AbstractBecause summer rainfall in the middle-lower reaches of the Yangtze River Valley has remarkable inter-annual and decadal variability and because the precursors that modulate the inter-annual rainfall change with the decadal variation of background state, a new model that employs novel statistical idea is needed to yield accurate prediction. In this study, the inter-annual rainfall model (IAM) and the decadal rainfall model (DM) were constructed, respectively. Moving updating of the IAM with the latest data within an optimal length of training period (20 years) can partially offset the effect of decadal change of precursors in IAM. To predict the inter-annual rainfall of 2001–2013 for validation, thirteen regression models were fitted with precursors that change every 4–5 years, from the preceding winter North Atlantic sea surface temperature anomaly (SSTA) dipole to the Mascarene High, followed by the East Asia sea level pressure anomaly (SLPA) dipole and the preceding autumn North Pacific SSTA dipole. The moving updated model demonstrated high skill in predicting inter-annual rainfall with a correlation coefficient of 0.76 and a hit rate of 76.9%. The DM was linked to the April SLPA in the central tropical Pacific, and it maintained good performance in the testing period with a correlation coefficient of 0.77 and a root-mean-square error (RMSE) of 7.7%. Our statistical model exhibited superior capability even compared with the best CFSv2 forecast initiated in early June, as indicated by increased correlation coefficient from 0.62 to 0.75 and reduced RMSE from 12.3% to 10.7%.
Journal of Applied Meteorology and Climatology – American Meteorological Society
Published: Jun 16, 2017
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