Proposed Data Assimilation Research Articles

A particle filter-based data assimilation framework for discrete event simulations

With the advent of new sensor technologies and communication solutions, the availability of data for discrete event systems has greatly increased. This motivates research on data assimilation for discrete event simulations that has not yet fully matured. This paper presents a particle filter-based data assimilation framework for discrete event simulations. The framework is formally defined based on the Discrete Event System Specification formalism. To effectively apply particle filtering in discrete event simulations, we introduce an interpolation operation that considers the elapsed time (i.e., the time elapsed since the last state transition) when retrieving the model state (which was ignored in related work) in order to obtain updated state values. The data assimilation problem finally boils down to estimating the posterior distribution of a state trajectory with variable dimension. This seems to be problematic; however, it is proven that in practice we can safely apply the sequential importance sampling algorithm to update the random measure (i.e., a set of particles and their importance weights) that approximates this posterior distribution of the state trajectory with variable dimension. To illustrate the working of the proposed data assimilation framework, a case is studied in a gold mine system to estimate truck arrival times at the bottom of the vertical shaft. The results show that the framework is able to provide accurate estimation results in discrete event simulations; it is also shown that the framework is robust to errors both in the simulation model and in the data.

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.

In vivo estimation of elastic heterogeneity in an infarcted human heart

In myocardial infarction, muscle tissue of the heart is damaged as a result of ceased or severely impaired blood flow. Survivors have an increased risk of further complications, possibly leading to heart failure. Material properties play an important role in determining post-infarction outcome. Due to spatial variation in scarring, material properties can be expected to vary throughout the tissue of a heart after an infarction. In this study we propose a data assimilation technique that can efficiently estimate heterogeneous elastic material properties in a personalized model of cardiac mechanics. The proposed data assimilation is tested on a clinical dataset consisting of regional left ventricular strains and in vivo pressures during atrial systole from a human with a myocardial infarction. Good matches to regional strains are obtained, and simulated equi-biaxial tests are carried out to demonstrate regional heterogeneities in stress–strain relationships. A synthetic data test shows a good match of estimated versus ground truth material parameter fields in the presence of no to low levels of noise. This study is the first to apply adjoint-based data assimilation to the important problem of estimating cardiac elastic heterogeneities in 3-D from medical images.

Geophysical and hydrological data assimilation to monitor water content dynamics in the rocky unsaturated zone

In recent years, geophysics is increasingly used to study the flow and transport processes in the vadose zone. Particularly, when the vadose zone is made up of rocks, it is difficult to install sensors in the subsurface to measure hydrological state variables directly. In these cases, the electrical resistivity tomography (ERT) represents a useful tool to monitor the hydrodynamics of the infiltration and to estimate hydraulic parameters and state variables, such as hydraulic conductivity and water content. We propose an integrated approach aimed at predicting water content dynamics in calcarenite, a sedimentary carbonatic porous rock. The uncoupled hydrogeophysical approach proposed consists in 4D ERT monitoring conducted during an infiltrometer test under falling head conditions. Capacitance probes were installed to measure water content at different depths to validate the estimations derived from ERT. A numerical procedure, based on a data assimilation technique, was accomplished by combining the model (i.e., Richards' equation) with the observations in order to provide reliable water content estimations. We have used a new data assimilation method that is easy to implement, based on the ensemble Kalman filter coupled with Brownian bridges. This approach is particularly suitable for strongly non-linear models, such as Richards' equation, in order to take into account both the model uncertainty and the observation errors. The proposed data assimilation approach was tested for the first time on field data. A reasonable agreement was found between observations and predictions confirming the ability of the integrated approach to predict water content dynamics in the rocky subsoil.

