Author(s): Sarah Kang

Quantifying extreme events from short weather forecast data

Source(s): Eos - AGU
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Subseasonal weather forecast ensembles are a useful tool for overcoming the inherent difficulty of quantifying extreme weather risk caused by data scarcity.

Assessing the risk of extreme weather events is crucial for our society, but it is incredibly challenging due to their sporadic nature. Although high-resolution weather models are critical for accurately evaluating extreme events, they are typically run only on synoptic timescales. Therefore, long-term climatological risk assessments rely on centennial-scale integrations of cheaper low-resolution or statistical models.

However, Finkel et al. [2023] propose new statistical methods that utilize subseasonal weather forecast ensembles to extract once-in-500-year events, even with only 20 years of 46-day weather forecasts. The method is specifically applied to sudden stratospheric warming (SSW) events, which provide a source of surface weather predictability on the subseasonal-to-seasonal timescale. As a potential next step, one could seek to understand the impact of global warming on extremes by utilizing a high-resolution model in ensemble forecast mode, initialized around a decade at the end of this century, as provided by a climate model.

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