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Chuan-Yao Lin
,
Wan-Chin Chen
,
Pao-Liang Chang
, and
Yang-Fan Sheng

Abstract

To evaluate the impacts of the urban heat island (UHI) effect on precipitation over a complex geographic environment in northern Taiwan, the next-generation mesoscale model, the Weather Research and Forecasting (WRF) model, coupled with the Noah land surface model and urban canopy model (UCM), was used to study this issue. Based on a better land use classification derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (the MODIS case), it has significantly improved simulation results for the accumulation rainfall pattern as compared with the original U.S. Geological Survey (USGS) 25-category land use classification (the USGS case). The precipitation system was found to develop later but stronger in the urban (MODIS) case than in the nonurban (USGS) case. In comparison with the observation by radar, simulation results predicted reasonably well; not only was the rainfall system enhanced downwind of the city over the mountainous area, but it also occurred at the upwind plain area in the MODIS case. The simulation results suggested that the correct land use classification is crucial for urban heat island modeling study. The UHI effect plays an important role in perturbing thermal and dynamic processes; it affects the location of thunderstorms and precipitation over the complex geographic environment in northern Taiwan.

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I-Han Chen
,
Yi-Jui Su
,
Hsiao-Wei Lai
,
Jing-Shan Hong
,
Chih-Hsin Li
,
Pao-Liang Chang
, and
Ying-Jhang Wu

Abstract

A 16-member convective-scale ensemble prediction system (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 1300 and 2000 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 0500, 0800, and 1100 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread–skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 1100 LST outperform those launched at 0500 and 0800 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 1100 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses.

Significance Statement

This study aims to understand the behavior of convective-scale short-range probabilistic forecasts in Taiwan and the surrounding area. Taiwan is influenced by diverse weather systems, including typhoons, mei-yu fronts, and local thunderstorms. During the past decade, there has been promising improvement in predicting mesoscale weather systems (e.g., typhoons and mei-yu fronts). However, it is still challenging to provide timely and accurate forecasts for rapid-evolving high-impact convection. This study provides a reference for the designation of convective-scale ensemble prediction systems; in particular, those with a goal to provide short-range probabilistic forecasts. While the findings cannot be extrapolated to all ensemble prediction systems, this study demonstrates that initial and boundary perturbations are the most important factors, while the model perturbation has an insignificant effect. This study suggests that in-depth studies are required to improve the convective-scale initial condition accuracy and uncertainty to provide reliable probabilistic forecasts within short lead times.

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Pao-Liang Chang
,
Jian Zhang
,
Yu-Shuang Tang
,
Lin Tang
,
Pin-Fang Lin
,
Carrie Langston
,
Brian Kaney
,
Chia-Rong Chen
, and
Kenneth Howard

Abstract

Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.

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Pin-Fang Lin
,
Pao-Liang Chang
,
Ben Jong-Dao Jou
,
James W. Wilson
, and
Rita D. Roberts

Abstract

The spatial and temporal characteristics and distributions of thunderstorms in Taiwan during the warm season (May–October) from 2005 to 2008 and under weak synoptic-scale forcing are documented using radar reflectivity, lightning, radiosonde, and surface data. Average hourly rainfall amounts peaked in midafternoon (1500–1600 local solar time, LST). The maximum frequency of rain was located in a narrow strip, parallel to the orientation of the mountains, along the lower slopes of the mountains. Significant diurnal variations were found in surface wind, temperature, and dewpoint temperature between days with and without afternoon thunderstorms (TSA and non-TSA days). Before thunderstorms occurred, on TSA days, the surface temperature was warmer (about 0.5°–1.5°C) and the surface dewpoint temperature was moister (about 0.5°–2°C) than on non-TSA days. Sounding observations from northern Taiwan also showed warmer and higher moisture conditions on TSA days relative to non-TSA days. The largest average difference was in the 750–550-hPa layer where the non-TSA days averaged 2.5°–3.5°C drier. These preconvective factors associated with the occurrences of afternoon thunderstorms could be integrated into nowcasting tools to enhance warning systems and decision-making capabilities in real-time operations.

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Pin-Fang Lin
,
Pao-Liang Chang
,
Ben Jong-Dao Jou
,
James W. Wilson
, and
Rita D. Roberts

Abstract

In this study, a fuzzy logic algorithm is developed to provide objective guidance for the prediction of afternoon thunderstorms in northern Taiwan using preconvective predictors during the warm season (May–October) from 2005 to 2008. The predictors are derived from surface stations and sounding measurements. The study is limited to 277 days when synoptic forcing was weak and thermal instability produced by the solar heating is primarily responsible for thunderstorm initiation. The fuzzy algorithm contains 29 predictors and associated weights. The weights are based on the maximum of the critical success index (CSI) to forecast afternoon thunderstorms. The most important predictors illustrate that under relatively warm and moist synoptic conditions, sea-breeze transport of moisture into the Taipei Basin along with weak winds inland provide favorable conditions for the occurrence of afternoon convective storms. In addition, persistence of yesterday’s convective storm activity contributed to improving today’s forecast. Skill score comparison between the fuzzy algorithm and forecasters from the Taiwan Central Weather Bureau showed that for forecasting afternoon thunderstorms, the fuzzy logic algorithm outperformed the operational forecasters. This was the case for both the calibration and independent datasets. There was a tendency for the forecasters to overforecast the number of afternoon thunderstorm days. The fuzzy logic algorithm is able to integrate the preconvective predictors and provide probability guidance for the prediction of afternoon thunderstorms under weak synoptic-scale conditions, and could be implemented in real-time operations as a forecaster aid.

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