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Advances in Meteorology
Volume 2014 (2014), Article ID 245104, 13 pages
http://dx.doi.org/10.1155/2014/245104
Research Article

Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)

1Department of Meteorology, VNU Hanoi University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam
2Institute of Meteorology, Hydrology and Environment (IMHEN), 23/62 Nguyen Chi Thanh, Dong Da, Hanoi, Vietnam
3Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT, IMK-IFU), Kreuzeckbahnstrasse 19, 82467 Garmisch-Partenkirchen, Germany

Received 2 February 2014; Revised 4 April 2014; Accepted 16 April 2014; Published 18 June 2014

Academic Editor: Hann-Ming H. Juang

Copyright © 2014 Tan Phan Van et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. The RegCM4.2 forecast is run four times per month from the current month up to the next six months. A model ensemble prediction initialized from the current month is computed from the mean of the four runs within the month. The results showed that, without any bias correction (CTL), the RegCM4.2 forecast has very little or no skill in both tercile and value predictions. With bias correction (BAS), model predictions show improved skill. The experiment in which the results from the BAS experiment are further successively adjusted (SUC) with model bias at one-month lead time of the previous run showed further improvement compared to CTL and BAS. Skill scores of the tercile probability forecasts were found to exceed 0.3 for most of the target months.