Data Assimilation in Air Contaminant Dispersion Using a Particle Filter and Expectation-Maximization Algorithm

The accurate prediction of air contaminant dispersion is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in chemical industry parks. Conventional atmospheric dispersion models can seldom give accurate predictions due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion models, two data assimilation methods (i.e., the typical particle filter & the combination of a particle filter and expectation-maximization algorithm) are proposed to assimilate the virtual Unmanned Aerial Vehicle (UAV) observations with measurement error into the atmospheric dispersion model. Two emission cases with different dimensions of state parameters are considered. To test the performances of the proposed methods, two numerical experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model predictions when the dimension of state parameters is relatively low. In contrast, when the dimension of state parameters becomes higher, the method of particle filter combining the expectation-maximization algorithm performs better in terms of the parameter estimation accuracy. Therefore, the proposed data assimilation methods are able to effectively support air quality monitoring and emergency management in chemical industry parks.

Data Assimilation for Fatigue Life Assessment of RC Bridge Decks Coupled with Path-Integral-Mechanistic Model and Non-Destructive Inspection

Remaining fatigue life of reinforced concrete (RC) slabs subjected to traveling wheel-type loads is estimated by data assimilation procedure, i.e., coupled life-span simulation with inspection data. Multi-scale analysis (MSA) with path-integral-mechanistic models is used for the platform of data assimilation on which the visual inspection of concrete cracking on the members’ surfaces and the acoustic emission tomography (AET) are numerically integrated. For investigating the applicability of the proposed data assimilation, the wheel running experiments of RC slabs was conducted. Both crack patterns (2D) and 3D-AET were measured over the fatigue life till failure. In the pseudo-cracking assimilation, observed cracks are converted to space-averaged surface strains and the internal strain fields are simply assumed by in-plane hypothesis. This pseudo-cracking assimilation brings about fair assessment of the transient maximum deflection, but the residual deformation was found to be overestimated. Another non-destructive inspection data applied in this assimilation is the 3D-AET associated with the acoustic wave velocity, which has much to do with stiffness of some control volume with and without cracking. The AET velocity is converted to the initial fracture parameter of un-cracked concrete based on the elasto-plastic and fracture model used. Although cracking is not explicitly taken into account unlike the pseudo-cracking method, the small number of load repetition automatically generates internal cracks over the volume of analysis domains, and the remaining life of the slabs inspected was successfully estimated.

Personalized glucose forecasting for type 2 diabetes using data assimilation.

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual’s blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.

Data Assimilation of Wildfires with Fuel Adjustment Factors in farsite using Ensemble Kalman Filtering

This paper shows the extension of the wildfire simulation tool FARSITE to allow for data assimilation capabilities on both fire perimeters and fuel adjustment factors to improve the accuracy of wildfire spread predictions. While fire perimeters characterize the overall burn scar of a wildfire, fuel adjustment factors are fuel model specific calibration numbers that adjust the rate of spread for each fuel type independently. Data assimilation updates of both fire perimeters and fuel adjustment factors are calculated from an Ensemble Kalman Filter (EnKF) that exploits the uncertainty information on the simulated fire perimeter, fuel adjustment factors and a measured fire perimeter. The effectiveness of the proposed data assimilation is illustrated on a wildfire simulation representing the 2014 Cocos fire, tracking time varying fuel adjustment factors based on noisy and limited spatial resolution observations of the fire perimeter.

Development of Dynamic Ground Water Data Assimilation for Quantifying Soil Hydraulic Properties from Remotely Sensed Soil Moisture

