International audienceThe collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint ESA-JAXA EarthCARE satellite mission, scheduled for launch in 2017, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, CALIPSO, and Aqua. Specifically, EarthCARE's Cloud Profling Radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle and raindrop fall speeds. EarthCARE's 355-nm High Spectral Resolution Lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The Multi-Spectral Imager will provide a context for, and the ability to construct the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross-section. The consistency of the retrievals will be assessed to within a target of ±10 W m⁻² on the (10 km²) scale by comparing the multi-view Broad-Band Radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains

Cloud drop condensation nuclei (CCN) and ice nuclei (IN) particles determine to a large extent cloud microstructure and, consequently, cloud albedo and the dynamic response of clouds to aerosol-induced changes to precipitation. This can modify the reflected solar radiation and the thermal radiation emitted to space. Measurements of tropospheric CCN and IN over large areas have not been possible and can be only roughly approximated from satellite-sensor-based estimates of optical properties of aerosols. Our lack of ability to measure both CCN and cloud updrafts precludes disentangling the effects of meteorology from those of aerosols and represents the largest component in our uncertainty in anthropogenic climate forcing. Ways to improve the retrieval accuracy include multiangle and multipolarimetric passive measurements of the optical signal and multispectral lidar polarimetric measurements. Indirect methods include proxies of trace gases, as retrieved by hyperspectral sensors. Perhaps the most promising emerging direction is retrieving the CCN properties by simultaneously retrieving convective cloud drop number concentrations and updraft speeds, which amounts to using clouds as natural CCN chambers. These satellite observations have to be constrained by in situ observations of aerosol-cloud-precipitation-climate (ACPC) interactions, which in turn constrain a hierarchy of model simulations of ACPC. Since the essence of a general circulation model is an accurate quantification of the energy and mass fluxes in all forms between the surface, atmosphere and outer space, a route to progress is proposed here in the form of a series of box flux closure experiments in the various climate regimes. A roadmap is provided for quantifying the ACPC interactions and thereby reducing the uncertainty in anthropogenic climate forcing.

Cloud fraction, liquid and ice water contents derived from long-term radar, lidar, and microwave radiometer data are systematically compared to models to quantify and improve their performance.

† The contribution of H. W. Barker to this article was prepared as part of his official duties as a Canadian government employee This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active-passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed.The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km) 2 domains constructed from MODIS, CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m −2 for ∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation ∼65% are within ±10 W m −2 .

Abstract. In this paper, a novel lidar/radar method for simultaneously determining cloud particle effective size profiles (for water and ice clouds) and the lidar attenuation profile is described. Simulations and application to real data show that this procedure can be quite robust even in cases where significant lidar attenuation is present. In addition, the concept of a suitable lidar/radar effective particle size is introduced, and the determination of water contents for both water and ice clouds using this effective size together with the radar reflectivity profile is discussed. This paper concludes by presenting examples of effective size profiles and water content profiles inferred from measurements made during the Dutch Clouds and Radiation (CLARA) campaign. In a companion paper, it is demonstrated that the results of the inversion procedure compare favorably with infrared radiometer measurements as well as with in-situ measurement results.