The value of multi-sensor remote sensing applied to cloud, aerosol, radiation and precipitation studies has become clear in recent years. For example, combinations of instruments including passive radiometers, lidars and cloud radars have proved invaluable for their ability to retrieve high resolution profiles of cloud macrophysical and microphysical properties. This is amply illustrated by various results from the US-DoE ARM (and similar) surface sites as well as results from data collected by sensors aboard the A-train satellites CloudSat, CALIPSO, and Terra. The Earth Clouds Aerosol and Radiation Explorer (EarthCARE) mission is a combined ESA/JAXA mission set for launch in 2021 and has been designed around sensor-synergy.. The mission consists of a Dopplerised cloud-profiling radar (CPR), a high-spectral resolution cloud/aerosol lidar (ATLID), a cloud/aerosol multi-spectral imager (MSI), and a three-view broadband radiometer (BBR) covering both LW and SW bands.

The CPR is similar in various aspects to CloudSat but has a Doppler capability, is somewhat more sensitive and has improved vertical sampling. ATLID is significantly different from the CALIPSO lidar. It operates at 355 nm and its HSRL deign enables it to separate the returns from clouds/aerosol from the thermally broadened molecular scattering. This enables ATLID to provide improved extinction and extinction-to-backscatter ratio measurements which are useful in identifying profiles of different cloud and aerosol types.

EarthCARE will deliver cloud, aerosol, radiation and precipitation products focusing on horizontal scales ranging from 1 km to 10 km. EarthCARE is a cloud-aerosol-radiation process oriented mission that will extend the record of active space-based cloud and aerosol measurements started by CloudSat and CALIPSO. EarthCARE data will be used in multiple ways ranging from cloud-aerosol model evaluation studies, to GCM-orientated cloud microphysical property parameterization development, to radiative closure studies, and even data assimilation in forcast models. By using innovative methods of combining the 2-D vertical properties retrieved along the nadir lidar/radar view with the 2-D horizontal context provided by the imager, EarthCARE will deliver level-2 products that will, thanks to BBR data, have already been evaluated for radiative consistency.

In order to achieve the aims of the EarthCARE mission, a system of individual instrument geophysical algorithms (L2a) and (L2b) synergetic (e.g. multi-instrument algorithms) is being developed. Even the single-instrument (L2a) algorithms are being built with synergy in mind and must fit into the overall (fundamentally synergetic) retrieval process as they will often produce input for the L2b algorithms (e.g. the Lidar Target classification and Radar target classification can be combined to a Lidar+Radar target classification).

In this presentation, a brief overview of the EarthCARE mission will be given, describing the scientific motivation of the mission, the mission instruments and the planned synergetic use of EarthCARE data. The L2 processing chain algorithms will be described as well as the process being used to facilitate the algorithm testing and development. Particular focus will be given to the lidar retrieval algorithms which exploit the unique HSRL characteristic of ATLID.

This presentation is made on behalf of the members of the EarthCARE joint mission advisory group, the EarthCARE project team and the members of the L2 development teams.

The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite is a joint European Space Agency and Japanese Aerospace Exploration Agency mission scheduled to launch in 2021. EarthCARE (EC) will host the first Doppler cloud profiling radar (CPR) in space which, in addition to constraining microphysical retrievals in particle sedimentation regimes, is expected to provide the first ever global observations of convective vertical air motion and associated mass fluxes. Owing to its lower orbit and larger antenna, the EC-CPR is expected to be 6 dB more sensitive than NASA’s CloudSat CPR. This enhanced sensitivity combined with the EC-CPR ability to perform considerable oversampling in range (every 100 m for a 500 m pulse) is expected to contribute to an improved detection of cloud systems and their properties. Another significant improvement of the EC-CPR includes recording, for the first time, Doppler velocity information from space. Several factors are expected to impact the quality of the EC-CPR Doppler velocity measurements. Here we review the enhanced measurement capabilities of the EC-CPR and we highlight a number of scientific applications that will benefit from the EC-CPR observations.

