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The Multi-Spectral Imager (MSI) on board the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) will provide horizontal information about aerosols and clouds. These measurements are needed to extend vertical cloud and aerosol property information, which is obtained from EarthCARE's active sensors, in order to obtain a full three-dimensional view of cloud and aerosol conditions. Mesoscale weather systems, in particular, will be characterized. The discovery of a non-compliance of the MSI visible–near-infrared–shortwave infrared (VNS) camera's visible (VIS) and shortwave infrared (SWIR1) channels regarding a spectral central wavelength (CWVL) shift across-track of up to 14 nm (VIS) and 20 nm (SWIR1) led to the need for an analysis regarding its impact on MSI Level-2A aerosol and cloud products. A significant influence of the spectral misalignment effect (SMILE) on MSI retrievals is identified due to the spectral variation in gas absorption, surface reflectance, and aerosol and cloud properties within the spectral ranges of these MSI bands. For example, the VIS channel is positioned in close proximity to the red edge of green vegetation and is impacted by residual absorption of water vapor and ozone. Small central wavelength variations introduce uncertainties due to the rapid change in surface reflectance for conditions with low optical thickness. The present central wavelength shift in the VIS towards shorter wavelengths than at nadir introduces a relative error in transmission of up to 3.3 % due to the increasing influence of water vapor and ozone absorption. We found relative errors in the top-of-atmosphere (TOA) signal due to the SMILE of up to 30 % for low optical thickness over a land surface in that band. Since the magnitude of the impact strongly depends on the underlying surface and atmospheric conditions, we conclude that accounting for the SMILE in Level-2 retrievals or correcting the Level-1 signal will improve MSI aerosol and cloud product quality. [ABSTRACT FROM AUTHOR]

The objective of the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) mission is to infer attributes of cloud, aerosol, precipitation, and radiation from observations made by four complementary instruments. This requires the development of single-instrument and multiple-instrument (i.e. synergistic) retrieval algorithms that employ measurements made by one, or more, of EarthCARE's cloud-profiling radar (CPR), atmospheric lidar (ATLID), and multi-spectral imager (MSI); its broadband radiometer (BBR) places the retrieved quantities in the context of the surface–atmosphere radiation budget. To facilitate the development and evaluation of ESA's EarthCARE production model prior to launch, sophisticated instrument simulators were developed to produce realistic synthetic EarthCARE measurements for simulated conditions provided by cloud-resolving models. While acknowledging that the physical and radiative representations of cloud, aerosol, and precipitation in the test scenes are based on numerical models, the opportunity to perform detailed evaluations wherein the "truth" is known provides insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study, we compare EarthCARE retrieval products both statistically across all simulated scenes and from a specific time series from a single scene. For ice clouds, it is shown that retrieved profiles of ice water content and effective particle size made by the ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) synergistic algorithm are consistently more accurate than those from its single-instrument counterparts. While liquid clouds are often difficult to detect from satellite-borne sensors, especially for multi-layered clouds, ACM-CAP benefits from combined constraints from lidar backscatter, solar radiances, and radar-path-integrated attenuation but still exhibits non-trivial random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have a similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation, where CPR signals are dominated by multiple scattering and attenuation. Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter, and depolarisation (A-EBD) product and ACM-CAP demonstrating similar performance. The multi-spectral imager (MSI) cloud optical properties (M-COP) product performs very well for unambiguous cloud layers. Similarly, the MSI aerosol optical thickness (M-AOT) product performs well when radiances are unaffected by cloud, but both products provide little information about vertical profiles of properties. Finally, a summary of the performance of all retrieval products and their random errors is provided. [ABSTRACT FROM AUTHOR]