Several inversion modeling-based approaches have been developed/used to extract soil hydraulic properties (α, n, θres, θsat, Ksat) from remotely sensed (RS) soil moisture footprints. Hydrological models with shallow ground water (SGW) table depths in soils simulate daily root zone soil moisture dynamics based on the extracted soil parameters. The presence of SGW table depths in soils significantly influences model performances; however, SGW table depths are usually unknown in the field, thus, unknown SGW table depths might cause uncertainties in the model outputs. In order to overcome these drawbacks, we developed a dynamic ground water (DGW) data assimilation approach that can consider SGW table depths across time for quantifying effective soil hydraulic properties in the unsaturated zone. In order to verify the DGW data assimilation scheme, numerical experiments comprising synthetic and field validation experiments were conducted. For the numerical studies, the Little Washita (LW) watershed in Oklahoma and Olney (OLN)/Bondville (BOND) sites in Illinois were selected as different hydroclimatic regions. For the synthetic conditions, we tested the DGW scheme using various soil textures and vegetation covers with fixed and dynamically changing SGW table depths across time in homogeneous and heterogeneous (layered) soil columns. The DGW-based soil parameters matched the observations under various synthetic conditions better than those that only consider fixed ground water (FGW) table depths in time. For the field validations, our proposed data assimilation scheme performed well in predicting the soil hydraulic properties and SGW table depths at the point, airborne sensing, and satellite scales, even though uncertainties exist. These findings support the robustness of our proposed DGW approach in application to regional fields. Thus, the DGW scheme could improve the availability and applicability of pixel-scale soil moisture footprints based on satellite platforms.

Enhancing Knowledge in Water Distribution Networks via Data Assimilation

This paper deals with uncertainty estimation and knowledge enhancement in water distribution networks (WDNs). A new three steps data assimilation approach is introduced, which in combination with multi-objective optimization, allows selecting effective and affordable monitoring networks. An innovative cascade of Ensemble Kalman Filters is used to assimilate the information deriving from sensors measuring pressure heads, flow in pipes and demands, with the objective of increasing knowledge while preserving at the same time the structural relationships among state variables. Selection of the most appropriate and economically affordable measurement network, is then based on the derivation of a Pareto front using the NSGA-II algorithm in conjunction with the data assimilation approach. The front is obtained by compromising between the overall sensors cost and the uncertainty reduction (or knowledge enhancement), which is expressed as a function of the Total Variance of state variables. The operational use of the proposed data assimilation approach as well as the effectiveness of the chosen observation network is also demonstrated by showing the reduction of uncertainty deriving from successive assimilations of real-time observations.

TerrSysMP–PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface–subsurface model

Abstract. Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce certain parameterisation errors of single compartment models, model predictions are still prone to uncertainties regarding model input variables. The resulting uncertainties of model predictions can be effectively tackled by data assimilation techniques, which allow one to correct model predictions with observations taking into account both the model and measurement uncertainties. The steadily increasing availability of computational resources makes it now increasingly possible to perform data assimilation also for computationally highly demanding integrated terrestrial system models. However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface–subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). TerrSysMP is connected via a memory-based coupling approach with the pre-existing parallel data assimilation library PDAF (Parallel Data Assimilation Framework). This framework provides a fully parallel modular environment for performing data assimilation for the land surface and the subsurface compartment. A simple synthetic case study for a land surface–subsurface system (0.8 million unknowns) is used to demonstrate the effects of data assimilation in the integrated model TerrSysMP and to assess the scaling behaviour of the data assimilation system. Results show that data assimilation effectively corrects model states and parameters of the integrated model towards the reference values. Scaling tests provide evidence that the data assimilation system for TerrSysMP can make efficient use of parallel computational resources for > 30 k processors. Simulations with a large problem size (20 million unknowns) for the forward model were also efficiently handled by the data assimilation system. The proposed data assimilation framework is useful in simulating and estimating uncertainties in predicted states and fluxes of the terrestrial system over large spatial scales at high resolution utilising integrated models.

Optimization of Parameter Values in the Turbulence Model Aided by Data Assimilation

