DATeS: a highly extensible data assimilation testing suite v1.0

Attia, Ahmed; Sandu, Adrian

doi:10.5194/gmd-12-629-2019

Cited by 8 publications

(10 citation statements)

References 59 publications

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“…Applying the approach presented in 3.1 to automatically tune the space-time inflation factor requires choosing a proper regularization parameter α. For a fair comparison with the benchmark results, here we solve the optimization problem (14), where the entries of λ are bounded withing the interval [1, 1.5]. To analyze the behavior of the proposed algorithm, we show results for multiple choices of the regularization parameter α.…”

Section: Adaptive Inflation Resultsmentioning

confidence: 99%

“…Solution of the A-optimality optimization problem. A gradient-based approach is typically followed for solving (14). The gradient of the objective in ( 14) is summarized by…”

Section: Oed Adaptive Inflationmentioning

confidence: 99%

“…This is done as follows. Once the forecast ensemble is made available, the optimization problem (14), is solved by using a gradient-based optimization procedure to find the A-optimal inflation factor λ A−OED . The inflated background covariance matrix ( 7) is calculated by using λ = λ A−OED and is used in the analysis step of the filter.…”

Section: Oed Adaptive Inflationmentioning

confidence: 99%

“…To study the performance of the proposed adaptive approach, we employ the two-layer Lorenz-96 model [52]. The numerical experiments in this work are implemented in Python by using DATeS [14]. The source code for all experiments carried out in this work is available from [10].…”

Section: Numerical Experimentsmentioning

confidence: 99%

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An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filters

Attia,

Constantinescu

2018

Preprint

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We develop an optimal experimental design framework for adapting the covariance inflation and localization in data assimilation problems. Covariance inflation and localization are ubiquitously employed to alleviate the effect of using ensembles of finite sizes in all practical data assimilation systems. The choice of both the inflation factor and the localization radius can have a significant impact on the performance of the assimilation scheme. These parameters are generally tuned by trial and error, rendering them expensive to optimize in practice. Spatially and temporally varying inflation parameter and localization radii have been recently proposed and have been empirically proven to enhance the performance of the employed assimilation filter. In this study, we present a variational framework for adaptive tuning of the inflation and localization parameters. Each of these parameters is optimized independently, with an objective to minimize the uncertainty in the posterior state. The proposed framework does not assume uncorrelated observations or prior errors and can in principle be applied without expert knowledge about the model and the observations. Thus, it is adequate for handling dense as well as sparse observational networks. We present the mathematical formulation, algorithmic description of the approach, and numerical experiments using the two-layer Lorenz-96 model.

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Section: Adaptive Inflation Resultsmentioning

confidence: 99%

“…Solution of the A-optimality optimization problem. A gradient-based approach is typically followed for solving (14). The gradient of the objective in ( 14) is summarized by…”

Section: Oed Adaptive Inflationmentioning

confidence: 99%

Section: Oed Adaptive Inflationmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

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An Optimal Experimental Design Framework for Adaptive Inflation and Covariance Localization for Ensemble Filters

Attia,

Constantinescu

2018

Preprint

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“…Meanwhile, this tutorial will give the reader a jump-start that hopefully shortens the learning curve of more advanced packages. A Python-based DA testing suite has been also designed to compare different methodologies [4].…”

Section: Introductionmentioning

confidence: 99%

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

Ahmed

Pawar

San

2020

Fluids

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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.

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