The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is the first mission that will provide measurements from active profiling, passive imaging and a broadband radiometer from a single satellite platform. The passive multi-spectral imager (MSI) features four solar and three thermal infrared channels, and has a swath of 150 km and a spatial pixel resolution of 500 m. The MSI observations will provide across-track information on clouds and aerosol to extend the active profiling information into the swath. In this paper, we present the algorithm used for retrieving the cloud optical and physical products (M-COP), specifically cloud optical thickness, effective radius and top height. The algorithm is based on the solar and terrestrial MSI channels within an optimal estimation framework. This framework enables full error propagation given by the uncertainties in measurements and a priori information. The MSI cloud algorithm has been successfully exercised on different imagers and on synthetically generated MSI observations. [ABSTRACT FROM AUTHOR]

Quantifying the role of clouds in the earth's radiation budget is essential for improving our understanding of the drivers and feedback mechanisms of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations using state-of-the-art ground-based remote sensing of clouds are contrasted with surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, most likely erroneous, representation of these low-level clouds in the radiative transfer simulations led to errors in the cloud radiative effect of 54 W m -2. In total, the proposed method could be applied to 11 % of the observations. For the data, where the proposed method was utilized, the average relative error decreased from 109 % to 37 % for the simulated solar and from 18 % to 2.5 % for the simulated terrestrial downward radiative fluxes at the surface. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided. [ABSTRACT FROM AUTHOR]

The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) is a combination of multiple active and passive instruments on a single platform. The Atmospheric Lidar (ATLID) provides vertical information of clouds and aerosol particles along the satellite track. In addition, the Multi-Spectral Imager (MSI) collects multi-spectral information from the visible to the infrared wavelengths over a swath width of 150 km across the track. The ATLID–MSI Column Products processor (AM-COL) described in this paper combines the high vertical resolution of the lidar along track and the horizontal resolution of the imager across track to better characterize a three-dimensional scene. ATLID Level 2a (L2a) data from the ATLID Layer Products processor (A-LAY), MSI L2a data from the MSI Cloud Products processor (M-CLD) and the MSI Aerosol Optical Thickness processor (M-AOT), and MSI Level 1c (L1c) data are used as input to produce the synergistic columnar products: the ATLID–MSI Cloud Top Height (AM-CTH) and the ATLID–MSI Aerosol Column Descriptor (AM-ACD). The coupling of ATLID (measuring at 355 nm) and MSI (at ≥670 nm) provides multi-spectral observations of the aerosol properties. In particular, the Ångström exponent from the spectral aerosol optical thickness (AOT 355/670 nm) adds valuable information for aerosol typing. The AOT across track, the Ångström exponent and the dominant aerosol type are stored in the AM-ACD product. The accurate detection of the cloud top height (CTH) with lidar is limited to the ATLID track. The difference in the CTH detected by ATLID and retrieved by MSI is calculated along track. The similarity of MSI pixels across track with those along track is used to transfer the calculated CTH difference to the entire MSI swath. In this way, the accuracy of the CTH is increased to achieve the EarthCARE mission's goal of deriving the radiative flux at the top of the atmosphere with an accuracy of 10 W m -2 for a 100 km 2 snapshot view of the atmosphere. The synergistic CTH difference is stored in the AM-CTH product. The quality status is provided with the products. It depends, e.g., on day/night conditions and the presence of multiple cloud layers. The algorithm was successfully tested using the common EarthCARE test scenes. Two definitions of the CTH from the model truth cloud extinction fields are compared: an extinction-based threshold of 20 Mm -1 provides the geometric CTH, and a cloud optical thickness threshold of 0.25 describes the radiative CTH. The first CTH definition was detected with ATLID and the second one with MSI. The geometric CTH is always higher than or equal to the radiative CTH. [ABSTRACT FROM AUTHOR]

