A Global Deep Learning Perspective on Australia-Wide Monthly Precipitation Prediction

Gaining a deep understanding of precipitation patterns is beneficial for enhancing Australia's adaptability to climate change. Driven by this motivation, we present a specific spatiotemporal deep learning model that well integrates matrix factorization and temporal convolutional networks, along with essential year-month covariates and key climatic drivers, to analyze and forecast monthly precipitation in Australia. We name this the spatiotemporal TCN-MF method. Our approach employs the precipitation profiler-observation fusion and estimation (PPrOFusE) method for data input, synthesizing monthly precipitation readings from the gauge measurement of the Bureau of Meteorology (BoM), the JAXA Global Satellite Mapping of Precipitation (GSMaP), and the NOAA Climate Prediction Center Morphing (CMORPH) technique. The input dataset spans from April 2000 to March 2021 and covers 1391 Australian grid locations. To evaluate the model's effectiveness, particularly in regions prone to severe flooding, we employ the empirical dynamic quantiles (EDQ) technique. This method ranks cumulative rainfall levels, enabling focused analysis on areas most affected by extreme weather events. Our assessment from April 2021 to March 2022 highlights the model's proficiency in identifying significant rainfall, especially in flood-impacted locations. Through the analysis across various climatic zones, the spatiotemporal TCN-MF model contributes to the field of continent-wide precipitation forecasting, providing valuable insights that may enhance climate change adaptability strategies in Australia.