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

• Published in: Fluids (Basel) Vol. 5; no. 4; p. 225
• Main Authors: Ahmed, Shady E., Pawar, Suraj, San, Omer
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2020; MDPI
• 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
• ISSN: 2311-5521; 2311-5521
• DOI: 10.3390/fluids5040225

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.