Physics-Incorporated Framework for Emulating Atmospheric Radiative Transfer and the Related Network Study

Yao, Yichen; Zhong, Xiaohui; Zheng, Yongjun; Wang, Zhibin

doi:10.1002/essoar.10512196.1

Cited by 1 publication

(3 citation statements)

References 22 publications

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“…Many researchers (Chevallier et al, 2000;Krasnopolsky et al, 2010;Song and Roh, 2021) have previously used the FC networks to replace the radiation schemes within the operational NWP models. Additionally, Yao et al (2022) demonstrated that the bidirectional long short-term memory (Bi-LSTM) achieves the best accuracy in emulating radiation. Therefore, this study has trained and tested both FC networks and Bi-LSTM models.…”

Section: Dl-based Radiation Emulators and Offline Evaluationsmentioning

confidence: 99%

“…Models A and D, as well as models B and E, have approximately the same number of parameters, respectively. More details about the model training and comparisons can be referenced inYao et al (2022). All the DL-based emulators have been converted to ONNX and run using the ONNX Runtime library version 1.7.…”

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confidence: 99%

“…It was also illustrated that DL-based parameterizations' computational efficiency and accuracy should be balanced to achieve the best overall performance for operational applications.Code and data availability. The source code and data used in this are available at https://doi.org/10.5281/zenodo.7056166(Zhong et al, 2022) Author contributions. Y.Y.…”

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confidence: 99%

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WRF-DL v1.0: A Bridge between WRF v4.3 and Deep Learning Parameterizations and its Application to Atmospheric Radiative Transfer

Zhong

Yao

et al. 2022

Preprint

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Abstract. In numerical weather prediction (NWP) models, physical parameterization schemes are the most computationally expensive components, despite being greatly simplified. In the past few years, an increasing number of studies have demonstrated that deep learning (DL) parameterizations of subgrid physics have the potential to accelerate and even outperform conventional physic-based schemes. However, as the DL models are commonly implemented using the DL libraries written in Python, very few DL-based parameterizations have been successfully integrated with NWP models due to the difficulty of embedding Python functions into Fortran-based NWP models. To address this issue, we developed a coupler to allow the DL-based parameterizations to be coupled with a widely used NWP model, i.e., the Weather and Research Forecasting (WRF) model. Similar to the WRF I/O methodologies, the coupler provides the options to run the DL model inference with exclusive processors or the same processors for WRF calculations. In addition, to demonstrate the effectiveness of the coupler, the DL-based radiation emulators are trained and coupled with the WRF model successfully.

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Section: Dl-based Radiation Emulators and Offline Evaluationsmentioning

confidence: 99%

mentioning

confidence: 99%