Subseasonal prediction of wintertime northern hemisphere extratropical cyclone activity by subX and S2S models

Cheng Zheng, Edmund Kar Man Chang, Hyemi Kim, Minghua Zhang, Wanqiu Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The prediction of wintertime extratropical cyclone activity (ECA) on subseasonal time scales by models participating in the Subseasonal Experiment (SubX) and the Seasonal to Subseasonal Prediction (S2S) is assessed. Consistent with a previous study that investigated the S2S models, the SubX models have skillful predictions of ECA over regions from central North Pacific across North America to western North Atlantic, as well as East Asia and northern and southern part of easternNorth Atlantic at 3-4weeks lead time. SubX provides daily mean data,while S2S provides instantaneous data at 0000 UTC each day. This leads to different variance of ECA. Different S2S and SubX models have different reforecast initialization times and reforecast time periods. These factors can all lead to differences in prediction skill. To fairly compare the prediction skill between different models, we develop a novel way to evaluate the prediction of individual model across the two ensembles by comparing every model to the Climate Forecast System, version 2 (CFSv2), as CFSv2 has 6-hourly output and forecasts initialized every day. Among the S2S and SubX models, the European Centre forMedium-Range Weather Forecasts model exhibits the best prediction skill, followed by CFSv2. Our results also suggest that while the prediction skill is sensitive to forecast lead time, including forecasts up to 4 days old into the ensemble may still be useful for weeks 3-4 predictions of ECA.

Original languageEnglish
Pages (from-to)75-89
Number of pages15
JournalWeather and Forecasting
Volume36
Issue number1
DOIs
StatePublished - Feb 2021

Bibliographical note

Publisher Copyright:
© 2021 American Meteorological Society.

Keywords

  • Ensembles
  • Forecast verification/skill
  • Hindcasts
  • Intraseasonal variability

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