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Subseasonal predictions of tropical cyclone occurrence and ACE in the S2S dataset
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2020
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Source: Weather and Forecasting, 35(3), 921-938.
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Journal Title:Weather and Forecasting
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Description:Probabilistic tropical cyclone (TC) occurrence, at lead times of week 1-4, in the Subseasonal to Seasonal (S2S) dataset are examined here. Forecasts are defined over 15 degrees in latitude x 20 degrees in longitude regions, and the prediction skill is measured using the Brier skill score with reference to climatological reference forecasts. Two types of reference forecasts are used: a seasonally constant one and a seasonally varying one, with the latter used for forecasts of anomalies from the seasonal climatology. Models from the European Centre for Medium-Range Weather Forecasts (ECMWF), Australian Bureau of Meteorology, and Meteo-France/Centre National de Recherche Meteorologiques have skill in predicting TC occurrence four weeks in advance. In contrast, only the ECMWF model is skillful in predicting the anomaly of TC occurrence beyond one week. Errors in genesis prediction largely limit models' skill in predicting TC occurrence. Three calibration techniques, removing the mean genesis and occurrence forecast biases, and a linear regression method, are explored here. The linear regression method performs the best and guarantees a higher skill score when applied to the in-sample dataset. However, when applied to the out-of-sample data, especially in areas where the TC sample size is small, it may reduce the models' prediction skill. Generally speaking, the S2S models are more skillful in predicting TC occurrence during favorable Madden-Julian oscillation phases. Last, we also report accumulated cyclone energy predictions skill using the ranked probability skill score.
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Source:Weather and Forecasting, 35(3), 921-938.
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Rights Information:CC BY
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Compliance:Submitted
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