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DA_Tutorial

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This is a 'hands-on' tutorial for the RIKEN International School on Data Assimilation (RISDA2018).

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# DA_Tutorial
This open source project originated as a 'hands-on' tutorial for the RIKEN International School on Data Assimilation [RISDA2018](https://www.data-assimilation.riken.jp/risda2018/index.html).

![image](https://github.com/user-attachments/assets/40190150-ef98-4fee-8b73-d7b9e8e6000b)

The tutorial focuses on the Lorenz-63 (Lorenz 1963) model. The purpose is to assimilate observational data drawn from a 'true' driver
system to synchronize a response system (the data assimilation system). Some of the most popular data assimilation methods are made
available for experimentation, including:
Optimal Interpolation
3D-Var
Ensemble Kalman Filter
4D-Var
Particle Filter
Hybrid Filter

This is based on code developed for:
Penny, S.G., 2017: Mathematical foundations of hybrid data assimilation from a synchronization perspective
Chaos 27, 126801 (2017); https://doi.org/10.1063/1.5001819. https://aip.scitation.org/doi/full/10.1063/1.5001819

The second application currently available is the Modular Arbitrary Order Ocean Atmospehre Model (MAOOAM;
https://github.com/Climdyn/MAOOAM). This is an example of a coupled data assimilation system (CDA; the model is a 2-layer atmosphere
and 1-layer ocean), in which multiple scales must be addressed simultaneously. The code has been developed for and by the class of
AOSC658E at the University of Maryland to study CDA.

~Stephen G. Penny

Currently:

Head of Weather  
[Sofar Ocean](https://www.sofarocean.com/)  
San Francisco, CA

Research Affiliate  
Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder

Visiting Professor  
University of Maryland College Park

DA_Tutorial

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