The Multi-spectral Imager (MSI) on board the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) will provide horizontal information about aerosols and clouds. These measurements are needed to extend vertical cloud and aerosol property information, which are obtained from EarthCARE's active sensors, in order to obtain a full three dimensional view on cloud and aerosol conditions. Especially, meso-scale weather systems will be characterized. The discovery of a non-compliance of the MSI VNS camera's visible (VIS) and shortwave-infrared (SWIR1) channels regarding a spectral central wavelength (CWVL) shift across track of up to 14 nm (VIS) and 20 nm (SWIR1), led to the need for an analysis regarding its impact on MSI Level 2A aerosol and cloud products. A significant influence of the Spectral MIsaLignment Effect (SMILE) on MSI retrievals is identified due to the spectral variation of gas absorption, surface reflectance as well as aerosol and cloud properties within the spectral ranges of these MSI bands. For example, the VIS channel is positioned in close proximity to the red edge of green vegetation and is impacted by residual absorption of water vapour and ozone. Small central wavelength variations introduce uncertainties due to the rapid change in surface reflectance for conditions with low optical thickness. The present central wavelength shift in the VIS towards shorter wavelengths than at nadir introduces a relative error in transmission of up to 3.3 % due to the increasing influence of water vapour and ozone absorption. We found relative errors in the TOA signal due to the SMILE of up to 30 % for low optical thickness over a land surface in that band. Since the magnitude of the impact strongly depends on the underlying surface and atmospheric conditions, we conclude that accounting for the SMILE in Level 2 retrievals or correcting the Level 1 signal will improve MSI aerosol and cloud product quality. [ABSTRACT FROM AUTHOR]

The mission of the Earth cloud, aerosol and radiation explorer (EarthCARE) mission to observe cloud, aerosol, precipitation and radiation using four complementary instruments requires the development of many single-instrument and synergistic algorithms for the retrieval of geophysical quantities. The retrieval products employ one or more of the cloud profiling radar (CPR), atmospheric lidar (ATLID) and multispectral imager (MSI), while the broadband radiometer (BBR) places the retrieved quantities in the context of the atmospheric radiation budget. To facilitate the development and evaluation of the ESA EarthCARE production model prior to launch, sophisticated instrument simulators have been developed to produce realistic synthetic EarthCARE measurements from the output of cloud-resolving model simulations. While acknowledging that the physical and radiative representation of cloud, aerosol and precipitation in the test scenes are based on numerical models, the opportunity to perform a detailed evaluation wherein the model ''truth'' is known has provided rare insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study we intercompare EarthCARE retrieval products from within the ESA production model both statistically across all simulated EarthCARE granules, and using timeseries of data from an individual scene. The comparison between the retrieved quantities helps to illustrate the strengths and limitations of the single-instrument retrievals, and the degrees to which the synergistic retrieval and composite products can represent the entire atmosphere of clouds, aerosols and precipitation. We show that radar-lidar synergy has the greatest impact in ice clouds; when compared with single-instrument radar and lidar retrievals, the synergistic ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) product accurately retrieves profiles of both ice water content and effective radius. While liquid cloud is difficult to detect directly from spaceborne remote sensors, especially in complex and layered scenes, the synergistic retrieval benefits from combined constraints from lidar backscatter, solar radiances and radar path-integrated attenuation, but still exhibits a high degree of random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation where the radar is dominated by multiple scattering and attenuation). Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter and depolarization (A-EBD) product and ACM-CAP demonstrating similar performance in the test scenes. The multispectral imager (MSI) cloud optical properties (M-COP) product performs very well in unambiguous cloud layers; similarly, the MSI aerosol optical thickness (M-AOT) product performs well where the possibility of contamination by cloud signal is very low. A summary of the performance of all retrieval products is provided, and may help to inform the selection of EarthCARE data products by future users. [ABSTRACT FROM AUTHOR]

