Statistical post-processing of multiple meteorological elements using the multimodel integration embedded method

Ma, Xingxing; Liu, Hongnian; Dong, Qiushi; Chen, Qizhi; Cai, Ninghao

doi:10.1016/j.atmosres.2024.107269

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…Then, the XGBoost, RF and LSTM models are selected and trained by the five-fold cross-validation method in the base model layer. Actually, referred to previous studies (Ma et al, 2024), 5-fold cross validation (CV) is also used to adjust the model and to avoid the over-fitting problem in this study. The five sets of predictions are vertically stacked and concatenated to form a new feature dataset (Figure 7).…”

Section: Construction Of the Icing Prediction Modelmentioning

confidence: 99%

Prediction model for transmission line icing based on data assimilation and model integration

Wang,

Shen,

Jin

et al. 2024

Front. Environ. Sci.

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With the increase of extreme weather events such as cold waves, power transmission line icing has become more and more severe, affecting the safe and stable operation of power systems. Thus, icing prediction has become crucial for power grids. In this study, we collect multi-source data including the historical observations of meteorological elements and transmission line icing in Sichuan during 2017–2019, and develop an artificial intelligence (artificial intelligence)-based integrated model to achieve icing thickness prediction according to meteorological elements. Using the Weather Research and Forecasting model and the three-dimensional variational data assimilation method, we analyze the weather conditions in Sichuan of China during the 2020 winter, and obtain the high-precision meteorological element fields that are related to icing prediction. The forecasted meteorological elements are then combined with the AI-based integrated model to predict icing conditions, assisting in the warning of transmission line icing. The results indicate that the AI-based integrated model displays superior performance on the accurate prediction of icing thickness in the test set, with only two samples having prediction errors of more than 3 mm. Data assimilation can effectively improve the forecast accuracy of meteorological elements near icing observation stations and thus enhance the accuracy of icing thickness prediction. In particular, icing thickness prediction is remarkably improved at Gaoqiao, Laolinkou and Erlangshan stations.

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Section: Construction Of the Icing Prediction Modelmentioning

confidence: 99%