Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

Jin, Wei; Zhang, Wei; Hu, Jie; Weng, Bin; Huang, Tianqiang; Chen, Jiazhen

doi:10.3389/feart.2021.760766

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Han et al 2021, Horat andLerch 2023). A residual network (ResNet) is helpful in correcting subseasonal forecasts of extreme high temperatures (Jin et al 2022). Although DL models have made significant achievements in the bias correction of subseasonal dynamical forecasts, the application of DL models still has great untapped potential in this area.…”

Section: Introductionmentioning

confidence: 99%

Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China

Nie,

Sun

2024

Environ. Res. Lett.

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Subseasonal precipitation forecasting is challenging but critical for water management, energy supply, and disaster prevention. To improve regional subseasonal precipitation prediction, previous studies have proposed a dynamical-statistical projection model (DSPM). In this study, we develop a new method that combines the DSPM and deep learning (DL), called the DL-DSPM. The DSPM is developed using the observed relationships between large-scale atmospheric circulations and regional precipitation, and the dynamical forecasted atmospheric circulations from the European Centre for Medium-Range Weather Forecasts (ECMWF) model. The DL-DSPM improves upon the DSPM by correcting biases in atmospheric circulation forecasts from the ECMWF model using two DL models, namely, residual network and U-Net models. In the case of Southwest China (SWC), DL models can improve atmospheric circulation forecasts at lead times beyond 5 pentads, including large-scale drivers of SWC precipitation variability. The DL-DSPM outperforms the ECMWF model and DSPM forecasts in predicting precipitation anomalies beyond 4 and 5 pentads over most SWC regions, respectively. In addition, the DL-DSPM is more skillful than the ECMWF model and DSPM in predicting extreme precipitation events more than 4 pentads in advance. The successful combination of DL and the DSPM provides a new possible direction for DL applications in subseasonal precipitation forecasting.

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Section: Introductionmentioning

confidence: 99%

Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China

Nie,

Sun

2024

Environ. Res. Lett.

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“…According to the study of Rasp and Thuerey [26], the use of multiple layers of residuals can always retain the data features of the previous layer while continuously digging deeper into the data relationship so that the network can memorize previous information in the process of extracting information. Therefore, residual structure has an excellent feature-extraction ability [27], and we attempt to apply this structure to the field of subseasonal prediction. However, with the extension of forecast lead time, the prediction results gradually smoothen [28] and tend to become low-frequency signals of the atmosphere [25].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Global Subseasonal Forecast for Intraseasonal Oscillation Components

Shen,

Lu,

Wang

et al. 2023

Atmosphere

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As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and deep-learning models for subseasonal forecasts, as forecast times increase, the prediction skill for high-frequency components will decrease, as the lead time is already far beyond the predictability. Meanwhile, intraseasonal low-frequency components are essential to the change in general circulation on subseasonal timescales. In this paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the intraseasonal oscillation (ISO) components of atmospheric signals and used an improved deep-learning model (SE-ResNet) to train and predict the ISO components of geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results show that the 10–30 day prediction performance of the SE-ResNet model is better than that of the model trained directly with original data. Compared with other models/methods, this model has a good ability to depict the subseasonal evolution of the ISO components of Z500 and T850. In particular, although the prediction results from the Climate Forecast System Version 2 have better performance through 10 days, the SE-ResNet model is substantially superior to CFSv2 through 10–30 days, especially in the middle and high latitudes. The SE-ResNet model also has a better effect in predicting planetary waves with wavenumbers of 3–8. Thus, the application of data-driven subseasonal forecasts of atmospheric ISO components may shed light on improving the skill of seasonal forecasts.

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“…Machine learning can provide a potential approach to the development of S2S prediction systems with significantly reduced computational costs (Weyn et al, 2021). Residual structure has an excellent feature extraction ability (Jin et al, 2021), so we attempt to apply this structure to the field of subseasonal prediction. However, with the extension of forecast time, the prediction results will gradually smoothen (Rasp et al, 2020) and tend to become low-frequency signals of the atmosphere (Weyn et al, 2021).…”

mentioning

confidence: 99%

Data-driven Global Subseasonal Forecast Model (GSFM v1.0) for intraseasonal oscillation components

Chen

Huang

Shen

et al. 2022

Preprint

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Abstract. As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and recent deep learning models for subseasonal forecasts, as forecast times increase, forecast results tend to become intraseasonal low-frequency components, which are essential to the change in general circulation on the subseasonal timescale as well as persistent extreme weather. In this paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the intraseasonal oscillation (ISO) components of atmospheric signals and used an improved deep learning model (SE-ResNet) to train and predict the ISO components of geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results show that the 10–30 day prediction performance of the model used in this paper is better than that of the model trained directly with original data. Compared with other models/methods, the SE-ResNet model has a good ability to depict the subseasonal evolution of the ISO components of Z500 and T850. In particular, although the CFSv2 results have a better prediction performance through 10 days, the SE-ResNet model is substantially superior to CFSv2 through 10–30 day, especially in the middle and high latitudes. The SE-ResNet model also has a better effect in predicting 3–8 planetary waves, which leads to the difference in model prediction performance in extratropical areas. A case study shows that the SE-ResNet model depicted the phase change and propagation characteristics of planetary waves well. Thus, the application of data- driven subseasonal forecasts of atmospheric ISO components may shed light on improving the skill of seasonal forecasts.

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Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

Cited by 5 publications

References 26 publications

Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China

Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China

Data-Driven Global Subseasonal Forecast for Intraseasonal Oscillation Components

Data-driven Global Subseasonal Forecast Model (GSFM v1.0) for intraseasonal oscillation components

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