This study proposes a data assimilation methodology for estimating the optimal parameter values of turbulence models. The proposed methodology was applied to the estimation of the parameter in the modified Menter shear-stress transport turbulence model. For this purpose, a fundamental turbulent flow, namely, the flow over a two-dimensional backward-facing step, was employed. The estimated value of (1.0) differed from its original value (i.e., 0.31). The modified Menter shear-stress transport turbulence model with was validated on several turbulent flow calculations; flows over a two-dimensional backward-facing step and a two-dimensional flat-plate boundary layer, two-dimensional transonic flows around the RAE 2822 airfoil, and three-dimensional transonic flows around the ONERA M6 wing. In simulations, the modified Menter shear-stress transport turbulence model with better modeled the separated and adverse pressure gradient flows than the original modified Menter shear-stress transport turbulence model with . Furthermore, in the absence of separation and adverse pressure gradient flows, the proposed and original modified Menter shear-stress transport turbulence models computed almost the same results. These observations suggest that the proposed data assimilation methodology effectively estimates the optimal parameter values of turbulence models and that the estimated (1.0) improves the performance of the modified Menter shear-stress transport turbulence model over the original value (i.e., 0.31).

Feasibility of improving a priori regional climate model estimates of Greenland ice sheet surface mass loss through assimilation of measured ice surface temperatures

Abstract. The Greenland ice sheet (GrIS) has been the focus of climate studies due to its considerable impact on sea level rise. Accurate estimates of surface mass fluxes would contribute to understanding the cause of its recent changes and would help to better estimate the past, current and future contribution of the GrIS to sea level rise. Though the estimates of the GrIS surface mass fluxes have improved significantly over the last decade, there is still considerable disparity between the results from different methodologies (e.g., Rae et al., 2012; Vernon et al., 2013). The data assimilation approach can merge information from different methodologies in a consistent way to improve the GrIS surface mass fluxes. In this study, an ensemble batch smoother data assimilation approach was developed to assess the feasibility of generating a reanalysis estimate of the GrIS surface mass fluxes via integrating remotely sensed ice surface temperature measurements with a regional climate model (a priori) estimate. The performance of the proposed methodology for generating an improved posterior estimate was investigated within an observing system simulation experiment (OSSE) framework using synthetically generated ice surface temperature measurements. The results showed that assimilation of ice surface temperature time series were able to overcome uncertainties in near-surface meteorological forcing variables that drive the GrIS surface processes. Our findings show that the proposed methodology is able to generate posterior reanalysis estimates of the surface mass fluxes that are in good agreement with the synthetic true estimates. The results also showed that the proposed data assimilation framework improves the root-mean-square error of the posterior estimates of runoff, sublimation/evaporation, surface condensation, and surface mass loss fluxes by 61, 64, 76, and 62 %, respectively, over the nominal a priori climate model estimates.

Improved boundary values of ocean wave fields using a data assimilation scheme

The objective of this study was to assess the accuracy of numerical wave forecasts through data assimilation of boundary values. A sequential data assimilation scheme was adopted to utilise altimeter data. The performance of the system in terms of operational applications, specifically for typhoon waves, was investigated. Two typhoons that occurred in 2006 around Taiwan (Kaemi and Shanshan) were used as case studies. The proposed data assimilation increased the forecast accuracy of the boundary values in terms of the wave parameters, such as the wave heights and periods. The results showed that after the assimilation, the assimilation model was significantly improved, especially the peak value of the wave and time of occurrence of the peak value.

A Particle Batch Smoother Approach to Snow Water Equivalent Estimation

AbstractThis paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%–82% and 60%–68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like the American where the moderate to high forest cover will necessarily obscure more of the snow-covered ground surface than in previously examined, less-vegetated basins. The PBS generally outperformed the EnBS: for snow pillows the PBS RMSE was ~54% of that seen in the EnBS, while for snow courses the PBS RMSE was ~79% of the EnBS. Sensitivity tests show relative insensitivity for both the PBS and EnBS results to ensemble size and fSCA measurement error, but a higher sensitivity for the EnBS to the mean prior precipitation input, especially in the case where significant prior biases exist.

Assimilation of decomposed in situ directional wave spectra into a numerical wave model of typhoon waves

Abstract. The purpose of this study was to enhance the accuracy of numerical wave forecasts through data assimilation during typhoon periods. A sequential data assimilation scheme was modified to enable its use with partitions of directional wave spectra. The performance of the system was investigated with respect to operational applications, specifically for typhoon waves. Two typhoons that occurred in 2006 around Taiwan (Kaemi and Shanshan) were used for this case study. The proposed data assimilation method increased the forecast accuracy in terms of wave parameters, such as wave height and period. After assimilation, the shapes of directional spectra were much closer to those reported from independent observations.

