Improving forecasts of precipitation extremes over northern and central Italy using machine learning

The accurate prediction of intense precipitation events is one of the main
objectives of operational weather services. This task is even more relevant
nowadays, with the rapid progression of global warming which intensifies
these events. Numerical weather prediction models have improved continuously
over time, providing uncertainty estimation with dynamical ensembles. How-
ever, direct precipitation forecasting is still challenging. Greater availability of
machine-learning tools paves the way to a hybrid forecasting approach, with the
optimal combination of physical models, event statistics, and user-oriented post-
processing. Here we describe a specific chain, based on a random-forest (RF)
pipeline, specialised in recognising favourable synoptic conditions leading to
precipitation extremes and subsequently classifying extremes into predefined
types. The application focuses on northern and central Italy, taken as a testbed
region, but is seamlessly extensible to other regions and time-scales. The system
is called MaLCoX (Machine Learning model predicting Conditions for eXtreme
precipitation) and is running daily at the Italian regional weather service of
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ARPAE Emilia-Romagna. MalCoX has been trained with the ARCIS gridded
high-resolution precipitation dataset as the target truth, using the last 20 years
of the European Centre for Medium-Range Weather Forecasts (ECMWF) refore-
cast dataset as input predictors. We show that, with a long enough training
period, the optimal blend of larger-scale information with direct model output
improves the probabilistic forecast accuracy of extremes in the medium range. In
addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.