The Hybrid End-To-End Aerosol Classification (HETEAC) model for the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is introduced. The model serves as the common baseline for the development, evaluation, and implementation of EarthCARE algorithms. It guarantees the consistency of different aerosol products from the multi-instrument platform and facilitates the conformity of broad-band optical properties needed for EarthCARE radiative-closure assessments. While the hybrid approach ensures that the theoretical description of aerosol microphysical properties is consistent with the optical properties of the measured aerosol types, the end-to-end model permits the uniform representation of aerosol types in terms of microphysical, optical, and radiative properties. Four basic aerosol components with prescribed microphysical properties are used to compose various natural and anthropogenic aerosols of the troposphere. The components contain weakly and strongly absorbing fine-mode and spherical and non-spherical coarse-mode particles and thus are representative for pollution, smoke, sea salt, and dust, respectively. Their microphysical properties are selected such that good coverage of the observational phase space of intensive, i.e., concentration-independent, optical aerosol properties derived from EarthCARE measurements is obtained. Mixing rules to calculate optical and radiative properties of any aerosol blend composed of the four basic components are provided. Applications of HETEAC in the generation of test scenes, the development of retrieval algorithms for stand-alone and synergistic aerosol products from EarthCARE's atmospheric lidar (ATLID) and multi-spectral imager (MSI), and for radiative-closure assessments are introduced. Finally, the implications of simplifying model assumptions and possible improvements are discussed, and conclusions for future validation and development work are drawn. [ABSTRACT FROM AUTHOR]

Quantifying the role of clouds in the Earth radiation budget is essential for improving our understanding of the drivers and feedbacks of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations based on state-of-the-art ground-based remote sensing of clouds are contrasted to surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, likely erroneous, representation of these low-level clouds in the radiative transfer simulations let to errors in the cloud radiative effect of 43 W m-2. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided. [ABSTRACT FROM AUTHOR]

Quantifying the role of clouds in the Earth radiation budget is essential for improving our understanding of the drivers and feedbacks of climate change. This holds in particular for the Arctic, the region currently undergoing the most rapid changes. This region, however, also poses significant challenges to remote-sensing retrievals of clouds and radiative fluxes, introducing large uncertainties in current climate data records. In particular, low-level stratiform clouds are common in the Arctic but are, due to their low altitude, challenging to observe and characterize with remote-sensing techniques. The availability of reliable ground-based observations as reference is thus of high importance. In the present study, radiative transfer simulations based on state-of-the-art ground-based remote sensing of clouds are contrasted to surface radiative flux measurements to assess their ability to constrain the cloud radiative effect. Cloud radar, lidar, and microwave radiometer observations from the PS106 cruise in the Arctic marginal sea ice zone in summer 2017 were used to derive cloud micro- and macrophysical properties by means of the instrument synergy approach of Cloudnet. Closure of surface radiative fluxes can only be achieved by a realistic representation of the low-level liquid-containing clouds in the radiative transfer simulations. The original, likely erroneous, representation of these low-level clouds in the radiative transfer simulations let to errors in the cloud radiative effect of 43Wm-2. The present study highlights the importance of jointly improving retrievals for low-level liquid-containing clouds which are frequently encountered in the high Arctic, together with observational capabilities both in terms of cloud remote sensing and radiative flux observations. Concrete suggestions for achieving these goals are provided. [ABSTRACT FROM AUTHOR]

ESA's Cloud, Aerosol and Radiation Explorer EarthCARE is the first mission which will provide measurements from active profiling, passive imaging and a broad-band radiometer from a single satellite platform. The passive multi-spectral imager (MSI) features four solar and three thermal infrared channels, and has a swath of 150 km and a spatial pixel resolution of 500 m. The MSI observations will provide across-track information on clouds and aerosol to extend the active profiling information into the swath. In this paper, we present the algorithm used for retrieving the cloud optical and physical products (M-COP), specifically cloud optical thickness, effective radius and top height. The algorithm is based on the solar and terrestrial MSI channels within an optimal estimation framework. The advantage of optimal estimation is that it enables full error propagation given by the uncertainties in measurements and a-priori information. The MSI cloud algorithm has been successfully exercised on different imagers and on synthetically generated MSI observations. [ABSTRACT FROM AUTHOR]

We introduce the development of EarthCARE Level 2 layer products derived from profile measurements of the high-spectral-resolution lidar ATLID and column products obtained from combined information of ATLID and the Multi-Spectral Imager (MSI). Layer products include cloud top height as well as aerosol layer boundaries and mean optical properties along the satellite nadir track. Synergistic column products comprise cloud top height, Ångström exponent, and aerosol type both along-track and across the MSI swath.

