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.

The EarthCARE mission for monitoring the aerosols, clouds and precipitation, and for radiation closure studies is planned to be launched in May 2024. In preparation of the calibration and validation (cal/val) activities that will be performed for the Atmospheric Lidar (ATLID), one of the four instruments onboard, a lidar simulator tool has been developed for providing realistic simulations of the ATLID lidar signals and the Level 1 (L1) products. The simulated ATLID L1 profiles are used to derive realistic ATLID Level 2A (L2A) profiles of aerosol properties. In this study, the realistic ATLID L2A profiles will be compared with the L2 profiles from eVe lidar, the ESA’s ground reference lidar for cal/val studies, aiming to investigate the sensitivity of the ATLID design on real aerosol layers. Moreover, key aspects on the validation of the aerosol EarthCARE products using the eVe lidar system as ground reference will be discussed.

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.

BACKGROUND: Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants. RESULTS: To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between genetic markers. It performs an agglomerative hierarchical clustering using the Mahalanobis distance covariance matrix of the markers. In simulation studies, we show that our method outperforms existing methods in detecting true causal variants. Applying CluStrat on WTCCC2 and UK Biobank cohorts, we found biologically relevant associations in Schizophrenia and Myocardial Infarction. CluStrat was also able to correct for population structure in polygenic adaptation of height in Europeans. CONCLUSIONS: CluStrat highlights the advantages of biologically relevant distance metrics, such as the Mahalanobis distance, which captures the cryptic interactions within populations in the presence of LD better than the Euclidean distance.

MOTIVATION: Principal Component Analysis is a key tool in the study of population structure in human genetics. As modern datasets become increasingly larger in size, traditional approaches based on loading the entire dataset in the system memory (Random Access Memory) become impractical and out-of-core implementations are the only viable alternative. RESULTS: We present TeraPCA, a C++ implementation of the Randomized Subspace Iteration method to perform Principal Component Analysis of large-scale datasets. TeraPCA can be applied both in-core and out-of-core and is able to successfully operate even on commodity hardware with a system memory of just a few gigabytes. Moreover, TeraPCA has minimal dependencies on external libraries and only requires a working installation of the BLAS and LAPACK libraries. When applied to a dataset containing a million individuals genotyped on a million markers, TeraPCA requires <5 h (in multi-threaded mode) to accurately compute the 10 leading principal components. An extensive experimental analysis shows that TeraPCA is both fast and accurate and is competitive with current state-of-the-art software for the same task. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are both available at https://github.com/aritra90/TeraPCA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The increasing volume and complexity of high-throughput genomic data make analysis and prioritization of variants difficult for researchers with limited bioinformatics skills. Variant Ranker allows researchers to rank identified variants and determine the most confident variants for experimental validation. RESULTS: We describe Variant Ranker, a user-friendly simple web-based tool for ranking, filtering and annotation of coding and non-coding variants. Variant Ranker facilitates the identification of causal variants based on novelty, effect and annotation information. The algorithm implements and aggregates multiple prediction algorithm scores, conservation scores, allelic frequencies, clinical information and additional open-source annotations using accessible databases via ANNOVAR. The available information for a variant is transformed into user-specified weights, which are in turn encoded into the ranking algorithm. Through its different modules, users can (i) rank a list of variants (ii) perform genotype filtering for case-control samples (iii) filter large amounts of high-throughput data based on user custom filter requirements and apply different models of inheritance (iv) perform downstream functional enrichment analysis through network visualization. Using networks, users can identify clusters of genes that belong to multiple ontology categories (like pathways, gene ontology, disease categories) and therefore expedite scientific discoveries. We demonstrate the utility of Variant Ranker to identify causal genes using real and synthetic datasets. Our results indicate that Variant Ranker exhibits excellent performance by correctly identifying and ranking the candidate genes CONCLUSIONS: Variant Ranker is a freely available web server on http://paschou-lab.mbg.duth.gr/Software.html . This tool will enable users to prioritise potentially causal variants and is applicable to a wide range of sequencing data.