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Informer-Based Temperature Prediction Using Observed and Numerical Weather Prediction Data.

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  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.

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Summary

This study introduces an Informer-based model for accurate temperature prediction, outperforming previous methods by integrating time-periodic data and addressing long-term dependencies for improved weather forecasting.

Keywords:
informer-based modellocal data assimilation and prediction system (LDAPS)model fusionnumerical weather prediction (NWP)temperature prediction

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Area of Science:

  • Meteorology and Atmospheric Sciences
  • Artificial Intelligence and Machine Learning
  • Time Series Analysis

Background:

  • Deep learning models like CNN-BLSTM show promise in temperature prediction but struggle with time data integration and long-term dependencies.
  • Existing models exhibit performance degradation with extended prediction time lengths, limiting their practical application.
  • The need for advanced models that can effectively handle temporal dynamics and diverse data sources in weather forecasting is critical.

Purpose of the Study:

  • To propose a novel Informer-based temperature prediction model that overcomes limitations of existing deep learning approaches.
  • To investigate the impact of incorporating time-periodic information and fusing data from Automatic Weather Stations (AWS) and Local Data Assimilation and Prediction Systems (LDAPS).
  • To evaluate the model's performance in mitigating long-term dependency issues and improving prediction accuracy over extended timeframes.

Main Methods:

  • Developed an Informer-based deep learning architecture, a variant of the Transformer, specifically designed for time series data.
  • Integrated time-periodic information into the model's input to enhance learning of temporal patterns.
  • Implemented fusion operations combining AWS and LDAPS data to assess their individual and combined effects on prediction accuracy.
  • Utilized Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE) for quantitative performance evaluation across various prediction horizons (6-336 hours).

Main Results:

  • The proposed Informer-based model demonstrated superior performance compared to the CNN-BLSTM model in temperature prediction.
  • The model achieved a relative reduction in average RMSE by 0.25 °C and MAE by 0.203 °C.
  • Incorporating time-periodic information and fusing AWS/LDAPS data contributed to enhanced prediction accuracy.
  • The model effectively addressed the long-term dependency problem, maintaining performance over longer prediction intervals.

Conclusions:

  • The Informer-based temperature prediction model offers a significant advancement over conventional deep learning methods, particularly for long-term forecasting.
  • Effective integration of time-periodic data and multi-source weather information is crucial for improving prediction accuracy.
  • The model's ability to mitigate long-term dependencies makes it a promising tool for operational weather forecasting applications.