PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python

Dynamic data assimilation offers a suite of algorithms that merge measurement data with numerical simulations to predict accurate state trajectories. Meteorological centers rely heavily on data assimilation to achieve trustworthy weather forecast. With the advance in measurement systems, as well as...

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Bibliographic Details
Published in:Fluids (Basel) 2020-12, Vol.5 (4), p.225
Main Authors: Ahmed, Shady E., Pawar, Suraj, San, Omer
Format: Article
Language:English
Subjects:
Algorithms
Computational fluid dynamics
Data assimilation
Data collection
Fluid flow
forward sensitivity
Kalman filtering
Kalman filters
Learning curves
MATHEMATICS AND COMPUTING
measurements fusion
Meteorology
Noise
Numerical prediction
Simulation
Variables
variational and sequential methods
Weather forecasting
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Summary:Dynamic data assimilation offers a suite of algorithms that merge measurement data with numerical simulations to predict accurate state trajectories. Meteorological centers rely heavily on data assimilation to achieve trustworthy weather forecast. With the advance in measurement systems, as well as the reduction in sensor prices, data assimilation (DA) techniques are applicable to various fields, other than meteorology. However, beginners usually face hardships digesting the core ideas from the available sophisticated resources requiring a steep learning curve. In this tutorial, we lay out the mathematical principles behind DA with easy-to-follow Python module implementations so that this group of newcomers can quickly feel the essence of DA algorithms. We explore a series of common variational, and sequential techniques, and highlight major differences and potential extensions. We demonstrate the presented approaches using an array of fluid flow applications with varying levels of complexity.
ISSN:2311-5521
2311-5521
DOI:10.3390/fluids5040225