Evaluation of Short-Term Prediction Skill of East Asian Summer Atmospheric Rivers

Abstract

Atmospheric rivers (ARs) are closely related to local precipitation which can be both beneficial and destructive. Although several studies have evaluated their predictability, there is a lack of studies on East Asian ARs. This study evaluates the prediction skill of East Asian ARs in the Korean Integrated Model (KIM) for 2020 similar to 2022 summer. The spatial distribution of AR frequency in KIM is qualitatively similar to the observation but overestimated. In particular, the model errors greatly increase along the boundary of the western North Pacific subtropical high as the forecast lead time increases. When the prediction skills are quantitatively verified by computing the Anomaly Correlation Coefficient and Mean Square Skill Score, the useful prediction skill of daily AR around the Korean Peninsula is found up to 5 days. Such prediction limit is primarily set by the wind field errors with a minor contribution of moisture distribution errors. This result suggests that the improved prediction of atmospheric circulation field can improve the prediction of East Asian summer ARs and the associated precipitation.