The degree to which a forecast changes from one issue time to the next is an interesting aspect of a forecast system. Weather forecasters report that they are reluctant to change a forecast if they judge there is a risk of it being changed back again. They believe such instability detracts from the message being delivered and are reluctant to use automated guidance which they perceive as having lack of stability. A Flip‐Flop Index was developed to quantify this characteristic of revisions of fixed‐event forecasts. The index retains physically meaningful units, has a simple definition and does not penalize a sequence of forecasts that show a trend, which is important when assessing forecasts where a trend can be interpreted as a forecast becoming more confident with a shorter lead time. The Flip‐Flop Index was used to compare the stability of sequences of automated guidance with the official Australian Bureau of Meteorology forecasts, which are prepared manually. The results show that the forecasts for chance of rain from the automated guidance are often more stable than the official, manual forecasts. However, the official forecasts for maximum temperature are more stable than those based on automated guidance. The Flip‐Flop Index is independent of observations and does not measure skill, but it can play a complementary role in characterizing and evaluating a forecasting system.

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Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parametrisation scheme emulation and replacement, and recently even to full ML-based weather and climate prediction models. While ML has been used in this space for more than 25 years, it is only in the last 10 or so years that progress has accelerated to the point that ML applications are becoming competitive with numerical knowledge-based alternatives. In this review, we provide a roughly chronological summary of the application of ML to aspects of weather and climate modelling from early publications through to the latest progress at the time of writing. We also provide an overview of key ML concepts and terms. Our aim is to provide a primer for researchers and model developers to rapidly familiarize and update themselves with the world of ML in the context of weather and climate models.