Neural networks for emulation variational method for data assimilation in nonlinear dynamics

Furtado, Helaine C. M.; Velho, Haroldo Fraga de Campos; Macau, Elbert E. N.

doi:10.1088/1742-6596/285/1/012036

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In this context, neural networks have been applied to emulate several data assimilation methods, such as: Kalman filter Härter and de Campos Velho, 2010), ensemble Kalman filter (Cintra and Campos Velho, 2018), particle Filter (Furtado et al, 2008), and variational methods (Furtado et al, 2011;Wu et al, 2021). Data assimilation by neural networks has been applied to space weather , 2D shallow water for ocean circulation (Sambatti et al, 2018), urban air pollution (Casas et al, 2020), hydrology (Boucher et al, 2020), medicine (Arcucci et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro

Almeida

Velho²,

França³

et al. 2022

Preprint

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Abstract. The practical feasibility of neural networks models for data assimilation using local observations data in the WRF model for the Rio de Janeiro metropolitan region in Brazil is evaluated. Surface and multi-level variables retrieved from airport meteorological stations are used: air temperature, relative humidity, and wind (speed and direction). Also, 6-hour forecast from WRF high-resolution simulations are used – domain centered in the Rio de Janeiro city with nested grids of 8 and 2.6 km. Periods of 168 h from 2015–2019 are used with 6 h and 12 h assimilation cycles for surface and upper-air data, respectively, applied to 6-hour forecast fields. The observed data (interpolated to grid points close to airport locations and influence computed in its surroundings) and short-range forecasts are used as input for training model and the 3D-Var analysis on 6-hour forecast fields for each grid point is used as target variable. The neural network models are built using two different approaches: WEKA mul- tilayer perceptron model and TensorFlow’s deep learning implementation. The year of 2019 is used as an independent dataset for forecast validation from the trained models. Results employing 6-hour forecast fields with neural network models are able to emulate the 3D-Var results for surface and multi-level variables, with better results for the NN-TensoFlow implementation. The main result refers to CPU time reduction enabled by the neural networks models, reducing the data assimilation CPU-time by 121 times and 25 times for NN-TensorFlow and NN-WEKA, respectively, in comparison to the 3D-Var method under the same hardware configurations.

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Section: Introductionmentioning

confidence: 99%

Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro

Almeida

Velho²,

França³

et al. 2022

Preprint

Self Cite

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“…The machine learning methods for DA are summarized in Table 5. Wave model [111] MLP PF Lorenz model [120] MLP KF Three-wave model [121] MLP Variational Lorenz model [122] MLP Variational Wave model [107] Elman KF Shallow water 1D model (DYNAMO-1D) [104] RBF KF Shallow water 1D model (DYNAMO-1D) [112] MLP LETKF Atmospheric general circulation model (FSUGSM) [123] MLP LETKF Atmospheric general circulation model (SPEEDY) [124] Mixed Type KF Satellite-Derived Sea Surface Temperature data [125] Fully Connected Variational , KF Dot system and Lorenz models [117] LSTM Variational (3DVAR) CFD model (Fluidity) [114] Elman Variational Dot system and Lorenz models [102] LSTM KF CFD model (Fluidity) [101] MLP Variational Lorenz model [126] LSTM Variational Lorenz model [127] MLP Variational and EnKF Lorenz model [118] LSTM KF Oxygen diffusion across the Blood-Brain Barrier model…”

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confidence: 99%

Data Assimilation for Agent-Based Models

Ghorbani,

Nazari-Heris

et al. 2023

Mathematics

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This article presents a comprehensive review of the existing literature on the topic of data assimilation for agent-based models, with a specific emphasis on pedestrians and passengers within the context of transportation systems. This work highlights a plethora of advanced techniques that may have not been previously employed for online pedestrian simulation, and may therefore offer significant value to readers in this domain. Notably, these methods often necessitate a sophisticated understanding of mathematical principles such as linear algebra, probability theory, singular value decomposition, optimization, machine learning, and compressed sensing. Despite this complexity, this article strives to provide a nuanced explanation of these mathematical underpinnings. It is important to acknowledge that the subject matter under study is still in its nascent stages, and as such, it is highly probable that new techniques will emerge in the coming years. One potential avenue for future exploration involves the integration of machine learning with Agent-based Data Assimilation (ABDA, i.e., data assimilation methods used for agent-based models) methods.

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Neural networks for emulation variational method for data assimilation in nonlinear dynamics

Cited by 2 publications

References 7 publications

Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro

Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro

Data Assimilation for Agent-Based Models

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