Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon quantitative precipitation forecasts (QPFs) from numerical models is often controlled by track errors, with better quality from those with smaller track errors. However, the present work demonstrates that in daily QPFs over Taiwan made by a cloud-resolving model during five seasons of 2012–2016, targeted for 84 days during 27 typhoons and at ranges of day one (0–24 h) to day eight (168–192 h), the control of track errors on QPF quality is reduced for typhoons associated with southwesterly flow, compared to those without, and decent QPFs could still be obtained with large track errors in some cases. Subsequently, the circumstances and reasons for good (or bad) QPFs in selected examples are further investigated to deepen our understanding of typhoon QPFs in Taiwan. Some common ingredients are found in three cases where good QPFs were produced at a longer range (day 7 or 8) without a good track: these typhoons passed near northern Taiwan and the southwesterly flow prevailed over much of the island during the accumulation period. Responsible for much of the rainfall in Taiwan, the southwesterly flow was reasonably captured, resulting in good QPFs. In another example where the typhoon moved across southern Taiwan, on the contrary, the rainfall was produced by the storm’s circulation, and the QPF was degraded without a good enough track prediction.
This study focused on improving the forecasting of the afternoon thunderstorm (AT) event on 5 August 2018 near Pingtung Airport in southern Taiwan through a three-dimensional variational data assimilation system using Doppler lidar-based wind profiler data from the Weather and Research Forecast model. The assimilation of lidar wind profiler data had a positive impact on predicting the occurrence and development of ATs and wind fields associated with the local circulations of the sea–land breeze and the mountains. Evaluation of the model quantitative precipitation forecast by using root-mean-square error analysis, Pearson product–moment correlation coefficient analysis, Spearman rank correlation coefficient analysis, and threat and bias scores revealed that experiments using data assimilation performed much better than those not using data assimilation. Among the experiments using data assimilation, when the implementation time of assimilation of the wind profiler data in the model was closer to the occurrence time of the observed ATs, the forecast performance greatly improved. Overall, our assimilation strategy has crucial implications for the prediction of short-duration intense rainfall caused by ATs with small temporal and spatial scales of few hours and a few tens of kilometers. Our strategy can help guarantee the flight safety of aircraft.
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