Improving Afternoon Thunderstorm Prediction over Taiwan through 3DVAR-based Radar and Surface Data Assimilation

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  • 1 Central Weather Bureau, Taipei, Taiwan
  • 2 Meteorologisches Institute, Ludwig-Maximilians-Universität, Munich, Germany
  • 3 Central Weather Bureau, Taipei, Taiwan
  • 4 Central Weather Bureau, Taipei, Taiwan
  • 5 Central Weather Bureau, Taipei, Taiwan
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Abstract

Recently, Central Weather Bureau of Taiwan developed a WRF and WRFDA based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate questions included (1) Is the designation of a rapid update cycle strategy with a blending scheme effective? (2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? (3) What is the relative importance between radar and surface observation to AT prediction? (4) Whether we can increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 08 July 2017 are investigated. Five experiments, each has 240 continuous cycles, are designed.

Results show that employing continuous cycles with a blending scheme mitigates model spin up compared with downscaled forecasts. Although there are little radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface contributes positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.

Corresponding author address: Jing-Shan Hong, Central Weather Bureau, No. 64, Gongyuan Road, Taipei 100006, Taiwan, E-mail: rfs14@cwb.gov.tw

Abstract

Recently, Central Weather Bureau of Taiwan developed a WRF and WRFDA based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate questions included (1) Is the designation of a rapid update cycle strategy with a blending scheme effective? (2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? (3) What is the relative importance between radar and surface observation to AT prediction? (4) Whether we can increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 08 July 2017 are investigated. Five experiments, each has 240 continuous cycles, are designed.

Results show that employing continuous cycles with a blending scheme mitigates model spin up compared with downscaled forecasts. Although there are little radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface contributes positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.

Corresponding author address: Jing-Shan Hong, Central Weather Bureau, No. 64, Gongyuan Road, Taipei 100006, Taiwan, E-mail: rfs14@cwb.gov.tw
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