OpenDA-NEMO framework for ocean data assimilation

Data assimilation methods provide a means to handle the modeling errors and uncertainties in sophisticated ocean models. In this study, we have created an OpenDA-NEMO framework unlocking the data assimilation tools available in OpenDA for use with NEMO models. This includes data assimilation methods, automatic parallelization, and a recently...

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...

A modified POD procedure with patterns in time for parameter estimation

Proper orthogonal decomposition (POD) is a well established model order reduction technique, however its efficiency is limited for advection dominated model. In this report, we explore a modified proper orthogonal decomposition procedure with patterns in time instead of the usual patterns in space. This modified procedure is more suitable for...

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)...

Uncertainty assessment of the geometry of an aquitard using an EnKF and EnKF-GMM

Hydraulic head data is frequently used for the calibration of groundwater flow models, usually with the main objective to improve the model performance. The hydraulic parameters in a groundwater flow model depend on the underlying geometry of the subsurface and the permeability of the soil. In the current calibration routines, the geometry is...

Towards a data assimilation system for morphodynamic modeling

Currently, available models are not able to accurately predict the temporal evolution of coastal morphodynamic processes. The main reason for this, is that essential components governing this evolution are neither fully identified nor understood. Additionally, there is high uncertainty in some of the parameterizations and their input parameters....

Ensemble-Based History Matching for Channelized Petroleum Reservoirs

The oil industry is at the backbone of global economy and, as natural resources are becoming scarce, there is a pressing need for efficient extraction strategies. This has led to the development of reservoir models and simulators, able to predict future field behaviour, when paired with accurate geological information. However, the data obtained...

Improving the Algal Bloom Prediction in the North Sea by Ensemble Kalman Filtering in the GEM/BLOOM Model

The ecological state of the North Sea surface water can be indicated by ocean variables such as the Chlorophyll-a (Chlfa) concentration. Chlfa is the principal photosynthetic pigment and is common to all phytoplankton and can therefore be used as a measure of phytoplankton biomass. The GEM/BLOOM model developed at Deltares is a generic...

Data Assimilation for Marine Ecosystem Models

In the presence of environmental issues caused by climate change and eutrophication, ecosystem models capable of providing accurate predictions are of considerable interest. Even the most advanced ecological models are not capable of reproducing reality perfectly, therefore it is essential to integrate them with the available observations. Data...

Quantification of the impact of data in reservoir modeling

Global energy use is increasing. As societies advance, they will continue to need energy to power residential and commercial buildings, in the industrial sector, for transportation and other vital services. To satisfy this rising demand, liquid, natural gas, coal, nuclear power and renewable fuel sources are extensively developed. Particularly...

Model-reduced gradient-based history matching

Since the world's energy demand increases every year, the oil & gas industry makes a continuous effort to improve fossil fuel recovery. Physics-based petroleum reservoir modeling and closed-loop model-based reservoir management concept can play an important role here. In this concept measured data are used to improve the geological model, while...

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...

A Generic Software Framework for Data Assimilation and Model Calibration

The accuracy of dynamic simulation models can be increased by using observations in conjunction with a data assimilation or model calibration algorithm. However, implementing such algorithms usually increases the complexity of the model software significantly. By using concepts from object oriented programming, this thesis proposes a software...

Improving the operational forecasting system of the stratified flow in Osaka Bay using an ensemble Kalman filter–based steady state Kalman filter

Numerical models of a water system are always based on assumptions and simplifications that may result in errors in the model's predictions. Such errors can be reduced through the use of data assimilation and thus can significantly improve the success rate of the predictions and operational forecasts. The ensemble Kalman filter (EnKF) is a...

Error subspace filtering for atmospheric chemistry data assimilation modeling

Abstract not available

Data assimilation in air pollution modelling