Search

In the next few years, numerous satellites with high-resolution instruments dedicated to the imaging of atmospheric gaseous compounds will be launched, to finely monitor emissions of greenhouse gases and pollutants. Processing the resulting images of plumes from cities and industrial plants to infer the emissions of these sources can be challenging. In particular traditional atmospheric inversion techniques, relying on objective comparisons to simulations with atmospheric chemistry transport models may poorly fit the observed plume due to modelling errors rather than due to uncertainties in the emissions. The present article discusses how these images can be properly compared to simulated concentrations to limit the weight modelling errors due to the meteorology used to analyse the images. For such comparisons, the usual pixel-wise L2 norm may not be a good option, because it is subject to the double penalty issue inherent to its local definition. This issue is characterised by a mutation of any position shift into significant amplitude discrepancies. To circumvent this issue, we propose to either provide an upstream correction of the position misfit between the observed and simulated plumes in the usual L2 norm or use a non-local metric based on the optimal transport theory, such as the Wasserstein distance. All the metrics are evaluated using first a catalogue of analytical plumes and then more realistic plumes simulated with mesoscale Eulerian atmospheric transport model, with an emphasis on the sensitivity of the metrics to position mismatch and the concentration values within the plumes. As expected, the metrics with the upstream correction are found to be less sensitive to position errors in both analytical and realistic conditions. Furthermore, in realistic cases, we evaluate the weight of changes in the norm and the direction of the four-dimensional wind fields in our metric values. This comparison highlights the link between differences in the synoptic-scale winds direction and position error. It is found that discrepancies between two plume images due to wind direction errors in the meteorological conditions are less penalised by our new metrics with the upstream correction than without, thus avoiding the double penalty issue. [ABSTRACT FROM AUTHOR]

Abstract: In the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. The accuracy of the forecast plume is highly dependent on the source term estimation. On several academic test cases, including real data, inverse modelling and data assimilation techniques were proven to help in the assessment of the source term. In this paper, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Two dispersion models have been used: Polair3D and Silam developed in two different research centres. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large multiplicative observational errors are assumed. The inverse modelling scheme accounts for strong error bias encountered with such errors. The efficiency of the data assimilation system is tested via statistical indicators. For France and Finland, the average performance of the data assimilation system is strong. However there are outlying situations where the inversion fails because of a too poor observability. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools are developed and tested to discriminate candidate release sites. [Copyright &y& Elsevier]

Abstract: Most of the atmospheric and oceanic data assimilation (DA) schemes rely on the Best Linear Unbiased Estimator (BLUE), which is sub-optimal if errors of assimilated data are non-Gaussian, thus calling for a full Bayesian data assimilation. This paper contributes to the study of the non-Gaussianity of errors in the observational space. Possible sources of non-Gaussianity range from the inherent statistical skewness and positiveness of some physical observables (e.g. moisture, chemical species), the nonlinearity, both of the data assimilation models and of the observation operators among others. Deviations from Gaussianity can be justified from a priori hypotheses or inferred from statistical diagnostics of innovations (observation minus background), leading to consistency relationships between the error statistics. From samples of observations and backgrounds as well as their specified error variances, we evaluate some measures of the innovation non-Gaussianity, such as the skewness, kurtosis and negentropy. Under the assumption of additive errors and by relating statistical moments from both data errors and innovations, we identify potential sources of the innovation non-Gaussianity. These sources range from: (1) univariate error non-Gaussianity, (2), nonlinear correlations between errors, (3) spatio-temporal variability of error variances (heteroscedasticity) and (4) multiplicative noise. Observational and background errors are often assumed independent. This leads to variance-dependent bounds for the skewness and the kurtosis of errors. From innovation statistics, we assess the potential DA impact of some scenarios of non-Gaussian errors. This impact is measured through the mean square difference between the BLUE and the Minimum Variance Unbiased Estimator (MVUE), obtained with univariate observations and background estimates. In order to accomplish this, we compute maximum entropy probability density functions (pdfs) of the errors, constrained by the first four order moments. These pdfs are then used to compute the Bayesian posterior pdf and the MVUE. The referred impact is studied for a large range of statistical moments, being higher for skewed innovations and growing in average with the skewness of data errors, specially if the skewnesses have the same sign. An application has been performed to the quality-accepted ECMWF innovations of brightness temperatures of a set of High Resolution Infrared Sounder (HIRS) channels. In this context, the MVUE has led in some extreme cases to a potential reduction of 20%–60% of the posterior error variance as compared to the BLUE, specially for extreme values of the innovations. [Copyright &y& Elsevier]

Abstract: The objective of this article is to investigate the validity of a modeling system developed for forecasting atmospheric dispersion, the Polyphemus platform, with a special focus on radionuclides. The platform is briefly described and model-to-data comparisons are reported for three cases: the ETEX campaign, the Chernobyl accident and the Algeciras release. The results are similar to those usually given in the literature by state-of-the-art models. Some preliminary sensitivity analysis indicates the main sources for uncertainties. [Copyright &y& Elsevier]

