Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

Abstract

This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

Effects of Domain Selection on Singular-Value-Decomposition Based Statistical Downscaling of Monthly Rainfall Accumulation in Southern Taiwan

“…Various statistical downscaling schemes have been developed for constructing the relationship between the predictor and the predictand, such as multiple regression models (Wilby et al 1999), artificial neural networks (ANNs) (Harpham and Wilby 2005), canonical correlation analysis (CCA) (Juneng et al 2010), support vector machine (Yu et al 2006), and the singular-value-decomposition (SVD) based statistical downscaling method (Chu et al 2008). The SVD-based statistical downscaling method was used successfully in Taiwan (Chu et al 2008;Chu and Yu 2010) and the East Asia (Paul et al 2008), which was adopted in this work for downscaling monthly rainfall accumulations.…”

Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece

“…According to the literature several attempts have been carried out in order to forecast precipitation using statistical methods. Juneng et al (2010) compared the skills of four different forecasting approaches in predicting the one-month lead time of the Malaysian winter season precipitation. The results showed that the appropriate downscaling technique and ensemble of various regional climate models (RCM) forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.…”

Climate Projections of Future Extreme Events in Malaysia

“…Hazards caused by extreme rainfall often results in extensive evacuation and loss of lives not to mention the destruction of public infrastructure, crop yield damage and economic losses (Juneng et al, 2010). A study on historical data between the years 1975 and 2010 by Syafrina et al (2015) shows increasing trends of extreme rainfall in Peninsular Malaysia.…”