Uncertainty Quantification for Climate Precipitation Prediction by Decision Tree

Numerical weather and climate prediction have been addressed by numerical methods. This approach has been under permanent development. In order to estimate the degree of confidence on a prediction, an ensemble prediction has been adopted. Recently, machine learning algorithms have been employed for many applications. Here, the con- fidence interval for the precipitation climate prediction is addressed by a decision tree algorithm, by using the Light Gradient Boosting Machine (LightGBM) framework. The best hyperparameters for the LightGBM models were determined by the Optuna hyperparameter optimization framework, which uses a Bayesian approach to calculate an optimal hyperparameter set. Numerical experiments were carried out over South America. LightGBM is a supervised machine-learning technique. A period from January-1980 up to December-2017 was em- ployed for the learning phase, and the years 2018 and 2019 were used for testing, showing very good results.