Preprints
https://doi.org/10.5194/egusphere-2023-3111
https://doi.org/10.5194/egusphere-2023-3111
16 Jan 2024
 | 16 Jan 2024
Status: this preprint is open for discussion.

Probabilistic short-range forecasts of high precipitation events: optimal decision thresholds and predictability limits

François Bouttier and Hugo Marchal

Abstract. Translation of ensemble predictions into high precipitation warnings is assessed using user oriented metrics. Short range probabilistic forecasts are derived from an operational ensemble prediction system using neighbourhood post-processing and conversion into categorical predictions by decision threshold optimization. Forecast skill is modelled for two different types of users. We investigate the balance between false alarms and missed events and the implications of the scales at which forecast information is communicated.

Results show that ensemble predictions objectively outperform the corresponding deterministic control forecasts at low precipitation intensities when an optimal probability threshold is used. Thresholds estimated from a short forecast archive are robust with respect to forecast range and season and can be extrapolated towards extreme values to estimate severe weather guidance.

Numerical weather forecast value is found to be limited: the highest usable precipitation intensities have return periods of a few years only, with resolution limited to several tens of kilometres. Implied precipitation warnings fall short of common skill requirements for high impact weather, confirming the importance of human expertise, nowcasting information and the potential of machine learning approaches.

The verification methodology presented here provides a benchmark for high precipitation forecasts, based on metrics that are relatively easy to compute and explain to non-experts.

François Bouttier and Hugo Marchal

Status: open (until 21 Mar 2024)

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François Bouttier and Hugo Marchal
François Bouttier and Hugo Marchal

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Short summary
Weather prediction uncertainties can be described as sets of possible scenarios – a technique called 'ensemble prediction'. Our machine learning technique translates them into more easily interpretable scenarios for various users, balancing the detection of high precipitation with false alarms. Key parameters are precipitation intensity, space and time scales of interest. We show that the approach can be used to facilitate warnings of extreme precipitations.