Typhoons with “deflection tracks” (DTs) within a 200-km distance of the mountainous island of Taiwan are examined. We analyze 84 landfalling typhoons that compose 49 DT cases turning to the left-hand side, including 18 with very large deflection angles (DA > 20°) and another 7 having looped tracks (LTs). Most of the large DA and LT cases are “northern landfall” type, reaching Taiwan’s east coast poleward of 24°N and originally possessing relatively slow translation speeds (~4 m s⁻¹). Their average translation speeds, however, increase by 50% in the 3 h prior to landfall. The WRF Model is used to simulate DT cases, and potential vorticity (PV) tendency diagnosis is used to interpret the contributions of the horizontal advection (HA), vertical advection (VA), and diabatic heating (DH) terms. The northern landfall tropical cyclones (TCs) possess significant cross-mountain flow to the south of the storm near the coast, resulting in vorticity stretching (the VA effect) and subsidence warming. The subsidence suppresses storm convection and produces heating asymmetries (the DH effect) that can induce significant southwestward deflections. The cross-mountain VA and DH effects are weaker for the “southern landfall” storms. The results explain well the observed increase of translation speed prior to landfall in DT cases and show that the HA effect, in general, does not contribute to the track deflection. Our results highlight the impact of topography on TC track by the vorticity stretching effect and by asymmetric diabatic heating.

In this study, we propose a volume‐to‐point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location‐specific precipitations. In contrast to QPE methods based on the Z–R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013–2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume‐to‐point framework performed comparably well in general cases and excelled in detecting heavy‐rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.

This study conducted a cluster analysis on the fine particulate matter (PM_2.5) data over Taiwan from 2006 to 2015 and diagnosed their association with the synoptic weather patterns. Five clusters are identified via a hierarchical clustering algorithm; three of them correspond to severe events, each with a distinct pattern of temporal evolution within the 240-h window. The occurrence of the different clusters exhibits strong seasonal variation. Two of the polluted clusters are more frequently associated with weak synoptic weather, while the other one is related to northeasterly winds and fronts. Detailed case studies show that the weather patterns’ temporal evolutions clearly modulate the transition among various pollution clusters by influencing the changes in local circulation and atmospheric stability. In winter, the clusters characterizing severe PM_2.5 pollution events occur when Taiwan is influenced by persistent weak synoptic condition, while in autumn, the long-range transport by strong northerly winds leads to the occurrence of severe PM_2.5 pollution. The current results shed light on the potential of combining the data-driven approach and the numerical weather forecasting model to provide extended range forecasts of local air pollution forecasts.

This study explains why observed storms with long concentric eyewall (CE) duration often have a large moat size. A series of slab boundary layer model experiments are conducted with the free atmosphere forcing from different CE structures. The experimental results indicate that the inner eyewall pumping (IEP), the maximum vertical motion at the inner eyewall region, increases with the increase in moat size d and vortex maximum wind v_m, the decrease in the inner eyewall radius r_m, and increase in inner eyewall vorticity. A scaling law is derived from hundreds of SBL experiments with four variables: IEP w_in, vortex inner radius r_m, vortex maximum wind v_m, and moat size d. The dimensionless scaling law is where w^∗ = w_in/v_m, d^∗ = ζd/c, ζ is the vorticity 2v_m/r_m, and c is a constant gravity wave speed. The moat size divided by the Rossby length c/ζ, the dimensionless moat size d^∗, combines the effect of the moat size and the vortex pressure gradient force to accelerate the inflow and to enlarge the IEP for inner eyewall convection maintenance. A phase diagram of IEP with vortex structure dimensionless moat ζd/c and vortex intensity v_m is constructed based on the scaling law for the aircraft and satellite observations. The eyewall replacement cycle may be viewed as the process to reduce the IEP from the decrease of the vortex intensity and the vortex size of the dimensionless moat. This leads to the ultimate disappearance of the inner eyewall.

Typhoon Saola affected Taiwan from July 30, 2012, to August 03, 2012. It started in a northwestward track, and it was weakening to Category 1 storm and had a sharp cyclonic deflection track when it was about to made landfall in Taiwan on August 02. After the deflection, the typhoon moved northward and then northwestward across northern Taiwan. The cyclonic southward deflection and the northward motion constituted 12 h of looping motion which increase both the typhoon duration time and the rainfall in northeastern Taiwan. This research is to study the looping motion by the Weather Research and Forecasting model for this very weak typhoon. The model is able to reproduce the looping motion similar to that of observation. The model shows a strong asymmetry of precipitation and cross‐mountain downslope wind near the typhoon center while the typhoon track is looping toward the southwest. The potential vorticity (PV) tendency diagnosis confirms that diabatic heating and downslope wind vertical stretching are the major contributors to tropical cyclone southwestward motion, rather than the commonly referred channeling effect. The asymmetric rainfall disappears when the tropical cyclone starts moving northward in its end‐stage of looping. The northward motion is dominated by horizontal advection of PV tendency. The results highlight the influence of convection and downslope wind near steep terrain on the initial southward motion in the looping track. The model sensitivity study indicates that a large part of track uncertainty resides in the interaction of model internal dynamics and model cloud microphysics.

This study aims to propose a strategy to optimize the performance of the Support Vector Machine (SVM) scheme for extreme Meiyu rainfall prediction over southern Taiwan. Variables derived from Climate Forecast System Reanalysis (CFSR) dataset are the candidates for predictor selection. A series of experiments with different combinations of predictors and domains are designed to obtain the optimal strategy for constructing the SVM scheme. The results reveal that the accuracy (ACC), positive predictive values (PPV), probability of detection (POD), and F1-score can exceed 0.6 on average. Choosing the predictors associated with the Meiyu system and determine the domain associated with the correlations between selected predictors and predictand can improve the forecast performance. Our strategy shows the potential to predict extreme Meiyu rainfall in southern Taiwan with lead times from 16 h to 64 h. The F1-score analysis further demonstrates that the forecast performance of our scheme is stable, with slight inter-annual fluctuations from 1990 to 2019. Higher performance would be expected when the north of the South China Sea is characterized by stronger southwesterly flow and abundant low-level moisture for a given year.