In this paper, we present the new research infrastructure of the ERATOSTHENES CoE for aerosol and cloud remote sensing observations and its capabilities to participate in cal/val activities. The research facility is installed less than 2 km from the coastline of Limassol, Cyprus. The multiplatform for atmospheric research consists of a multiwavelength lidar, 35 GHz cloud radar, a microwave radiometer, and ancillary instruments to become a fully ACTRIS cloud and aerosol remote sensing station. Measurements performed at Limassol’s station were already utilized for the validation of the aerosol products of Sentinel 5P and AEOLUS and will be used for the validation of the EarthCARE mission.

The EarthCARE mission of ESA & JAXA will be launched in May 2024. The mission will provide observations of aerosols, clouds, precipitation, and solar and thermal radiative flux measurements on a global scale, with a goal to constrain the uncertainties in aerosol, cloud, radiation, and their interactions. Before the utilization of the mission’s observations on science studies, calibration/validation activities are necessary to evaluate the uncertainties of its products. To support the timely and maximum exploitation of EarthCARE products, the ACROSS Mediterranean experiment will be implemented in 2024, 2025, and 2026. ACROSS is a joint effort of several institutes and universities in Europe, which will deploy advanced state-of-the-art instruments tailored for the mission’s validation requirements in three Mediterranean supersites (Potenza-Italy, Antikythera-Greece, Cyprus). The ground-based remote sensing measurements will be enhanced with airborne measurements on board UAVs, light aircraft, and large airborne platforms which will perform measurements in the Mediterranean region. The synergistic dataset will be exploited for a thorough validation of the EarthCARE mission, but also for the science studies including aerosol-cloud-radiation interaction studies and numerical weather prediction optimizations for the region.

EarthCARE is an Earth Explorer Mission of the European Space Agency (ESA) scheduled for launch in May 2024. The mission will perform atmospheric observations using a high spectral resolution lidar, a Doppler cloud radar, a multi- spectral imager, and a broadband radiometer. A project to support the validation of the EarthCARE mission was initiated in the framework of the ATMO ACCESS project. The activity is supported by the broader ACTRIS, EARLINET and Cloudnet communities, resulting in a consortium of 46 participating observatories, centers, and facilities. The activity includes the development of procedures for data quality control and near-real-time provision of the suborbital measurements collocated with the satellite overpasses to online data centers, and their easy findability by data validation experts. A rehearsal campaign has been implemented using simulated overpasses, supporting the community to gain experience in the upcoming real validation activities. The real validation activities are scheduled with the launch of EarthCARE in May 2024.

In this study, we present a comparison of the AEOLUS satellite L2A aerosol product with the retrievals of the ground-based lidar systems of EARLINET (European Aerosol Research Lidar Network), part the European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases (ACTRIS). Dedicated ground-based measurements during AEOLUS overpasses have been performed among the 18 member stations since the beginning of the mission. The satellite timeseries covers the period 06/2019 – 05/2023, including observations processed with Baseline 15 and 16. In order to identify a collocation, the satellite must pass closer than 120 km around a station and also within a time interval of ±1.5 hours from the lidar measurement time. The AEOLUS backscatter and extinction coefficient profiles at 355nm are compared with the corresponding ground-based profiles. Biases are calculated in different atmospheric regions and the correlation of the two timeseries is also investigated. The ground-based dataset is split in two categories, measurements that include the particle linear depolarization ratio (PLDR) profile at 355nm and measurements that do not. PLDR allows us to calculate profile of the co-polar backscatter component that is directly comparable with AEOLUS. The total backscatter coefficient is used when the PLDR is not available. The main aspects of this analysis involve both searching for random and systematic bias patterns in the AEOLUS timeseries and investigating their link to other relevant parameters such as the aerosol load and the Baseline version.

Calibration of an atmospheric lidar is often required due to variations in the electro-optical system. Rayleigh fitting commonly performed may fail under various conditions. Temporal and spatial variations both affect lidar signals. We hence opt for spatiotemporal analysis. We present a novel deep-learning (DL) lidar calibration model based on convolutional neural networks (CNN). We demonstrate our method on simulated data that mimics natural ground-based pulsed time-of-flight lidar signals. Such an approach can better address measurements with a poor signal-to-noise ratio (SNR) and provide a more frequent calibration.

Methods based on statistical learning have become prevalent in various signal processing disciplines and have recently gained traction in atmospheric lidar studies. Nonetheless, such methods often require large quantities of annotated or resolved data. Such data is rare and requires effort, especially when exploring evolving phenomena. Existing simulators and databases primarily focus on atmospheric vertical profiles. We propose the Atmospheric Lidar Data Augmentation (ALiDAn) framework to fill this gap. ALiDAn serves as an end-to-end generation and augmentation framework of spatiotemporal and multi-wavelength resolved lidar simulated data. ALiDAn employs a hybrid approach of physical models, data statistics, and sampling processes. Additionally, it takes into account geographical and seasonal characteristics of aerosols, meteorological conditions, along with short- and long-term phenomena that affect lidar measurements. This approach can provide diversified data and robust benchmarks to assist in developing and validating new lidar processing algorithms. We demonstrate simulations compatible with a pulsed time-of-flight lidar. Our approach leverages a broader use of existing databases and can inspire similar data augmentation to other types of lidars and active sensors.