Machine Learning Based Statistical Downscaling for Rainfall on Hawaiian Islands

Abstract

Long-term rainfall prediction on Hawaiian islands in the scale of up to decades is a crucial task for water resource management. The current physics based climate models only produce coarse outputs, which are not suitable to the islands due to high rainfall gradient. Statistical downscaling is a method of learning a model to perform super-resolution on weather and climate variables; predicting local weather and climate from coarse resolution variables. This project focuses on rainfall data, and aims at building a framework for statistical downscaling using historical reanalysis data in coarse resolution. Statistical downscaling is typically done using linear regression models. Here we test the use of machine learning methods such as decision trees and neural networks, which are underutilized for this application. Given a set of coarse inputs, non-linear machine learning models are trained to make rainfall predictions. In this study, we compare machine learning methods for statistical downscaling on a large historical dataset for Hawaiʻi's rainfall. In Chapter 2, the dataset used for this project is explained. In Chapter 3, explanations on each method are provided. Chapter 4 iterates the result on feature selection and experiment on site-specific models. It also has a followup on the site-specific experiment, where the effect of sample size on machine learning methods is examined. Our results show that neural networks are able to improve upon linear regression prediction. However, while this is true in aggregate, there are some cases where linear regression is superior to neural networks, typically when there is not much data. Overall, this project provides a demonstration of the capabilities and limitations of non-linear machine learning methods, establishing the initial milestone on improvement on statistical downscaling research to follow.