The Hybrid End-To-End Aerosol Classification (HETEAC) model for the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is introduced. The model serves as the common baseline for development, evaluation, and implementation of EarthCARE algorithms. It guarantees the consistency of different aerosol products from the multi-instrument platform and facilitates the conform specification of broad-band optical properties needed for EarthCARE radiative closure assessments. While the hybrid approach ensures that the theoretical description of aerosol microphysical properties is consistent with the optical properties of the measured aerosol types, the end-to-end model permits the uniform representation of aerosol types in terms of microphysical, optical, and radiative properties. Four basic aerosol components with prescribed microphysical properties are used to compose various natural and anthropogenic aerosols of the troposphere. The components contain weakly and strongly absorbing fine-mode as well as spherical and non-spherical coarse-mode particles and thus are representative for pollution, smoke, sea salt, and dust, respectively. Their microphysical properties are selected such that a good coverage of the observational phase space of intensive, i.e., concentration-independent, optical aerosol properties derived from EarthCARE measurements is obtained. Mixing rules to calculate optical and radiative properties of any aerosol blend composed of the four basic components are provided. Applications of HETEAC in the generation of test scenes, the development of retrieval algorithms for stand-alone and synergistic aerosol products from EarthCARE's Atmospheric Lidar (ATLID) and Multi-Spectral Imager (MSI), as well as for radiative closure assessments are discussed. In the end, conclusions for future development work are drawn. [ABSTRACT FROM AUTHOR]

The EarthCARE satellite mission will provide new insights into aerosol-cloud and radiation interactions by means of synergistic observations of the Earth's atmosphere from a collection of active and passive remote sensing instruments, flying on a single satellite platform. The Multi-Spectral Imager (MSI) will provide visible and infrared images in the cross-track direction with a 150 km swath and a pixel sampling at 500 m. The suite of MSI cloud algorithms will deliver cloud macro- and micro-physical properties complementary to the vertical profiles measured from the ATmospheric LIDar (ATLID) and the Cloud Profiling Radar (CPR) instruments. The MSI cloud mask algorithm (M-CM) provides a cloud flag, cloud phase and cloud type product which are essential parameters for the cloud optical and physical properties (M-COP) as well as for the aerosol optical properties (M-AOT). Synthetic test scenes from the EarthCARE end-to-end simulator provide a first overview of the expected performance of the M-CM products before launch. Validation of the MSI cloud mask algorithm against satellite products from MSG SEVIRI and MODIS demonstrates a good performance of the algorithm. The MSI cloud detection performance is not completely aligned with that from MODIS, which is however not surprising when considering the rather limited number of spectral channels of MSI in comparison to MODIS. The direct comparison to MODIS shows that some threshold tuning should be done especially over desert or sun glint regions once real observations are available. However the MSI bands are less affected by sun glint because of the asymmetric view of the imager. [ABSTRACT FROM AUTHOR]

We introduce the Hybrid End-To-End Aerosol Classification (HETEAC) model for the upcoming EarthCARE mission. The model serves as the common baseline for development, evaluation, and implementation of EarthCARE algorithms. It shall ensure the consistency of different aerosol products from the multi-instrument platform as well as facilitate the conform specification of broad-band optical properties necessary for the EarthCARE radiative closure efforts. The hybrid approach ensures the theoretical description of aerosol microphysics consistent with the optical properties of various aerosol types known from observations. The end-to-end model permits the uniform representation of aerosol types in terms of microphysical, optical and radiative properties.