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

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 Syste...

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Bibliographic Details
Published in:Advances in Meteorology 2014-01, Vol.2014 (2014), p.ac1-13
Main Authors: Phan Van, Tan, Van Nguyen, Hiep, Trinh Tuan, Long, Nguyen Quang, Trung, Ngo-Duc, Thanh, Laux, Patrick, Nguyen Xuan, Thanh
Format: Article
Language:English
Subjects:
Atmospheric temperature
Bias
Climate
Climate change
Climate models
Climatology
Cold
Dynamical systems
Lead time
Mathematical models
Meteorology
Precipitation
Skills
Studies
Vietnam
Weather forecasting
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Summary: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.
ISSN:1687-9309
1687-9317
DOI:10.1155/2014/245104