Evaluation of a Strategy for the Assimilation of Satellite Radiance Observations with the Local Ensemble Transform Kalman Filter

Abstract This paper evaluates a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter (LETKF) data assimilation scheme. The assimilation strategy includes a mechanism to select the radiance observations that are assimilated at a given grid point and an ensemble-based observation bias-correction technique. Numerical experiments are carried out with a reduced (T62L28) resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The observations used for the evaluation of the assimilation strategy are AMSU-A level 1B brightness temperature data from the Earth Observing System (EOS) Aqua spacecraft. The assimilation of these observations, in addition to all operationally assimilated nonradiance observations, leads to a statistically significant improvement of both the temperature and wind analysis in the Southern Hemisphere. This result suggests that the LETKF, combined with the proposed data assimilation strategy for the assimilation of satellite radiance observations, can efficiently extract information from radiance observations.

Feasibility of estimating regional mechanical properties of cerebral aneurysmsin vivo

In this article, the authors studied the feasibility of estimating regional mechanical properties in cerebral aneurysms, integrating information extracted from imaging and physiological data with generic computational models of the arterial wall behavior. A data assimilation framework was developed to incorporate patient-specific geometries into a given biomechanical model, whereas wall motion estimates were obtained from applying registration techniques to a pair of simulated MR images and guided the mechanical parameter estimation. A simple incompressible linear and isotropic Hookean model coupled with computational fluid-dynamics was employed as a first approximation for computational purposes. Additionally, an automatic clustering technique was developed to reduce the number of parameters to assimilate at the optimization stage and it considerably accelerated the convergence of the simulations. Several in silico experiments were designed to assess the influence of aneurysm geometrical characteristics and the accuracy of wall motion estimates on the mechanical property estimates. Hence, the proposed methodology was applied to six real cerebral aneurysms and tested against a varying number of regions with different elasticity, different mesh discretization, imaging resolution, and registration configurations. Several in silico experiments were conducted to investigate the feasibility of the proposed workflow, results found suggesting that the estimation of the mechanical properties was mainly influenced by the image spatial resolution and the chosen registration configuration. According to the in silico experiments, the minimal spatial resolution needed to extract wall pulsation measurements with enough accuracy to guide the proposed data assimilation framework was of 0.1 mm. Current routine imaging modalities do not have such a high spatial resolution and therefore the proposed data assimilation framework cannot currently be used on in vivo data to reliably estimate regional properties in cerebral aneurysms. Besides, it was observed that the incorporation of fluid-structure interaction in a biomechanical model with linear and isotropic material properties did not have a substantial influence in the final results.

Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF)

Non-linear data assimilation for a wind-wave dynamical surrogate model in a reduced space is presented. A dynamic artificial neural network is used for surrogate modeling. It provides a fast emulation of a wind-wave model which is used for the evaluation of the system states during a small period of time. The system state consists of wave height and wave direction in the reduced space which is affected by the reduced space wind field. The projection from the full space to the reduced one is performed by a principal component analysis. Ensemble methods require the evaluation of dynamics for a large number of statistical ensembles, so coupling this surrogate (instead of a full model) with an Ensemble Kalman Filter (EnKF) leads to computational efficiency. Application of the procedure is demonstrated through 6 month hindcast study of wind waves over the Caspian Sea using the third-generation wave model and the analysis of the ECMWF wind field. The trained network is embedded into the stochastic environment. Then, the EnKF is used to find estimate of the system states. Experiments show that the proposed data assimilation technique can correct the prediction of the wind-waves requiring just a modest execution time.

A nudging data assimilation algorithm for the identification of groundwater pumping

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measured data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistent physical interpretation for pumping rate identification. The algorithm identifies the unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rates, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show an excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.