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The strategies of the extrapolation adjusted by model prediction (ExAMP) blending scheme, which trusts the field pattern predicted by extrapolation and allows the field intensity to be adjusted by numerical weather prediction (NWP), for rainfall nowcasting are analyzed in this study. The McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) and the Weather Research and Forecasting (WRF) model serve as the extrapolation and NWP models, respectively. Seven 150-min rainfall nowcasting experiments with different strategies are carried out for 37 sampled periods from seven heavy rainfall events in Taiwan in 2019. The results of the overall statistics indicate that, for the extrapolation component, extrapolating the current rainfall rate estimated from the lowest dual-polarimetric radar observations is a superior strategy. The ExAMP scheme that blends the MAPLE and WRF forecasts can surpass both components in 150-min rainfall nowcasting, and an empirical limitation on the innovation of intensity during the blending procedure is found unnecessary in this study. Moreover, the spatial performance for two contrasting events reveals the ability of ExAMP in grasping the rainfall strengthening and weakening in different areas. The skill statistics separately at rainfall strengthening gauges and weakening gauges further prove the effectiveness of ExAMP even though it is effective in intensity correction instead of pattern correction. [ABSTRACT FROM AUTHOR]

The strategies of the extrapolation adjusted by model prediction (ExAMP) blending scheme, which trusts the field pattern predicted by extrapolation and allows the field intensity to be adjusted by numerical weather prediction (NWP), for rainfall nowcasting are analyzed in this study. The McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) and the Weather Research and Forecasting (WRF) model serve as the extrapolation and NWP models, respectively. Seven 150-min rainfall nowcasting experiments with different strategies are carried out for 37 sampled periods from seven heavy rainfall events in Taiwan in 2019. The results of the overall statistics indicate that, for the extrapolation component, extrapolating the current rainfall rate estimated from the lowest dual-polarimetric radar observations is a superior strategy. The ExAMP scheme that blends the MAPLE and WRF forecasts can surpass both components in 150-min rainfall nowcasting, and an empirical limitation on the innovation of intensity during the blending procedure is found unnecessary in this study. Moreover, the spatial performance for two contrasting events reveals the ability of ExAMP in grasping the rainfall strengthening and weakening in different areas. The skill statistics separately at rainfall strengthening gauges and weakening gauges further prove the effectiveness of ExAMP even though it is effective in intensity correction instead of pattern correction. [ABSTRACT FROM AUTHOR]

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 pick-out 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. [ABSTRACT FROM AUTHOR]