EarthCARE will provide vertically resolved aerosol and cloud properties at the same time. The combination of space-borne lidar (ATLID) and passive imager (MSI) on one single satellite will be a great benefit. This configuration is ideal to study aerosol-cloud interaction needed to better understand atmospheric feedback processes in a changing climate.
State of the art algorithms have been developed to retrieve MSI cloud and aerosol products, e.g. cloud mask, ISCCP cloud types, cloud phase, cloud optical thickness, cloud effective radius and cloud top height, aerosol optical thickness and Ångström exponent. ATLID L2 algorithms provide the vertical structure of aerosol optical properties in terms of depolarization ratio, backscatter coefficient, extinction coefficient, and lidar ratio at 355 nm. In order to study aerosol-cloud interaction, it is first of all important to classify the correct aerosol type based on the measured aerosol intensive properties (depolarization ratio, lidar ratio). The Hybrid End-To-End Aerosol Classification model (HETEAC) is developed for this purpose. Based on the stand alone retrievals we combine active and passive measurements to derive synergetic cloud and aerosol products: the cloud top height, spectral aerosol optical thickness, Ångström exponent and aerosol types. The cloud top height retrieved from MSI is an infrared effective radiating height, which is located somewhere within the cloud. The ATLID cloud top height gives the physical boundaries of the clouds along track, which will be used to study the relationships between the effective and true cloud top height. The aerosol typing is further supported by synergistically retrieved columnar aerosol properties based on measurements of the aerosol optical thickness at 355 nm (ATLID), 670 nm (MSI) and 865 nm (MSI, only ocean). In this way, the columnar Ångström exponent can be used on completing the aerosol information derived by EarthCARE. To develop a combined retrieval, which uses the cloud/aerosol information from ATLID at the track and MSI over the entire swath, we use MODIS and Calipso observations and specific test scenes generated with the EarthCARE Simulator (ECSIM), which have been created from model output data, as a test bed.

The scientific goal of the EarthCARE satellite mission is to retrieve profiles of cloud and aerosol properties, from active and passive sensor measurements, well enough such that when acted upon by radiative transfer models (RTMs), simulated top-of-atmosphere (TOA) shortwave (SW) and longwave (LW) radiative fluxes agree, on average, to within W/m² of corresponding values inferred from collocated measurements made by the on-board broadband radiometer (BBR). In essence, this will amount to a near-continuous radiative closure assessment given that no retrieval algorithms utilize BBR radiances. This closure experiment was articulated in EarthCARE’s initial proposal and represents the BBR’s raison d'être.

In actuality, closure assessments will involve use of both BBR radiances directly, and TOA SW and LW fluxes obtained by applying angular direction models (ADMs) to BBR radiances. The ADMs have been tailor-made for EarthCARE and use radiances from all three BBR telescopes: nadir and ~55° viewing zenith angles along-track (fore and aft). Corresponding RTM estimates of broadband fluxes, which are standard fare for cloud- and aerosol-related satellite missions, come from both conventional 1D RTMs as well as 3D RTMs. Hence, EarthCARE will be the first satellite mission to employ 3D RTMs operationally; all others have relied on either forward or inverse 1D RTMs. While the 1D RTMs operate, by definition, directly on each ~1 km-wide column of retrieved properties, the 3D RTMs run into immediate trouble as they account for horizontal transport of photons in and out of the narrow retrieved cross-section. In some atmosphere-surface conditions, lateral fluxes can be large, and so 3D domains will be “constructed” around the retrieved cross-section. Given current estimates of computational resources that will be available for near real-time processing of EarthCARE products, constructed domains will measure ~30 km across-track and ~50 km along-track; mean radiances and fluxes will be computed for the inner ~5 x 21 km assessment domain, and it is these values that will be involved in the closure tests. The objective was to work with assessment domains that are large enough to keep BBR signal-to-noise ratios at bay, yet small enough so as to focus the close test on the retrievals, and not the scene construction algorithm.

