Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper [Sandu et al.(2011)] we derived an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify two information metrics (the signal and degrees of freedom for signal) for satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content, gives analyses that are comparable in quality with the one obtained using the entire data set

A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. I: Methodology

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. This paper focuses on the four dimensional variational (4D-Var) data assimilation framework. Metrics from information theory are used to quantify the contribution of observations to decreasing the uncertainty with which the system state is known. We establish an interesting relationship between different information-theoretic metrics and the variational cost function/gradient under Gaussian linear assumptions. Based on this insight we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. The approach is illustrated on linear and nonlinear test problems. In the companion paper [Singh et al.(2011)] the methodology is applied to a global chemical data assimilation problem

A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper (Sandu et al., 2012) we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify the signal and degrees of freedom for signal information metrics of satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content yields an analysis comparable in quality with the one obtained using the entire data set

A Hybrid Approach to Estimating Error Covariances in Variational Data Assimilation

Data Assimilation (DA) involves the combination of observational data with the underlying dynamical principles governing the system under observation. In this work we combine the advantages of the two prominent advanced data assimilation systems, the 4D-Var and the ensemble methods. The proposed method consists of identifying the subspace spanned by the major 4D-Var error reduction directions. These directions are then removed from the background covariance through a Galerkin-type projection. This generates an updated error covariance information at both end points of an assimilation window. The error covariance information is updated between assimilation windows to capture the ``error of the day''. Numerical results using our new hybrid approach on a nonlinear model demonstrate how the background covariance matrix leads to an error covariance update that improves the 4D-Var DA results

Warthin-like papillary thyroid carcinoma: a case report and comprehensive review of the literature

BackgroundPapillary Thyroid Carcinoma (PTC) is the most frequent endocrine malignancy with a variety of histological presentations. Warthin-like Papillary Thyroid Carcinoma (WLPTC) is an uncommon neoplasm that is recognized as a distinct subtype of PTC in the WHO classification of thyroid tumors. In this report, we present a novel case of WLPTC in a female patient and provide an in-depth review of the available literature on its clinical, pathological, and therapeutic characteristics.Case presentationA 27-year-old female patient was referred for neck swelling. Ultrasound showed two suspicious thyroid nodules leading to a thyroidectomy. She was diagnosed with intermediate-risk bifocal foci of classic PTC and WLPTC, arising from a background of chronic lymphocytic thyroiditis (CLT). This pT1b(m) N1b M0 malignancy was treated with adjuvant isotopic ablation and suppressive thyroxine therapy. The 1-year outcomes were favorable.Literature reviewIt covered articles published from 1995 to 2022, by searching PubMed and Google Scholar using specific terms. Out of 148 articles reviewed by two authors, 25 relevant articles were selected, including 13 case reports and 12 case series. The study included 150 cases of WLPTC. Data related to clinical presentation, imaging, histological features, management, and outcomes, were extracted. The mean age of diagnosis was 39 years, with a female predominance. The most common clinical presentation was neck swelling. Thyroid autoimmunity was positive in 71.6% of patients. Lymph node metastases were present in 28% of cases, with no reported distant metastases. Overall, the outcomes were favorable.ConclusionWLPTC shares similar clinical and radiological presentations as classic PTC. The hallmark histological features of WLPTC are papillae lined with oncocytic tumor cells with papillary nuclear changes and lymphoid stroma. WLPTC is almost constantly associated with CLT. The management of WLPTC aligns with that of classic PTC with comparable stage and risk category, often resulting in favorable outcomes

Entwicklung nachhaltiger Pflanzenschutzstrategien zur Bek_mpfung von Schadschmetterlingen im olivenanbau

