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Kok-Seng Yap


This study illustrates the capability of Newtonian nudging and physical initialization in improving the initialized state and forecasts in the Florida State University high-resolution regional tropical mesoscale model. In particular it is shown that this form of initialization leads to major improvement in the precipitation forecasts. The precipitation spinup, precipitation phase shift, and overactive convection over the tropical region that are inherent characteristics of prediction models initialized through analyses produced by an intermittent data assimilation scheme are minimized by the initialization.

It is shown that the coupling of Newtonian nudging of the nondivergent component of the wind and surface pressure with physical initialization of cumulus parameterization, surface latent heat flux over the rain areas, and outgoing longwave radiation enables the model to build the required temperature, moisture, and velocity divergence distribution to produce the desired precipitation. Of these, the initialization of cumulus parameterization and surface latent heat flux show the most impact. The cumulus initialization as illustrated was done through a reanalysis of the humidity fields over the cloud depth via a “reverse Kuo” algorithm. The algorithm constrained the Kuo cumulus parameterization scheme of the model to precipitate according to prescribed rates. The initialization of surface latent heat flux was done through a reanalysis of the humidity field at the lowest level of the model through a “reverse similarity” algorithm that constrained the surface latent heat flux of the model to be similar to prescribed values.

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Liew Juneng, Fredolin T. Tangang, Hongwen Kang, Woo-Jin Lee, and Yap Kok Seng


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.

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