In addition to the closure tests which will see SW and LW TOA radiances (at the BBR’s three views) computed by the 3D RTMs, LW flux and heating rate profiles, averaged over assessment domains, will be computed by the 1D RTM in Independent Column Approximation (ICA) mode. Similarly, flux and heating rate profiles for SW radiation will be computed by the 1D RTM, as well as by the 3D RTM. To facilitate both cloud and aerosol radiative effects, both 1D RTMs will also compute flux and heating rate profiles for pristine atmospheric conditions (clouds and aerosols are neglected), and clear-skies (aerosols are included but clouds are not).

Further to the novelty of its Doppler cloud radar and high-spectral resolution lidar, the promise of the EarthCARE mission lies in the ambitious on-board synergy of its four complementary instruments. Despite radar, lidar, imaging radiometer and broadband radiometer orbiting in formation for many years, the synergy of A-Train satellites has not yet been exploited to produce a simultaneous retrieval of clouds, aerosols and precipitation. Such an internally-consistent retrieval would have benefits for the improved understanding of cloud-aerosol-precipitation interactions and their roles in the global energy and water cycles.

The CAPTIVATE algorithm will provide unified a retrieval of the profile of clouds, aerosols and precipitation from the synergy of the Atmospheric Lidar, Cloud Profiling Radar and Multi-Spectral Imager (ACM-CAP) aboard EarthCARE. CAPTIVATE’s optimal estimation framework includes radar and lidar multiple scattering models, a fast forward-model for solar radiances and a Kalman smoother to support aerosol retrievals from noisy lidar measurements, and reports detailed diagnostics of retrieval uncertainty.

In this study we apply CAPTIVATE to the synergy of CloudSat, CALIPSO and MODIS measurements. The retrievals are evaluated against the state-of-the-art estimates of ice and snow from CloudSat-CALIPSO synergy, rain from CloudSat, and liquid cloud water from MODIS. A radiative transfer model is used to estimate top-of-atmosphere radiative fluxes from the retrieved profile of cloud and aerosol, as a test of radiative closure against CERES measurements. Finally, the retrieval is used to evaluate ECMWF forecasts and inform the improved representation of cloud-aerosol-precipitation interactions.

The accurate specification of the atmospheric state is vital to produce reliable weather forecasts. Data assimilation is used to bring many different observation types together to derive the atmospheric conditions on which forecasts are initialised. Cloud related observations, such as those from microwave radiances, have been at the forefront of recent developments in assimilation, but contain limited information on the vertical structure of clouds. Active observations from profiling instruments such as cloud radar or lidar contain a wealth of information on the structure of clouds and precipitation, providing the much-needed vertical context of clouds, but have never been assimilated in global NWP models. Inspired by the success of previous experiments, in which Cloudsat radar reflectivity and Calipso attenuated backscatter profiles were indirectly assimilated via pseudo-observations of temperature and humidity, the European Centre for Medium-range Weather Forecasts (ECMWF) 4D-Var system has been adapted to allow their direct assimilation.

In this presentation, we will demonstrate the first-time assimilation of cloud radar and lidar observations into a global NWP model. Using Cloudsat and Calipso observations we will show that, as well as pulling the initial conditions closer to the cloud radar and lidar data, the assimilation brings forecasts closer to TEMP radiosonde, PILOT and AIREP observations, with the largest impact observed for wind. We will then show the potential for improvement in forecasts of rain rates in the tropics when compared to TRMM measurements. Sensitivity of the results to assumptions in the observation operator and observation error will also be discussed. Gaining benefit to forecast skill by including new observations into a well-established observing system is extremely difficult, so the encouraging results warrant further research, not only to realise the direct benefit of cloud radar and lidar assimilation, but also to explore the instrument synergy with passive sensors. Additionally, the developments facilitate the monitoring of observations both in an operational sense and for weather forecast model evaluation.