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Keyword: equitable threat score ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Skill assessment of AI-based weather model in heavy precipitation prediction over South Korea

   Hong, Park, Kim, Baik and Jin · Theoretical and Applied Climatology · 2026

   The study compared the AI weather model GraphCast with WRF simulations at three resolutions against rain gauge data for 10 heavy precipitation cases in South Korea.

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   1. UncheckedIn 10 South Korean heavy-rain cases, GraphCast overpredicted light rain (≤20 mm) and underpredicted heavy rain (≥70 mm) more than WRF at 27, 9 and 3 km.“When the skill for different precipitation thresholds is separately examined using the equitable threat score (ETS), neighborhood ETS, and other contingency table-based metrics, GraphCast shows relatively large overprediction of light precipitation (≤ 20 mm) and underprediction of heavy precipitati…”
   2. UncheckedGraphCast reproduced 42% of the observed mountain-driven precipitation enhancement over the Sobaek Mountains, versus 63–90% for WRF simulations.“GraphCast reproduces observed topographic enhancements of precipitation over the Sobaek Mountains by 42%, which is less than those reproduced by WRF 27km (71%), WRF 9km (63%), and WRF 3km (90%).”
   3. UncheckedGraphCast's underprediction of heavy and mountain-enhanced rain is mainly linked to weak prediction of large rainfall peaks about 25–100 km across.“The underpredictions of heavy precipitation and topographically enhanced precipitation by GraphCast are mainly associated with its weak ability to predict large precipitation peaks with scales of about 25–100 km.”