Efficient estimation of space varying parameters in numerical models using non-intrusive subdomain reduced order modeling

A reduced order modeling algorithm for the estimation of space varying parameter patterns in numerical models is proposed. In this approach domain decomposition is applied to construct separate approximations to the numerical model in every subdomain. We introduce a new local parameterization that decouples the computational cost of the...

Source backtracking for dust storm emission inverssion using an adjoint method: case study of Northeast China

Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations. In practice, emission uncertainties are...

Deep-learning inversion to efficiently handle big-data assimilation: Application to seismic history matching

Seismic history matching can play a key role in geological characterization and uncertainty quantification. However, challenges related to intensive computational demands and complexity restricts its application in many practical cases. This paper presents a conceptual deep-learning-based framework fully deployed in the popular Pytorch...

Machine learning for observation bias correction with application to dust storm data assimilation

Data assimilation algorithms rely on a basic assumption of an unbiased observation error. However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice. Assimilation analysis might diverge from reality since the data assimilation itself cannot distinguish whether the differences...

Magnetic Susceptibility Estimation for Magnetostatics

This paper presents a parameter estimation method to determine the linear behavior of an object constructed of thin plates. Based on the magnetostatic field equations, an integral equation is derived that fully determines the induced magnetization, whenever the spatial magnetic susceptibility distribution is known. This forward problem is used...

Dust Emission Inversion Using Himawari-8 AODs Over East Asia: An Extreme Dust Event in May 2017

Aerosol optical depths (AODs) from the new Himawari-8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a...

Spatially varying parameter estimation for dust emissions using reduced-tangent-linearization 4DVar

In previous studies, a number of model-based dust forecasts and early warning systems have been developed for the prevention of environmental impacts due to dusts. However, the accuracy of the model is limited by imperfect identification of dust emissions, in particular by the friction velocity threshold parameterization in the emission process....

Accelerating volcanic ash data assimilation using a mask-state algorithm based on an ensemble Kalman filter: A case study with the LOTOS-EUROS model (version 1.10)

In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity...

Curriculum vitae of the LOTOS–EUROS (v2.0) chemistry transport model

The development and application of chemistry transpor tmodels has a long tradition. Within the Netherlands the LOTOS–EUROS model has been developed by a consortium of institutes, after combining its independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the...

Evaluation criteria on the design for assimilating remote sensing data using variational approaches

Remote sensing, as a powerful tool for monitoring atmospheric phenomena, has been playing an increasingly important role in inverse modeling. Remote sensing instruments measure quantities that often combine several state variables as one. This creates very strong correlations between the state variables that share the same observation variable....

Data assimilation for volcanic ash plumes using a satellite observational operator: A case study on the 2010 Eyjafjallajökull volcanic eruption

Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and...

Estimation of volcanic ash emissions through assimilating satellite data and ground‐based observations

In this paper, we reconstruct the vertical profile of volcanic ash emissions by assimilating satellite data and ground‐based observations using a modified trajectory‐based 4D‐Var (Trj4DVar) approach. In our previous work, we found that the lack of vertical resolution in satellite ash column data can result in a poor estimation of the injection...

Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements

The forecast accuracy of distal volcanic ash clouds is important for providing valid aviation advice during volcanic ash eruption. However, because the distal part of volcanic ash plume is far from the volcano, the influence of eruption information on this part becomes rather indirect and uncertain, resulting in inaccurate volcanic ash forecasts...

A probabilistic parametrization for geological uncertainty estimation using the ensemble Kalman filter (EnKF)

In the past years, many applications of historymatching methods in general and ensemble Kalman filter in particular have been proposed, especially in order to estimate fields that provide uncertainty in the stochastic process defined by the dynamical system of hydrocarbon recovery. Such fields can be permeability fields or porosity fields, but...

Multiscale ensemble filtering for reservoir engineering applications

Reservoir management requires periodic updates of the simulation models using the production data available over time. Traditionally, validation of reservoir models with production data is done using a history matching process. Uncertainties in the data, as well as in the model, lead to a nonunique history matching inverse problem. It has been...