Skilful sub-seasonal forecasts of aggregated temperature over Europe

METEOROLOGICAL APPLICATIONS(2023)

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摘要
Subseasonal-to-seasonal (S2S) forecasts span the prediction range of weeks to 2-3 months ahead, bridging the gap between medium-range and seasonal weather forecasts. There has been growing interest in S2S forecasts in recent years, largely because of the many potential uses of forecasts spanning these timescales. However, the skill of S2S forecasts beyond the first 2 weeks or so is poor, potentially limiting the usability of these forecasts. We show in this study that when considering accumulated temperatures, there is in fact good forecasting skill over Europe for accumulation periods up to 30 days ahead. Using a set of S2S hindcasts, we show using both a deterministic and a probabilistic measure of skill that the accumulated 2-metre temperature forecasts out to 30 days are skilful over most of Europe. In summer, South West Europe has highest skill, while in winter North East Europe has highest skill. As an example application of such forecasts, we also evaluate the skill for summer cooling degree-days (CDD) and winter heating degree-days (HDD). For 30-day winter HDD, there is good skill in all four European regions; for 30-day summer CDD, the skill is limited in North West Europe, but still good in other regions. Sub-seasonal forecasts of aggregated 2-metre temperature over periods of up to 30 days ahead have good skill over Europe. This skill is also present in forecasts of heating and cooling degree-days, which are more complex metrics than simply temperature. There are many applications that use aggregated temperatures, including energy forecasting and crop growth forecasts.image
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关键词
ensemble forecasts,Europe,evaluation,forecasting,medium range,modelling,probabilistic forecasts,seasonal,sub-seasonal forecasting,temperature,verification
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