in Cyprus and Crete. Those measurements will be used for the verification and validation of the HETEAC algorithm and EarthCARE related parametrizations CCN and INP number concentrations. The airborne measured particle size distributions of dust, pollution-dust and marine-dust mixtures will be used for the comparison to the lidar derived profiles of particle number concentrations. This is the basis for estimating the number concentration of cloud condensation nuclei and ice nucleating particles.

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