A scalable approach for Variational Data Assimilation

Data assimilation (DA) is a methodology for combining mathematical models simulating complex systems (the background knowledge) and measurements (the reality or observational data) in order to improve the estimate of the system state (the forecast). The DA is an inverse and ill posed problem usually used to handle a huge amount of data, so, it is a large and computationally expensive problem. Here we focus on scalable methods that makes DA applications feasible for a huge number of background data and observations. We present a scalable algorithm for solving variational DA which is highly parallel. We provide a mathematical formalization of this approach and we also study the performance of the resulted algorith

Preconditioning of the background error covariance matrix in data assimilation for the Caspian Sea

Data Assimilation (DA) is an uncertainty quantification technique used for improving numerical forecasted results by incorporating observed data into prediction models. As a crucial point into DA models is the ill conditioning of the covariance matrices involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here we present first studies concerning the introduction of two different preconditioning methods in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea by using the Regional Ocean Modeling System (ROMS) with observations provided by the Group of High resolution sea surface temperature (GHRSST). We also present the algorithmic strategies we employ

Toward the S3DVAR data assimilation software for the Caspian Sea

Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The forecasting model used for producing oceanographic prediction into the Caspian Sea is the Regional Ocean Modeling System (ROMS). Here we propose the computational issues we are facing in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March

Dynamic Neural Assimilation: a deep learning and data assimilation model for air quality predictions

Ambient air pollution is known to be a serious issue that has an impact on human health and the environment. Assessing air quality is of the utmost importance to protect human health and the environment. Different tools are available, from monitoring stations to complex models. These systems are capable of accurately predicting air quality levels, but they are often computationally very expensive which makes them poorly efficient. In this paper, we developed a novel model called Dynamic Neural Assimilation (DyNA) integrating Recurrent Neural Networks and Data Assimilation methods to derive a physics-informed system capable of accurately forecasting air pollution tendencies and investigating the relationship with industrial statistics. DyNA is trained in historical data and is fine-tuned as soon as new data comes available. We trained and tested the system on real data provided by the air quality monitoring stations located in Italy from the European Environment Agency and simulated results derived from the air quality modelling system Atmospheric Modelling System-Model to support the International Negotiation on atmospheric pollution on a National Italian level. We analysed air pollution data in Italy from the years 2003–2010 and studied its correlation with nearby industries in some regions where monitoring sensors were available

Enhancing CFD-LES air pollution prediction accuracy using data assimilation

It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude

The higher-level phylogeny of Archosauria (Tetrapoda:Diapsida)

Crown group Archosauria, which includes birds, dinosaurs, crocodylomorphs, and several extinct Mesozoic groups, is a primary division of the vertebrate tree of life. However, the higher-level phylogenetic relationships within Archosauria are poorly resolved and controversial, despite years of study. The phylogeny of crocodile-line archosaurs (Crurotarsi) is particularly contentious, and has been plagued by problematic taxon and character sampling. Recent discoveries and renewed focus on archosaur anatomy enable the compilation of a new dataset, which assimilates and standardizes character data pertinent to higher-level archosaur phylogeny, and is scored across the largest group of taxa yet analysed. This dataset includes 47 new characters (25% of total) and eight taxa that have yet to be included in an analysis, and total taxonomic sampling is more than twice that of any previous study. This analysis produces a well-resolved phylogeny, which recovers mostly traditional relationships within Avemetatarsalia, places Phytosauria as a basal crurotarsan clade, finds a close relationship between Aetosauria and Crocodylomorpha, and recovers a monophyletic Rauisuchia comprised of two major subclades. Support values are low, suggesting rampant homoplasy and missing data within Archosauria, but the phylogeny is highly congruent with stratigraphy. Comparison with alternative analyses identifies numerous scoring differences, but indicates that character sampling is the main source of incongruence. The phylogeny implies major missing lineages in the Early Triassic and may support a Carnian-Norian extinction event.Marshall Scholarship for study in the United KingdomJurassic FoundationUniversity of BristolPaleontological Societ

Influence of tumor microenvironment and fibroblast population plasticity on melanoma growth, therapy resistance and immunoescape

Cutaneous melanoma (CM) tissue represents a network constituted by cancer cells and tumor microenvironment (TME). A key feature of CM is the high structural and cellular plasticity of TME, allowing its evolution with disease and adaptation to cancer cell and environmental alter-ations. In particular, during melanoma development and progression each component of TME by interacting with each other and with cancer cells is subjected to dramatic structural and cellular modifications. These alterations affect extracellular matrix (ECM) remodelling, phenotypic profile of stromal cells, cancer growth and therapeutic response. The stromal fibroblast populations of the TME include normal fibroblasts and melanoma‐associated fibroblasts (MAFs) that are highly abun-dant and flexible cell types interacting with melanoma and stromal cells and differently influencing CM outcomes. The shift from the normal microenvironment to TME and from normal fibroblasts to MAFs deeply sustains CM growth. Hence, in this article we review the features of the normal mi-croenvironment and TME and describe the phenotypic plasticity of normal dermal fibroblasts and MAFs, highlighting their roles in normal skin homeostasis and TME regulation. Moreover, we dis-cuss the influence of MAFs and their secretory profiles on TME remodelling, melanoma progres-sion, targeted therapy resistance and immunosurveillance, highlighting the cellular interactions, the signalling pathways and molecules involved in these processes

Machine learning for earth system observation and prediction

N/

A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies of combatting the pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information to assess the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors to quantify intervention impacts at a finer granularity. A data assimilation framework is developed to estimate these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is built to quantify the impacts of intervention strategies by monitoring the evolution of the estimated parameters. We reveal the intervention impacts in European countries and Wuhan and the resurgence risk in the United States