Emulation of an atmospheric gas-phase chemistry solver through deep learning: Case study of Chinese Mainland

Liu, Chang; Zhang, Hairui; Cheng, Zhiwei; Shen, Jun; Zhao, Junhao; Wang, Yichao; Wang, Shuo; Cheng, Yun

doi:10.1016/j.apr.2021.101079

Cited by 11 publications

(18 citation statements)

References 29 publications

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“…The online ML solver embedded within GEOS-Chem performs the chemical integration 5× faster than the reference Super-Fast simulation (single Intel Broadwell CPU core; 2.10 GHz). This speedup is smaller than in Kelp et al (2020) and Liu et al (2021) because the Super-Fast mechanism is simpler and because of the overhead in accessing Python code at each time step. Further speedup could be achieved by reading the trained ML solver parameters through text files or by writing them in Fortran.…”

Section: One-year Simulation Testing Of Online ML Solvermentioning

confidence: 93%

“…First, random forest algorithms are much slower. Keller and Evans (2019) found that their random forest solver was 85% slower than the reference Rosenbrock solver, while neural networks should be much faster (Kelp et al, 2020;Liu et al, 2021). Second, random forests are not easily amenable to online training because the growing of the architecture to incorporate more trees and branches further slows performance (Lakshminarayanan et al, 2015), whereas online neural network training simply updates parameters.…”

Section: Offline and Online Trainingmentioning

confidence: 99%

“…They found that they could compress the 101-species dimension of their mechanism into fewer than 20 features without significant error penalty, and that they could avoid error growth over a 1-week integration time. Liu et al (2021) developed a gas-phase neural network solver for the CMAQ regional CTM over China, combining a standard implicit solver for radicals and oxidants with a ML solver for volatile organic compounds (VOCs). They achieved an order-of-magnitude speedup over a one-month simulation but with error growth over remote ocean grid cells.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Online-Learned Neural Network Chemical Solver for Stable Long-Term Global Simulations of Atmospheric Chemistry

Kelp¹,

Jacob²,

Lin³

et al. 2022

Preprint

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A major computational barrier in global modeling of atmospheric chemistry is the numerical integration of the coupled kinetic equations describing the chemical mechanism. Machine-learned (ML) solvers can offer order-of-magnitude speedup relative to conventional implicit solvers, but past implementations have suffered from fast error growth and only run for short simulation times (<1 month). A successful ML solver for global models must avoid error growth over year-long simulations and allow for re-initialization of the chemical trajectory by transport at every time step. Here we explore the capability of a neural network solver equipped with an autoencoder to achieve stable full-year simulations of tropospheric oxidant chemistry in the global 3-D GEOS-Chem model, replacing its standard mechanism (228 species) by the Super-Fast mechanism (12 species) to avoid the curse of dimensionality. We find that online training of the ML solver within GEOS-Chem is essential for accuracy, whereas offline training from archived GEOS-Chem inputs/outputs produces large errors. After online training we achieve stable 1-year simulations with five-fold speedup compared to the standard implicit Rosenbrock solver with global tropospheric normalized mean biases of -0.3% for ozone, 1% for hydrogen oxide radicals, and -5% for nitrogen oxides. The ML solver captures the diurnal and synoptic variability of surface ozone at polluted and clean sites. There are however large regional biases for ozone and NOx under remote conditions where chemical aging leads to error accumulation. These regional biases remain a major limitation for practical application, and ML emulation would be more difficult in a more complex mechanism.

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Section: One-year Simulation Testing Of Online ML Solvermentioning

confidence: 93%

Section: Offline and Online Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Online-Learned Neural Network Chemical Solver for Stable Long-Term Global Simulations of Atmospheric Chemistry

Kelp¹,

Jacob²,

Lin³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…First, random forest algorithms are much slower. Keller and Evans (2019) found that their random forest solver was 85% slower than the reference Rosenbrock solver, while neural networks should be much faster (Kelp et al., 2020; Liu et al., 2021). Second, random forests are not easily amenable to online training because the growing of the architecture to incorporate more trees and branches further slows performance (Lakshminarayanan et al., 2015), whereas online neural network training simply updates parameters.…”

Section: Methodsmentioning

confidence: 99%

“…Liu et al. (2021) developed a gas‐phase neural network solver for the CMAQ regional CTM over China, combining a standard implicit solver for radicals and oxidants with an ML solver for volatile organic compounds (VOCs). They achieved an order of magnitude speedup over a 1‐month simulation but with error growth over remote ocean grid cells.…”

Section: Introductionmentioning

confidence: 99%

An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry

Kelp

Jacob

Lin

et al. 2022

J Adv Model Earth Syst

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Global modeling of atmospheric chemistry is a grand computational challenge due to the large number of coupled chemical species, the nonlinearity and numerical stiffness of chemical mechanisms, and the interactions with transport on all scales. The U.S. National Research Counci's National Strategy for Advancing Climate Modeling identifies atmospheric chemistry as a priority frontier for Earth System Model (ESM) development (National Research Council, 2012). Current atmospheric chemistry models integrate the coupled chemical kinetic equations for the mechanism species over model time steps by using high-order implicit numerical solvers, but these solvers are expensive (Sandu et al., 1997) and often dominate the cost of an atmospheric simulation (Eastham et al., 2018). Here, we explore the potential of machine learning (ML) neural network algorithms to dramatically reduce the computational intensity of atmospheric chemistry in global simulations.

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Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

Zhang

et al. 2022

Front. Environ. Sci. Eng.

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Emulation of an atmospheric gas-phase chemistry solver through deep learning: Case study of Chinese Mainland

Cited by 11 publications

References 29 publications

An Online-Learned Neural Network Chemical Solver for Stable Long-Term Global Simulations of Atmospheric Chemistry

An Online-Learned Neural Network Chemical Solver for Stable Long-Term Global Simulations of Atmospheric Chemistry

An Online‐Learned Neural Network Chemical Solver for Stable Long‐Term Global Simulations of Atmospheric Chemistry

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

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