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Additional Reading Resources
PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python
1.
Reference: Fluids; Basel Vol. 5, Iss. 4, (2020): 225. DOI:10.3390/fluids5040225
Retrieved from:
https://www.proquest.com/docview/2467148451/B22396B1FEA749CEPQ/5?accountid=173659
To access the complete document please go to the e-library and copy paste the below DOI
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2467148451
 
Abstract
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.