Estimation of volcanic ash emissions using trajectory-based 4D-Var data assimilation

Volcanic ash forecasting is a crucial tool in hazard assessment and operational volcano monitoring. Emission parameters such as plume height, total emission mass, and vertical distribution of the emission plume rate are essential and important in the implementation of volcanic ash models. Therefore, estimation of emission parameters using...

Assimilating aircraft-based measurements to improve forecast accuracy of volcanic ash transport

The 2010 Eyjafjallajokull volcano eruption had serious consequences to civil aviation. This has initiated a lot of research on volcanic ash transport forecast in recent years. For forecasting the volcanic ash transport after eruption onset, a volcanic ash transport and diffusion model (VATDM) needs to be run with Eruption Source Parameters (ESP)...

Assimilating Aircraft-based measurements to improve the State of Distal Volcanic Ash Cloud

Estimation of volcanic ash emissions with satellite data: The inclusion of mass loading and plume height information in modified 4D-Var

Ensemble methods for large scale inverse problems

Variational data assimilation, also sometimes simply called the ‘adjoint method’, is used very often for large scale model calibration problems. Using the available data, the uncertain parameters in the model are identified by minimizing a certain cost function that measures the difference between the model results and the data. A variational...

Why the Euler scheme in particle tracking is not enough: The shallow-sea pycnocline test case

During the last decades, the Euler scheme was the common “workhorse” in particle tracking, although it is the lowest-order approximation of the underlying stochastic differential equation. To convince the modelling community of the need for better methods, we have constructed a new test case that will show the shortcomings of the Euler scheme....

Model-reduced gradient-based history matching

Gradient-based history matching algorithms can be used to adapt the uncertain parameters in a reservoir model using production data. They require, however, the implementation of an adjoint model to compute the gradients, which is usually an enormous programming effort. We propose a new approach to gradient-based history matching which is based...

The ensemble particle filter (EnPF) in rainfall-runoff models

Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the particle filter (PF) both have their own...

The backward Îto method for the Lagrangian simulation of transport processes with large space variations of the diffusivity

Random walk models are a powerful tool for the investigation of transport processes in turbulent flows. However, standard random walk methods are applicable only when the flow velocities and diffusivity are sufficiently smooth functions. In practice there are some regions where the rapid but continuous change in diffusivity may be represented by...

Limitations to upscaling of groundwater flow models dominated by surface water interaction

Different upscaling methods for groundwater flow models are investigated. A suite of different upscaling methods is applied to several synthetic cases with structured and unstructured porous media. Although each of the methods applies best to one of the synthetic cases, no performance differences are formed if the methods were applied to a real...

Model inversion of transient nonlinear groundwater flow models using model reduction

Despite increasing computational resources many high?dimensional applications are impractical for model inversions. In this paper, two methods are presented that are promising for high?dimensional model inversion. The methods draw on proper orthogonal decomposition (POD) and yield reduced models that describe a truncated behavior of the original...

Inverse modeling of groundwater flow using model reduction

Numerical groundwater flow models often have a very high number of model cells (greater than a million). Such models are computationally very demanding, which is disadvantageous for inverse modeling. This paper describes a low?dimensional formulation for groundwater flow that reduces the computational burden necessary for inverse modeling. The...