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Improving seasonal forecasts of air temperature using a genetic algorithm.

J V Ratnam1, H A Dijkstra2, Takeshi Doi3

  • 1Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan. jvratnam@jamstec.go.jp.

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Summary

This study used a genetic algorithm (GA) to optimize seasonal air temperature forecasts from a climate model. The GA-weighted forecasts significantly improved accuracy over unweighted ensemble means in several global regions.

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Area of Science:

  • Climate Science
  • Atmospheric Science
  • Machine Learning Applications

Background:

  • Accurate seasonal air temperature forecasts are crucial for societal planning but are challenged by atmospheric internal variability.
  • Ensemble forecasting methods are used to capture prediction signals, but managing large prediction spreads requires techniques to mitigate unrealistic members.
  • Weighted averaging of ensemble members is a key technique for refining seasonal forecast accuracy.

Purpose of the Study:

  • To apply a machine learning technique, specifically a genetic algorithm (GA), to optimize ensemble members for seasonal air temperature forecasts.
  • To derive optimal weights for 24 ensemble members of the SINTEX-F2 coupled general circulation model for boreal summer forecasts.
  • To evaluate the effectiveness of the GA-derived weights in improving the accuracy and spatial representation of 2m-air temperature anomalies.

Main Methods:

  • Utilized a genetic algorithm (GA) to determine optimal weights for ensemble members of the SINTEX-F2 climate model.
  • Applied the GA to the Scale Interaction Experiment-Frontier research center for global change version 2 (SINTEX-F2) model's boreal summer forecasts.
  • Compared the performance of GA-weighted ensemble means against unweighted ensemble means for predicting 2m-air temperature anomalies.

Main Results:

  • The GA technique significantly improved the prediction of 2m-air temperature anomalies over South America, North America, Australia, and Russia.
  • The spatial distribution of predicted air temperature anomalies showed better representation using the GA-derived weights.
  • Improvements were particularly noted in mid- and high-latitude regions where model skill is typically modest.

Conclusions:

  • Machine learning techniques, such as genetic algorithms, can effectively enhance the accuracy of seasonal air temperature forecasts.
  • Optimizing ensemble member weights using GA leads to improved regional climate predictions.
  • The GA approach offers a promising method for refining climate model outputs, especially in regions with moderate model skill.