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- Author or Editor: Ke Fan x
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Abstract
This paper presents a year-by-year incremental approach to forecasting the Atlantic named storm frequency (ATSF) for the hurricane season (1 June–30 November). The year-by-year increase or decrease in the ATSF is first forecasted to yield a net ATSF prediction. Six key predictors for the year-by-year increment in the number of Atlantic named tropical storms have been identified that are available before 1 May. The forecast model for the year-by-year increment of the ATSF is first established using a multilinear regression method based on data taken from the years 1965–99, and the forecast model of the ATSF is then derived. The prediction model for the ATSF shows good prediction skill. Compared to the climatological average mean absolute error (MAE) of 4.1, the percentage improvement in the MAE is 29% for the hindcast period of 2004–09 and 46% for the cross-validation test from 1985 to 2009 (26 yr). This work demonstrates that the year-by-year incremental approach has the potential to improve the operational forecasting skill for the ATSF.
Abstract
This paper presents a year-by-year incremental approach to forecasting the Atlantic named storm frequency (ATSF) for the hurricane season (1 June–30 November). The year-by-year increase or decrease in the ATSF is first forecasted to yield a net ATSF prediction. Six key predictors for the year-by-year increment in the number of Atlantic named tropical storms have been identified that are available before 1 May. The forecast model for the year-by-year increment of the ATSF is first established using a multilinear regression method based on data taken from the years 1965–99, and the forecast model of the ATSF is then derived. The prediction model for the ATSF shows good prediction skill. Compared to the climatological average mean absolute error (MAE) of 4.1, the percentage improvement in the MAE is 29% for the hindcast period of 2004–09 and 46% for the cross-validation test from 1985 to 2009 (26 yr). This work demonstrates that the year-by-year incremental approach has the potential to improve the operational forecasting skill for the ATSF.
Abstract
This paper presents a new approach for forecasting the typhoon frequency of the western North Pacific (WNP). The year-to-year increase or decrease in typhoon frequency is first forecasted to yield a net typhoon frequency prediction. Five key predictors for the year-to-year increment in the number of typhoons in the WNP have been identified, and a forecast model is established using a multilinear regression method based on data taken from 1965 to 2001. Using the forecast model, a hindcast of the typhoon frequency of the WNP during 2002–07 is made. The model exhibited a reasonably close fit for the period 1965–2007, including the larger anomalies in 1997 and 1998. It also accounted for the smaller variability of the typhoon frequency of the WNP during the validation period 2002–07 with an average root-mean-square error (RMSE) of 1.3 (2.85) during 2002–07 (1965–2001). The cross-validation test of the prediction model shows that the new approach and the prediction model demonstrate better prediction skill when compared to the models established based on typhoon frequency rather than the typhoon frequency increment. Thus, this new approach has the potential to improve the operational forecasting skill for typhoon frequency in the WNP.
Abstract
This paper presents a new approach for forecasting the typhoon frequency of the western North Pacific (WNP). The year-to-year increase or decrease in typhoon frequency is first forecasted to yield a net typhoon frequency prediction. Five key predictors for the year-to-year increment in the number of typhoons in the WNP have been identified, and a forecast model is established using a multilinear regression method based on data taken from 1965 to 2001. Using the forecast model, a hindcast of the typhoon frequency of the WNP during 2002–07 is made. The model exhibited a reasonably close fit for the period 1965–2007, including the larger anomalies in 1997 and 1998. It also accounted for the smaller variability of the typhoon frequency of the WNP during the validation period 2002–07 with an average root-mean-square error (RMSE) of 1.3 (2.85) during 2002–07 (1965–2001). The cross-validation test of the prediction model shows that the new approach and the prediction model demonstrate better prediction skill when compared to the models established based on typhoon frequency rather than the typhoon frequency increment. Thus, this new approach has the potential to improve the operational forecasting skill for typhoon frequency in the WNP.
