The Role of Streamline Models for Dynamic Data Assimilation in Petroleum Engineering and Hydrogeology

2008 ◽  
pp. 105-136 ◽  
Author(s):  
Akhil Datta-Gupta ◽  
Deepak Devegowda ◽  
Dayo Oyerinde ◽  
Hao Cheng
Keyword(s):  
Data Assimilation ◽  
Petroleum Engineering ◽  
Dynamic Data

scholarly journals Reviews and syntheses: Flying the satellite into your model: on the role of observation operators in constraining models of the Earth system and the carbon cycle

2017 ◽  
Vol 14 (9) ◽  
pp. 2343-2357 ◽  
Author(s):  
Thomas Kaminski ◽  
Pierre-Philippe Mathieu
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Automatic Differentiation ◽  
Satellite Observations ◽  
Spectral Radiance ◽  
Special Focus ◽  
Earth System ◽  
The Earth ◽  
Retrieval Scheme

Abstract. The vehicles that fly the satellite into a model of the Earth system are observation operators. They provide the link between the quantities simulated by the model and the quantities observed from space, either directly (spectral radiance) or indirectly estimated through a retrieval scheme (biogeophysical variables). By doing so, observation operators enable modellers to properly compare, evaluate, and constrain their models with the model analogue of the satellite observations. This paper provides the formalism and a few examples of how observation operators can be used in combination with data assimilation techniques to better ingest satellite products in a manner consistent with the dynamics of the Earth system expressed by models. It describes commonalities and potential synergies between assimilation and classical retrievals. This paper explains how the combination of observation operators and their derivatives (linearizations) form powerful research tools. It introduces a technique called automatic differentiation that greatly simplifies both the development and the maintenance of code for the evaluation of derivatives. Throughout this paper, a special focus lies on applications to the carbon cycle.

scholarly journals Dynamic Data Assimilation for Improved Streamflow Forecast Using Sensitive Soil Moisture Observations

2022 ◽  
Author(s):  
R Visweshwaran ◽  
Raaj Ramsankaran ◽  
TI Eldho ◽  
S. Lakshmivarahan
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Dynamic Data ◽  
Streamflow Forecast

scholarly journals Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0198586 ◽  
Author(s):  
Xiaodong Luo ◽  
Tuhin Bhakta ◽  
Morten Jakobsen ◽  
Geir Nævdal
Keyword(s):  
Big Data ◽  
Data Assimilation ◽  
Sparse Representation ◽  
Petroleum Engineering

CHAOS AND WEATHER FORECASTING: THE ROLE OF THE UNSTABLE SUBSPACE IN PREDICTABILITY AND STATE ESTIMATION PROBLEMS

2011 ◽  
Vol 21 (12) ◽  
pp. 3389-3415 ◽  
Author(s):  
ANNA TREVISAN ◽  
LUIGI PALATELLA
Keyword(s):  
Data Assimilation ◽  
Chaotic Systems ◽  
Weather Forecasting ◽  
Variational Assimilation ◽  
Forecasting Models ◽  
Operational Forecasting ◽  
Estimation Problems ◽  
Atmospheric Predictability ◽  
Unstable Subspace

In the first part of this paper, we review some important results on atmospheric predictability, from the pioneering work of Lorenz to recent results with operational forecasting models. Particular relevance is given to the connection between atmospheric predictability and the theory of Lyapunov exponents and vectors. In the second part, we briefly review the foundations of data assimilation methods and then we discuss recent results regarding the application of the tools typical of chaotic systems theory described in the first part to well established data assimilation algorithms, the Extended Kalman Filter (EKF) and Four Dimensional Variational Assimilation (4DVar). In particular, the Assimilation in the Unstable Space (AUS), specifically developed for application to chaotic systems, is described in detail.

scholarly journals Ensemble Methods for Dynamic Data Assimilation of Chemical Observations in Atmospheric Models

2011 ◽  
Vol 5 (4) ◽  
pp. 667-692 ◽  
Author(s):  
Adrian Sandu ◽  
Emil Constantinescu ◽  
Gregory R. Carmichael ◽  
Tianfeng Chai ◽  
Dacian Daescu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ensemble Methods ◽  
Atmospheric Models ◽  
Dynamic Data
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