Current olive growing practices range from the traditional Mediterranean olive grove to intensively managed olive plantations. Insecticides against major olive pests, like the olive moth (Prays oleae, Lep.: Yponomeutidae) and the jasmine moth, (Palpita unionalis, Lep.: Pyralidae) are still applied frequently. The European Union-funded international research project TRIPHELIO is aimed at the development of economically feasible and sustainable insecticide-free control methods for key Lepidopterous pests of olive. Main research activities focus on (1) the use of pheromones for mating disruption and improved monitoring of target pests, (2) habitat management strategies to enhance the activity of natural enemies in the olive grove, and (3) the use of inundative releases of mass-reared egg parasitoids of the genus Trichogramma. Moreover, tools for successful integration of the methods developed into an integrated pest management (IPM) strategy for olive pests were explored by optimising techniques for surveillance as well as considering potential side-effects of common pesticides on beneficial organisms. An intense exchange of scientific information and technology between European and North African countries was undertaken to create solutions for a wide range of olive growing regions. Key results and recommendations for further essential steps towards practical implementation are presented in this publication.Zusammenfassung Traditionelle, extensiv bearbeitete Olivenhaine, aber auch moderne Intensiv-Plantagen mit k_nstlicher Bew_sserung und hohem Einsatz von D_ngern bzw. chemischen Pflanzenschutzmitteln kennzeichnen die derzeitigen verschiedenen Anbauformen der Olive im Mittelmeerraum. Schadlepidopteren wie die Olivenmotte (Prays oleae, Lep.: Yponomeutidae) und die Jasminmotte (Palpita unionalis, Lep.: Pyralidae) werden durch regelm_ssigen Insektizideinsatz bek_mpft. Das von der EU gef_rderte internationale Forschungsprojekt TRIPHELIO zielte auf die Entwicklung insektizidfreier Alternativmethoden durch (1) die Optimierung der pheromongest _tzten _berwachung und Verwirrtechnik, (2) der Anwendung von Habitatmanagement-Strategien zur F_rderung nat_rlicher Gegenspieler, und (3) dem Einsatz von Trichogramma-Schlupfwespen. Zus_tzlich wurden Module f_r eine optimale Anwendung biotechnischer und biologischer Methoden bez_glich der Ph_nologie der Schadinsekten und m_glicher Nebenwirkungen von Pestiziden erarbeitet. Die intensive Kooperation zwischen Wissenschaftlern und Praktikern aus mehreren L_ndern Europas und Nordafrikas erlaubte den Entwurf m_glicher L_sungsans_tze f_r verschiedene Anbaubedingungen und klimatische Regionen des Mittelmeerraumes. Die wichtigsten Ergebnisse und Ausblicke f_r eine zuk_nftige praktische Umsetzung werden in dieser Ver_ffentlichung beschrieben

Ensemble variational assimilation as a probabilistic estimator - Part 1: The linear and weak non-linear case

International audienceData assimilation is considered as a problem in Bayesian estimation, viz. determine the probability distribution for the state of the observed system, conditioned by the available data. In the linear and additive Gaussian case, a Monte Carlo sample of the Bayesian probability distribution (which is Gaussian and known explicitly) can be obtained by a simple procedure: perturb the data according to the probability distribution of their own errors, and perform an assimilation on the perturbed data. The performance of that approach, called here ensemble variational assimilation (EnsVAR), also known as ensemble of data assimilations (EDA), is studied in this two-part paper on the non-linear low-dimensional Lorenz-96 chaotic system, with the assimilation being performed by the standard variational procedure. In this first part, EnsVAR is implemented first, for reference, in a linear and Gaussian case, and then in a weakly non-linear case (assimilation over 5 days of the system). The performances of the algorithm, considered either as a probabilistic or a deterministic estimator, are very similar in the two cases. Additional comparison shows that the performance of EnsVAR is better, both in the assimilation and forecast phases, than that of standard algorithms for the ensemble Kalman filter (EnKF) and particle filter (PF), although at a higher cost. Globally similar results are obtained with the Kuramoto-Sivashinsky (K-S) equation

Ensemble variational assimilation as a probabilistic estimator - Part 2: The fully non-linear case

International audienceThe method of ensemble variational assimilation (EnsVAR), also known as ensemble of data assimilations (EDA), is implemented in fully non-linear conditions on the Lorenz-96 chaotic 40-parameter model. In the case of strong-constraint assimilation, it requires association with the method of quasi-static variational assimilation (QSVA). It then produces ensembles which possess as much reliability and resolution as in the linear case, and its performance is at least as good as that of ensemble Kalman filter (EnKF) and particle filter (PF). On the other hand, ensembles consisting of solutions that correspond to the absolute minimum of the objective function (as identified from the minimizations without QSVA) are significantly biased. In the case of weak-constraint assimilation, EnsVAR is fully successful without need for QSVA