🌎 TEDA - A Computational Toolbox for Teaching Ensemble Based Data Assimilation

Introducing TEDA: The Premier Python Toolbox for Teaching Ensemble-based Data Assimilation

We are thrilled to present TEDA, a cutting-edge Python toolbox designed to facilitate the teaching and learning of ensemble-based data assimilation (DA). TEDA caters to the needs of educators and learners alike, offering a comprehensive platform to explore the captivating world of meteorological anomalies, climate change, and DA methodologies.

TEDA stands out as an exceptional resource in the landscape of DA software, as it prioritizes the educational experience. While operational software tends to focus on practical applications, TEDA empowers undergraduate and graduate students by providing an intuitive platform to grasp the intricacies of ensemble-based DA concepts and foster enthusiasm for scientific exploration.

Equipped with a diverse range of features and functionalities, TEDA enriches the learning process by offering powerful visualization tools. Students can delve into error statistics, model errors, observational errors, error distributions, and the dynamic evolution of errors. These visualizations provide multiple perspectives on numerical results, enabling students to develop a deep understanding of ensemble-based DA principles.

TEDA offers numerous ensemble-based DA methods, allowing students to explore an extensive range of techniques. From the stochastic ensemble Kalman filter (EnKF) to dual EnKF formulations, EnKF via Cholesky decomposition, EnKF based on modified Cholesky decomposition, EnKF based on B-localization, and beyond, TEDA equips learners with a comprehensive toolkit to study and compare various DA methodologies.

To enhance the learning experience further, TEDA incorporates a selection of captivating toy models. These models, including the Duffing equation (2 variables), the Lorenz 63 model (3 variables), and the Lorenz 96 model (40 variables), exhibit chaotic behavior under specific parameter configurations. Such models provide an excellent opportunity for students to experiment, validate, and gain hands-on experience with diverse DA methods.

TEDA’s foundation is rooted in the Object-Oriented Programming (OOP) paradigm, enabling effortless integration of new techniques and models. This ensures that TEDA remains at the forefront of the rapidly evolving field of ensemble-based DA, allowing educators and researchers to incorporate the latest advancements into their teaching and experimentation.

Embark on a captivating journey of discovery and knowledge with TEDA as it unveils the secrets of data assimilation. Simulate various DA scenarios, experiment with different model configurations, and witness firsthand the transformative impact of ensemble-based DA methods on forecast accuracy and data analysis.

Unlock your true potential in the realm of data assimilation with TEDA. Whether you are an enthusiastic student or a dedicated educator, TEDA stands as your ultimate companion in unraveling the complexities of ensemble-based DA.

Keywords: Data Assimilation, Ensemble Kalman Filter, Education, Python.

💻 GitHub Repo - https://github.com/enino84/TEDA

🚀 How easy is to use TEDA?

Choose the desired method from the pool of available methods:

from analysis.analysis_enkf_modified_cholesky import AnalysisEnKFModifiedCholesky

Select a model to perform the simulations:

model = Lorenz96()

Create a background object with the desired parameters:

background = Background(model, ensemble_size=20)

Create an analysis object with the desired method and its parameters + the chosen model:

analysis = AnalysisEnKFModifiedCholesky(model, r=2)

Define the observation parameters:

observation = Observation(m=32, std_obs=0.01)

Set up the parameters for the simulation:

params = {'obs_freq': 0.1, 'obs_times': 10, 'inf_fact': 1.04}
simulation = Simulation(model, background, analysis, observation, params=params)

and then, run the simulation!

simulation.run()

You can then request the backgound and analysis errors per assimilation step for plotting purposes or statistical computations:

import matplotlib.pyplot as plt

errb, erra = simulation.get_errors()

plt.figure(figsize=(12, 10))
plt.plot(np.log10(errb),'-ob')
plt.plot(np.log10(erra),'-or')
plt.show()

✔️ Supported methods

Analysis (Abstract class for analysis methods)

AnalysisEnKF (Analysis EnKF full covariance matrix)

AnalysisEnKFBLoc (Analysis EnKF B-Loc)

AnalysisEnKFCholesky (EnKF implementation Cholesky)

AnalysisEnKFModifiedCholesky (Analysis EnKF Modified Cholesky decomposition)

AnalysisEnKFNaive (Analysis EnKF Naive)

AnalysisEnKFShrinkagePrecision (Analysis EnKF Shrinkage Precision)

AnalysisEnSRF (Analysis EnSRF)

AnalysisETKF (Analysis Ensemble Transform Kalman Filter (ETKF))

AnalysisLEnKF (Analysis LEnKF)

AnalysisLETKF (Analysis Local Ensemble Transform Kalman Filter (LETKF))