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Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method.

Rongnian Tang1, Yuke Ning1, Chuang Li1

  • 1Electrical and Mechanical College, Hainan University, Haikou 570228, China.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary

This study introduces a spatial Light Gradient Boosting Machine (LightGBM) model to enhance numerical weather prediction (NWP) accuracy. The model improves medium-range forecasts by incorporating local spatial data, outperforming existing methods.

Keywords:
LightGBMforecastspatial featuretemperatureweather correctionwind

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Area of Science:

  • Meteorology
  • Data Science
  • Machine Learning

Background:

  • Numerical Weather Prediction (NWP) is crucial for socioeconomic development but often lacks sufficient local spatial information.
  • Current NWP models solve differential equations using global data, neglecting station-specific details.

Purpose of the Study:

  • To improve the accuracy and local relevance of medium-range weather forecasts.
  • To develop a novel model for correcting NWP results at individual observation stations.

Main Methods:

  • A spatial Light Gradient Boosting Machine (LightGBM) model was developed.
  • The model employs a single-station, single-time strategy to integrate observed and model data.
  • It focuses on capturing local spatial information for forecast correction.

Main Results:

  • The spatial LightGBM model effectively corrected medium-range weather predictions for temperature and wind in Hainan Province.
  • The proposed correction method demonstrated superior performance compared to the ECMWF model.
  • It outperformed other competing weather forecasting methods in experimental evaluations.

Conclusions:

  • The novel spatial LightGBM model significantly enhances NWP accuracy by leveraging local spatial data.
  • This approach offers a promising method for improving the reliability of medium-range weather forecasts.
  • The model's ability to incorporate station-specific information makes it valuable for practical applications.