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Hong J.-S.,Central Weather Bureau | Fong C.-T.,Central Weather Bureau | Hsiao L.-F.,Taiwan Typhoon Flood Research Institute | Yu Y.-C.,National Science and Technology Center for Disaster Reduction | Tzeng C.-Y.,Central Weather Bureau
Weather and Forecasting | Year: 2015

In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pickout cases, which are screened using certain criterion based on given typhoon tracks from an ensemble prediction system (EPS). Therefore, the ETQPF model resembles a climatology model. However, the ETQPF model uses the quantitative precipitation forecasts (QPFs) from an EPS instead of historical rainfall observations. Two typhoon cases, Fanapi (2010) and Megi (2010), are used to evaluate the ETQPF model performance. The results show that the rainfall forecast from the ETQPF model, which is qualitatively compared and quantitatively verified, provides reasonable typhoon rainfall forecasts and is valuable for real-time operational applications. By applying the forecast track to the ETQPF model, better track forecasts lead to better ETQPF rainfall forecasts. Moreover, the ETQPF model provides the "scenario" of the typhoon QPFs according to the uncertainty of the forecast tracks. Such a scenario analysis can provide valuable information for risk assessment and decision making in disaster prevention and reduction. Deficiencies of the ETQPF model are also presented, including that the average over the pick-out case usually offsets the extremes and reduces the maximum ETQPF rainfall, the underprediction is especially noticeable for weak phase-locked rainfall systems, and the ETQPF rainfall error is related to the model bias. Therefore, reducing model bias is an important issue in further improving the ETQPF model performance. © 2015 American Meteorological Society. Source

Hsiao L.-F.,Taiwan Typhoon Flood Research Institute | Huang X.-Y.,U.S. National Center for Atmospheric Research | Huang X.-Y.,Center for Climate Research Singapore | Kuo Y.-H.,U.S. National Center for Atmospheric Research | And 9 more authors.
Weather and Forecasting | Year: 2015

A blending method to merge the NCEP global analysis with the regional analysis from the WRF variational data assimilation system is implemented using a spatial filter for the purpose of initializing the Typhoon WRF (TWRF) Model, which has been in operation at Taiwan's Central Weather Bureau (CWB) since 2010. The blended analysis is weighted toward the NCEP global analysis for scales greater than the cutoff length of 1200 km, and is weighted toward the WRF regional analysis for length below that. TWRF forecast experiments on 19 typhoons from July to October 2013 over the western North Pacific Ocean show that the large-scale analysis from NCEP GFS is superior to that of the regional analysis, which significantly improves the typhoon track forecasts. On the other hand, the regional WRF analysis provides a well-developed typhoon structure and more accurately captures the influence of the Taiwan topography on the typhoon circulation. As a result, the blended analysis takes advantage of the large-scale analysis from the NCEP global analysis and the detailed mesoscale analysis from the regional WRF analysis. In additional to the improved track forecast, the blended analysis also provides more accurate rainfall forecasts for typhoons affecting Taiwan. Because of the improved performance, the blending method has been implemented in the CWB operational TWRF typhoon prediction system. © 2015 American Meteorological Society. Source

Hsiao L.-F.,Taiwan Typhoon Flood Research Institute | Yang M.-J.,Taiwan Typhoon Flood Research Institute | Yang M.-J.,National Central University | Lee C.-S.,Taiwan Typhoon Flood Research Institute | And 18 more authors.
Journal of Hydrology | Year: 2013

In this study, an ensemble meteorological modeling system is one-way coupled with a hydrological model to predict typhoon rainfall and flood responses in a mountainous watershed in Taiwan. This ensemble meteorological model framework includes perturbations of the initial conditions, data analysis methods, and physical parameterizations. The predicted rainfall from the ensemble meteorological modeling system is then used to drive a physically distributed hydrological model for flood responses in the Lanyang basin during the landfall of Typhoon Nanmadol (2011). The ensemble forecast provides track forecasts that are comparable to the operational center track forecasts and provides a more accurate rainfall forecast than a single deterministic prediction. The runoff forecast, which is driven by the ensemble rainfall prediction, can provide uncertainties for the runoff forecasts during typhoon landfall. Thus, the ensemble prediction system provides useful probability information for rainfall and runoff forecasting. © 2013 The Authors. Source

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