The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is the topic of recent studies in the geosciences, in which the key output is a surrogate model meant to emulate the dynamical model. In order to treat sparse and noisy observations in a rigorous way, ML can be combined with data assimilation (DA). This yields a class of iterative methods in which, at each iteration, a DA step assimilates the observations and alternates with a ML step to learn the underlying dynamics of the DA analysis. In this article, we propose to use this method to correct the error of an existing, knowledge‐based model. In practice, the resulting surrogate model is a hybrid model between the original (knowledge‐based) model and the ML model. We demonstrate the feasibility of the method numerically using a two‐layer, two‐dimensional, quasi‐geostrophic channel model. Model error is introduced by the means of perturbed parameters. The DA step is performed using the strong‐constraint 4D‐Var algorithm, while the ML step is performed using deep learning tools. The ML models are able to learn a substantial part of the model error and the resulting hybrid surrogate models produce better short‐ to mid‐range forecasts. Furthermore, using the hybrid surrogate models for DA yields a significantly better analysis than using the original model. [ABSTRACT FROM AUTHOR]

Data assimilation for systems possessing many scales of motions is a substantial methodological and technological challenge. Systems with these features are found in many areas of computational physics and are becoming common thanks to increased computational power allowing to resolve finer scales and to couple together several sub-components. Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward by meteo services worldwide. The core issue is the scale separation acting as a barrier that hampers the propagation of the information across model components (e.g. ocean and atmosphere). We provide a brief survey of CDA, and then focus on CDA using the ensemble Kalman filter (EnKF), a widely used Monte Carlo Gaussian method. Our goal is to elucidate the mechanisms behind information propagation across model components. We consider first a coupled system of equations with temporal scale difference, and deduce that: (i) cross components effects are strong from the slow to the fast scale, but, (ii) intra-component effects are much stronger in the fast scale. While observing the slow scale is desirable and benefits the fast, the latter must be observed with high frequency otherwise the error will grow up to affect the slow scale. Numerical experiments are performed using the atmosphere-ocean model, MAOOAM. Six configurations are considered, differing for the strength of the atmosphere-ocean coupling and/or the number of model modes. The performance of the EnKF depends on the model configuration, i.e. on its dynamical features. A comprehensive dynamical characterisation of the model configurations is provided by examining the Lyapunov spectrum, Kolmogorov entropy and Kaplan–Yorke attractor dimension. We also compute the covariant Lyapunov vectors and use them to explain how model instabilities act on different model's modes according to the coupling strength. The experiments confirm the importance of observing the fast scale, but show also that, despite its slow temporal scale, frequent observations in the ocean are beneficial. The relation between the ensemble size, N, and the unstable subspace dimension, n 0 , has been studied. Results largely ratify what known for uncoupled system: the condition N ≥ n 0 is necessary for the EnKF to work satisfactorily. Nevertheless the quasi-degeneracy of the Lyapunov spectrum of MAOOAM, with many near-zero exponents, is potentially the cause of the smooth gradual reduction of the analysis error observed for some model configurations, even when N > n 0 . Future prospects for the EnKF in the context of coupled ocean-atmosphere systems are finally discussed. [ABSTRACT FROM AUTHOR]

We refer to state estimation theory in geosciences as data assimilation (DA). DA is standard practice in numerical weather prediction, and its application is becoming widespread in many other areas of climate, atmosphere, ocean, and environment modelling; in all circumstances where one intends to estimate the state of a large dynamical system based on limited information. Atmosphere and ocean, are examples of chaotic dissipative dynamics: error dynamics is extremely sensitive to the initial condition and highly state-dependent. Dealing with such a flow-dependent error growth is a challenge for DA: one must in fact be able to properly track and incorporate this dependency in the DA process. A situation that is made even worse by the large size of the geophysical models. This talk will recall the unique challenges that chaotic environmental systems have posed to the development of adequate DA and predictability tools and will provide an outlook on one of such approach, the assimilation in the unstable subspace and on its applications in atmospheric and ocean systems. References:- Carrassi A., M. Bocquet, L. Bertino and G. Evensen, 2018. WIREs Clim Change. 10.1002/wcc.535 - Grudzien C., A. Carrassi and M. Bocquet, 2018. Nonlin. Proc. Geophys., 25, 633-648- Palatella L., A. Carrassi and A. Trevisan, 2013. J. Phys. A: Math. Theor. 46, 254020 [ABSTRACT FROM AUTHOR]

