Developing Extreme Weather Event training datasets to accelerate Machine Learning Applications

Climate change is increasing the frequency and intensity of Extreme Weather Events (EWEs), which causes widespread disruption globally. As these events intensify, the need for better hazard identification becomes critical. While machine learning (ML) is already enhancing forecasts, and has huge potential for identifying future hazards. To unlock this potential, we need comprehensive training datasets of historic EWEs that integrate and harmonize diverse datasets, account for data collection discrepancies, and address gaps in temporal and spatial records.

This presentation initially discusses the development of an Aotearoa New Zealand EWE database from 1996 to 2021, which currently includes occurrence data derived from subjective classifications from the national weather service, research organizations, and insurance information. Careful analysis of that database and ancillary reanalyses output can successfully characterise rainfall extreme intensities by deriving duration, peak rainfall, and total accumulation.

Building on that work, this presentation will discuss the development and testing of a methodology to integrate extreme weather event (EWE) occurrence, intensity, and storm track data into a unified database. By processing this combined dataset, we aim to harmonise data from the disparate sources and improve data accuracy and reliability, making it robust for future ML analyses. We also use our experience of applying ML classification schemes in climate research to provide proof-of-concept applications demonstrating the value of our harmonisation methodology.