Abstract
A new statistical forecast scheme, referred to as scheme 1, is developed using observed autumn Atlantic sea surface temperature (SST) and Eurasian snow cover in the preceding autumn to predict the upcoming winter North Atlantic Oscillation (NAO) using the year-to-year increment prediction approach (i.e., DY approach). Two predictors for the year-to-year increment are identified that are available in the preceding autumn. Cross-validation tests for the period 1950–2011 and independent hindcasts for the period 1990–2011 are performed to validate the prediction ability of the proposed technique. The cross-validation test results for 1950–2011 reveal a high correlation coefficient of 0.52 (0.58) between the predicted and observed NAO indices (DY of the NAO). The model also successfully predicts the independent hindcasts for the period 1990–2011 with a correlation coefficient of 0.55 (0.74). In addition, scheme 0 (i.e., anomaly approach) is established using the SST and snow cover anomalies during the preceding autumn. Compared with scheme 0, this new prediction model has higher predictive skill in reproducing the interdecadal variability of NAO. Therefore, this study provides an effective climate prediction scheme for the interannual and interdecadal variability of NAO in boreal winter.
Abstract
A new statistical forecast scheme, referred to as scheme 1, is developed using observed autumn Atlantic sea surface temperature (SST) and Eurasian snow cover in the preceding autumn to predict the upcoming winter North Atlantic Oscillation (NAO) using the year-to-year increment prediction approach (i.e., DY approach). Two predictors for the year-to-year increment are identified that are available in the preceding autumn. Cross-validation tests for the period 1950–2011 and independent hindcasts for the period 1990–2011 are performed to validate the prediction ability of the proposed technique. The cross-validation test results for 1950–2011 reveal a high correlation coefficient of 0.52 (0.58) between the predicted and observed NAO indices (DY of the NAO). The model also successfully predicts the independent hindcasts for the period 1990–2011 with a correlation coefficient of 0.55 (0.74). In addition, scheme 0 (i.e., anomaly approach) is established using the SST and snow cover anomalies during the preceding autumn. Compared with scheme 0, this new prediction model has higher predictive skill in reproducing the interdecadal variability of NAO. Therefore, this study provides an effective climate prediction scheme for the interannual and interdecadal variability of NAO in boreal winter.
Abstract
A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.
Abstract
A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.
Abstract
East Asian summer monsoon (EASM) prediction is difficult because of the summer monsoon’s weak and unstable linkage with El Niño–Southern Oscillation (ENSO) interdecadal variability and its complicated association with high-latitude processes. Two statistical prediction schemes were developed to include the interannual increment approach to improve the seasonal prediction of the EASM’s strength. The schemes were applied to three models [i.e., the Centre National de Recherches Météorologiques (CNRM), the Met Office (UKMO), and the European Centre for Medium-Range Weather Forecasts (ECMWF)] and the Multimodel Ensemble (MME) from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) results for 1961–2001. The inability of the three dynamical models to reproduce the weakened East Asian monsoon at the end of the 1970s leads to low prediction ability for the interannual variability of the EASM. Therefore, the interannual increment prediction approach was applied to overcome this issue. Scheme I contained the EASM in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme II contained two predictors: both the EASM and also the western North Pacific circulation in the form of year-to-year increments. Both the cross-validation test and the independent hindcast experiments showed that the two prediction schemes have a much better prediction ability for the EASM than does the original scheme. This study provides an efficient approach for predicting the EASM.
Abstract
East Asian summer monsoon (EASM) prediction is difficult because of the summer monsoon’s weak and unstable linkage with El Niño–Southern Oscillation (ENSO) interdecadal variability and its complicated association with high-latitude processes. Two statistical prediction schemes were developed to include the interannual increment approach to improve the seasonal prediction of the EASM’s strength. The schemes were applied to three models [i.e., the Centre National de Recherches Météorologiques (CNRM), the Met Office (UKMO), and the European Centre for Medium-Range Weather Forecasts (ECMWF)] and the Multimodel Ensemble (MME) from the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) results for 1961–2001. The inability of the three dynamical models to reproduce the weakened East Asian monsoon at the end of the 1970s leads to low prediction ability for the interannual variability of the EASM. Therefore, the interannual increment prediction approach was applied to overcome this issue. Scheme I contained the EASM in the form of year-to-year increments as a predictor that is derived from the direct outputs of the models. Scheme II contained two predictors: both the EASM and also the western North Pacific circulation in the form of year-to-year increments. Both the cross-validation test and the independent hindcast experiments showed that the two prediction schemes have a much better prediction ability for the EASM than does the original scheme. This study provides an efficient approach for predicting the EASM.
