3A.6 On the development of seasonal tropical cyclone prediction schemes for the Fiji region

Monday, 10 May 2010: 2:30 PM
Arizona Ballroom 6 (JW MArriott Starr Pass Resort)
Savin S. Chand, University of Melbourne, Melbourne, VIC, Australia; and K. J. E. Walsh and J. Chan

Seasonal prediction schemes for tropical cyclones (TCs) affecting the Fiji, Samoa and Tonga regions (the FST region) are developed. Two separate Bayesian regression models are constructed: (i) for cyclones forming within the FST region (type “FORM”) and (ii) for cyclones entering the FST region (type “ENT”). Predictors examined include various El Niño–Southern Oscillation (ENSO) indices and large–scale environmental conditions.

Analysis of predictors suggest that TC activity in the FST region is strongly affected by the ENSO phenomenon. In El Niño years, relatively more cyclones are observed to form within the FST region as opposed to La Niña years. In contrast, the number of cyclones entering the FST region during La Niña years is greater than those entering the region during El Niño years. TCs forming within the FST region exhibit high correlation with several ENSO indices, particularly with Niño-4 and Niño-3.4, as far back as the May-July preseason. Correlations are also high with various large-scale environmental parameters. TCs associated with ENT are only weakly related to ENSO variability. As a result, various ENSO indices and large-scale environmental parameters examined here for the November-April season show only weak correlations with the annual number of TCs entering the FST region. Nevertheless, their correlations with May-July preseason ENSO indices (particularly with the Niño-4) and with some large-scale environmental conditions in the East China Sea are found to be statistically significant. Because most of these correlations are statistically significant as far back as the May-July preseason, the May-July predictors are used to make initial predictions and later an updated prediction is issued using the October-December early cyclone season predictors.

A suite of predictors identified in this study are evaluated through cross-validation of observations with hindcasts obtained by the Bayesian regression approach. Results suggest that the relative vorticity and Niño-4 combined model is optimal to predict the annual number of TCs associated with FORM as it has small root-mean-squared error (RMSE) associated with its hindcasts (RMSE = 1.63). Similarly, the all-parameter combined model, which include Niño-4 index and some large-scale environmental fields over the East China Sea, appears appropriate to predict the annual number of TCs entering the FST region (RMSE = 0.98).

While the all-parameter combined ENT model appears to have good skill over all years, the May-July prediction using FORM model has two limitations. First, it underestimates (overestimates) the formation for years where onset of El Niño (La Niña) events is after the May-July preseason or when the previous La Niña (El Niño) event continued through the May-July season during its decay phase. Second, its performance in neutral conditions is quite variable. Overall, no significant skill can be achieved for neutral conditions even after an October-December update. This is contrary to El Niño or La Niña events where model performance is improved substantially after an October-December early cyclone season update.

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