In recent years, there has been increased interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. In this setting, previous studies introduced the contextual formulation of model evidence, or contextual model evidence (CME), and showed that CME can be efficiently computed using a hierarchy of ensemble‐based DA procedures. Although these studies analysed the DA methods most commonly used for operational atmospheric and oceanic prediction worldwide, they did not study these methods in conjunction with localization to a specific domain. Yet, any application of ensemble DA methods to realistic, very high‐dimensional geophysical models requires the implementation of some form of localization. The present study extends CME estimation to ensemble DA methods with domain localization. Domain‐localized CME (DL‐CME) developed in this article is tested for model selection with two models: (a) the Lorenz 40‐variable midlatitude atmospheric dynamics model (Lorenz‐95); and (b) the simplified global atmospheric SPEEDY model. CME is compared to the root‐mean‐square error (RMSE) as a metric for model selection. The experiments show that CME systematically outperforms RMSE in model selection skill, and that this skill improvement is further enhanced by applying localization to the CME estimate using DL‐CME. The potential use and range of applications of CME and DL‐CME as a model selection metric are also discussed. [ABSTRACT FROM AUTHOR]

Hydrographic profiles are crucial observational datasets for constraining ocean models and their vertical structure. In this study, we investigate a key implementation setup for optimising their assimilation into isopycnal ocean models. For this purpose, we use the Norwegian Climate Prediction Model (NorCPM), which is a fully-coupled climate prediction system based on the Norwegian Earth System Model and the ensemble Kalman filter. First, we revisit whether it is more accurate to assimilate observations in their original coordinate (z-level coordinate) or to transform them into isopycnal coordinates prior to assimilation. The analysis is performed with a single assimilation step using synthetic observations that mimic the characteristic properties of hydrographic profiles: varying vertical resolutions, profiles of only temperature and observations only in the top 1000 m. Assimilating profiles in their native coordinate (z-level coordinates) performs best because converting observations into isopycnal coordinates is strongly non-linear which reduces the efficiency of the assimilation. Secondly, we investigate how to set the horizontal localisation radius for our system. A radius that varies with latitude following a bimodal Gaussian function fits the system well. Thirdly, we estimate observation error, which consists of both instrumental error and representativeness error. In the proposed formulation only the instrumental error decreases with the number of observations during superobing, because the representativeness error is dominated by model limitation. Finally, we demonstrate the impact of assimilating hydrographic profiles from the observational EN4 dataset into NorCPM. An analysis of 10 years with monthly assimilation is performed with special focus on assessing the accuracy and the reliability of our analysis. The assimilation of hydrographic profiles into NorCPM is found to efficiently reduce the model bias and error, and the ensemble spread is found to be a reliable estimator for the forecast error in most regions. [ABSTRACT FROM AUTHOR]

Abstract: “Normally Off, Instantly On” applications are becoming common in our environment. They range from healthcare to video surveillance. As the number of applications and their associated performance requirements grow rapidly, more and more powerful, flexible, and power efficient computing units are necessary. In such a context, Field Programmable Gate Arrays (FPGA) architectures present a good trade-off between performance and flexibility. However, they consume high static power and can hardly be associated with power-gating techniques due to their long context-restoring phase. In this paper, we propose to integrate non-volatile resistive memories in the configuration cells and registers in order to instantly restore the FPGA context. If the circuit is in the ‘ON’ state for less than 42% of time, non-volatile FPGA starts saving energy compared to classical FPGA. Finally, when context-saving functionality is included, for a typical application with only 1% of time spent in the ‘ON’ state, the energy gain exceeds 40%. [Copyright &y& Elsevier]

Abstract: This work presents a Non-Volatile SRAM (NV-SRAM) cell, resilient to information loss. The cell features fast storage (20ns) for the operating voltage of 1.0V. The information is backed-up during POWER-DOWN/RESTORE cycle in two bipolar Oxide Resistive RAMs (OxRRAMs). The proposed NV-SRAM is designed with an 8T2R structure using 22nm FDSOI technology and resistive memory devices based on HfO2. The stability and the reliability of the NV-SRAM cell is investigated by taking into account the variability of the transistors. It is shown that high R OFF/R ON is necessary to ensure reliable RESTORE operation and high SRAM yield under cell area and power consumption constraints. [Copyright &y& Elsevier]

The ensemble Klaman filter (EnKF) has been designed as a practical implementation for the extended Kalman filter. In particular, EnKF is able to time propagate huge size covariance matrices thanks to the sampling of the initial distribution and its update during the analysis step. In this contribution we introduce and explore an alternative for the EnKF to implement the extended Kalman filter: the parametric Kalman filter (PKF). The basic idea is to approximate a covariance matrix by a parametric formulation (in place of the ensemble), then to design the evolution of these parameters along the tangent linear propagation and the analysis update. In the forecast step, the numerical cost of the PKF is equivalent to the cost of a single member of an EnKF. We present the formalism for the linear advection-diffusion equation with applications in chemical transport model, and then for the one-dimensional nonlinear advection-diffusion dynamics (Burgers equation). For these illustrations, the parametric model considered is the covariance model based on the diffusion equation, where the parametric Kalman filter describes the dynamics of the forecast error variance and of the local diffusion tensor. [ABSTRACT FROM AUTHOR]