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A Deep Convolutional Neural Network Model for Improving WRF Simulations.

Alqamah Sayeed, Yunsoo Choi, Jia Jung

    IEEE Transactions on Neural Networks and Learning Systems
    |August 10, 2021
    PubMed
    Summary

    Deep learning, specifically convolutional neural networks (CNNs), effectively corrects biases in numerical weather prediction (NWP) models. This approach significantly improves the accuracy of weather simulations for various meteorological parameters.

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    Area of Science:

    • Atmospheric Sciences
    • Meteorology
    • Artificial Intelligence in Weather Forecasting

    Background:

    • Numerical weather prediction (NWP) models advance weather understanding but suffer from inherent biases due to physical process parameterization and equation discretization, reducing simulation accuracy.
    • These biases necessitate postprocessing techniques to enhance the reliability of NWP model outputs for critical applications.

    Purpose of the Study:

    • To investigate the efficacy of a computationally efficient deep learning (DL) method, the convolutional neural network (CNN), as a postprocessing technique for mesoscale NWP models.
    • To bias-correct key meteorological parameters from the Weather Research and Forecasting (WRF) model using a CNN architecture trained on historical data.

    Main Methods:

    • Employed a CNN architecture for postprocessing one-day WRF simulations (1-h temporal resolution) covering South Korea at a 27 km spatial resolution.

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  • Trained the CNN model using four years (2014-2017) of WRF data to identify and learn patterns in model biases.
  • Validated the bias-corrected outputs against ground observations from 93 Korean Meteorological Administration stations for surface wind, precipitation, humidity, pressure, dewpoint, and temperature.

    • Main Results:

    • The CNN postprocessing demonstrated noticeable improvements across all meteorological parameters and station locations.
    • Significant enhancements were observed in the index of agreement for surface wind (0.85 vs. 0.67), precipitation (0.62 vs. 0.56), and surface pressure (0.91 vs. 0.69).
    • Near-perfect agreement was achieved for temperature (0.99), dewpoint temperature (0.98), and relative humidity (0.92) post-correction.

    Conclusions:

    • The study validates the effectiveness of CNNs as an efficient deep learning approach for bias correction in mesoscale NWP models.
    • The proposed method offers a robust strategy to improve the accuracy of weather simulations, with potential applicability to any location and weather parameter.