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Ching-Sen Chen, Yi-Leng Chen, Che-Ling Liu, Pay-Liam Lin, and Wan-Chin Chen

Abstract

The seasonal variations of heavy rainfall days over Taiwan are analyzed using 6-yr (1997–2002) hourly rainfall data from about 360 rainfall stations, including high-spatial-resolution Automatic Rainfall and Meteorological Telemetry System stations and 25 conventional stations. The seasonal variations and spatial variations of nontyphoon and typhoon heavy rainfall occurrences (i.e., the number of rainfall stations with rainfall rate >15 mm h−1 and daily accumulation >50 mm) are also analyzed. From mid-May to early October, with abundant moisture, potential instability, and the presence of mountainous terrain, nontyphoon heavy rainfall days are frequent (>60%), but only a few stations recorded extremely heavy rainfall (>130 mm day−1) during the passage of synoptic disturbances or the drifting of mesoscale convective systems inland. During the mei-yu season, especially in early June, these events are more widespread than in other seasons. The orographic effects are important in determining the spatial distribution of heavy rainfall occurrences with a pronounced afternoon maximum, especially during the summer months under the southwesterly monsoon flow. After the summer–autumn transition, heavy rainfall days are most frequent over northeastern Taiwan under the northeasterly monsoon flow. Extremely heavy rainfall events (>130 mm day−1) are infrequent during the winter months because of stable atmospheric stratification with a low moisture content. Typhoon heavy rainfall events start in early May and become more frequent in late summer and early autumn. During the analysis period, heavy rainfall occurrences are widespread and dominated by extremely heavy rainfall events (>130 mm day−1) on the windward slopes of the storm circulations. The spatial distribution of typhoon heavy rainfall occurrences depends on the typhoon track with very little diurnal variation.

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Weiguo Wang, Bin Liu, Lin Zhu, Zhan Zhang, Avichal Mehra, and Vijay Tallapragada

Abstract

A new physically based horizontal mixing-length formulation is introduced and evaluated in the Hurricane Weather and Research Forecasting (HWRF) Model. Recent studies have shown that the structure and intensity of tropical cyclones (TCs) simulated by numerical models are sensitive to horizontal mixing length in the parameterization of horizontal diffusion. Currently, many numerical models including the operational HWRF Model formulate the horizontal mixing length as a fixed fraction of grid spacing or a constant value, which is not realistic. To improve the representation of the horizontal diffusion process, the new formulation relates the horizontal mixing length to local wind and its horizontal gradients. The resulting horizontal mixing length and diffusivity are much closer to those derived from field measurements. To understand the impact of different mixing-length formulations, we analyze the evolutions of an idealized TC simulated by the HWRF Model with the new formulation and with the current formulation (i.e., constant values) of horizontal mixing length. In two real-case tests, the HWRF Model with the new formulation produces the intensity and track forecasts of Hurricanes Harvey (2017) and Lane (2018) that are much closer to observations. Retrospective runs of hundreds of forecast cycles of multiple hurricanes show that the mean errors in intensity and track simulated by HWRF with the new formulation can be reduced approximately by 10%.

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Si Gao, Wei Zhang, Jia Liu, I.-I. Lin, Long S. Chiu, and Kai Cao

Abstract

Tropical cyclone (TC) intensity prediction, especially in the warning time frame of 24–48 h and for the prediction of rapid intensification (RI), remains a major operational challenge. Sea surface temperature (SST) based empirical or theoretical maximum potential intensity (MPI) is the most important predictor in statistical intensity prediction schemes and rules derived by data mining techniques. Since the underlying SSTs during TCs usually cannot be observed well by satellites because of rain contamination and cannot be produced on a timely basis for operational statistical prediction, an ocean coupling potential intensity index (OC_PI), which is calculated based on pre-TC averaged ocean temperatures from the surface down to 100 m, is demonstrated to be important in building the decision tree for the classification of 24-h TC intensity change ΔV 24, that is, RI (ΔV 24 ≥ 25 kt, where 1 kt = 0.51 m s−1) and non-RI (ΔV 24 < 25 kt). Cross validations using 2000–10 data and independent verification using 2011 data are performed. The decision tree with the OC_PI shows a cross-validation accuracy of 83.5% and an independent verification accuracy of 89.6%, which outperforms the decision tree excluding the OC_PI with corresponding accuracies of 83.2% and 83.9%. Specifically for RI classification in independent verification, the former decision tree shows a much higher probability of detection and a lower false alarm ratio than the latter example. This study is of great significance for operational TC RI prediction as pre-TC OC_PI can skillfully reduce the overestimation of storm potential intensity by traditional SST-based MPI, especially for the non-RI TCs.

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Vijay Tallapragada, Chanh Kieu, Samuel Trahan, Qingfu Liu, Weiguo Wang, Zhan Zhang, Mingjing Tong, Banglin Zhang, Lin Zhu, and Brian Strahl

Abstract

This study presents evaluation of real-time performance of the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecast (HWRF) modeling system upgraded and implemented in 2013 in the western North Pacific basin (WPAC). Retrospective experiments with the 2013 version of the HWRF Model upgrades for 2012 WPAC tropical cyclones (TCs) show significant forecast improvement compared to the real-time forecasts from the 2012 version of HWRF. Despite a larger number of strong storms in the WPAC during 2013, real-time forecasts from the 2013 HWRF (H213) showed an overall reduction in intensity forecast errors, mostly at the 4–5-day lead times. Verification of the H213’s skill against the climate persistence forecasts shows that although part of such improvements in 2013 is related to the different seasonal characteristics between the years 2012 and 2013, the new model upgrades implemented in 2013 could provide some further improvement that the 2012 version of HWRF could not achieve. Further examination of rapid intensification (RI) events demonstrates noticeable skill of H213 with the probability of detection (POD) index of 0.22 in 2013 compared to 0.09 in 2012, suggesting that H213 starts to show skill in predicting RI events in the WPAC.

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Weiguo Wang, Jason A. Sippel, Sergio Abarca, Lin Zhu, Bin Liu, Zhan Zhang, Avichal Mehra, and Vijay Tallapragada

Abstract

This note describes a modification of the boundary layer parameterization scheme in the Hurricane Weather Research and Forecasting (HWRF) Model, which improves the simulations of low-level wind and surface inflow angle in the eyewall area and has been implemented in the HWRF system and used in the operational system since 2016. The modification is on an observation-based adjustment of eddy diffusivity previously implemented in the model. It is needed because the previous adjustment resulted in a discontinuity in the vertical distribution of eddy diffusivity near the surface-layer top, which increases the friction within the surface layer and compromises the surface-layer constant-flux assumption. The discontinuity affects the simulation of storm intensity and intensification, one of the main metrics of model performance, particularly in strong tropical cyclones. This issue is addressed by introducing a height-dependent adjustment so that the vertical profile of eddy diffusivity is continuous throughout the boundary layer. It is shown that the implementation of the modification results in low-level winds and surface inflow angles in the storm’s eyewall region closer to observations.

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