台风降水的WRF模拟:以福建省杜苏芮台风为例（英文）

吴静雯; 颜悠逸; 殷方旭; 游介文; 庄瑶; 官晓军; 姜立智; 高路

doi:10.13928/j.cnki.wrahe.2025.11.001

水利水电技术（中英文） ›› 2025, Vol. 56 ›› Issue (11) : 1 -20. DOI: 10.13928/j.cnki.wrahe.2025.11.001

复杂灾害链与水旱巨灾风险评估专栏

台风降水的WRF模拟:以福建省杜苏芮台风为例（英文）

吴静雯 ¹^,²^,³ ,
颜悠逸 ¹^,²^,³ ,
殷方旭 ¹^,²^,³ ,
游介文 ¹^,²^,³^,⁷ ,
庄瑶 ⁴^,⁵^,⁶ ,
官晓军 ⁵ ,
姜立智 ⁵ ,
高路 ¹^,²^,³^,⁷

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WRF simulation of typhoon precipitation: A case study of Typhoon Doksuri in Fujian Province, China

Jingwen WU ¹^,²^,³ ,
Youyi YAN ¹^,²^,³ ,
Fangxu YIN ¹^,²^,³ ,
Jiewen YOU ¹^,²^,³^,⁷ ,
Yao ZHUANG ⁴^,⁵^,⁶ ,
Xiaojun GUAN ⁵ ,
Lizhi JIANG ⁵ ,
Lu GAO ¹^,²^,³^,⁷

Author information +

文章历史 +

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摘要

【目的】基于WRF模式模拟并评估超强台风杜苏芮在福建省造成的降水事件,为中国东南沿海地区的台风降水模拟和预报提供参考依据。【方法】基于新一代中尺度数值天气预报模式WRF V4.3(The Weather Research and Forecasting Model)模拟了2023年杜苏芮台风在福建省造成的降水事件,并利用86个气象站的逐小时降水观测记录,使用相关系数R、均方根误差RMSE、平均绝对误差M AE、公平威胁评分ETS、命中率POD和误报率FAR等6个指标评估WRF模拟台风降水的性能。【结果】结果表明:(1)WRF模式有效地捕捉了杜苏芮台风期间降水的时空演变特征,2023年7月27日至29日期间降水强度呈现逐步增加的趋势,最大降水量集中在福建省北部和东部沿海区域。(2)R、 RMSE和MAE指标的结果存在差异,最大误差出现在莆田市,福建西南部的误差较小。6个指标的评估结果表明,相较于小时、三小时、六小时和十二小时降水,WRF模型对日降水的模拟表现最好。(3)WRF模式通过R95p指数能够捕捉到极端降水的总体空间分布,但在某些沿海区域高估了极端降水的强度。(4)尽管WRF模式能够正确识别出福建沿海地区是受灾最严重的区域,但未能精确模拟出降水空间分布和强度,模拟的降水中心与观测中心存在一定偏差。【结论】尽管WRF模式的模拟结果低估了逐小时降水量,但它捕捉到了杜苏芮台风期间福建省降水的时间演变和空间格局。WRF模式基本再现了福建省中部的暴雨中心,日降水总量峰值高达350 mm,凸显了杜苏芮台风引发的极端降水的严重性。

Abstract

[Objective] Precipitation events caused by Super Typhoon Doksuri in Fujian Province were simulated and evaluated based on the WRF model to provide a reference for typhoon precipitation simulation and forecasting in southeast coastal areas of China. [Methods] The next-generation mesoscale numerical weather prediction model WRF V4. 3(The Weather Research and Forecasting Model) was used to simulate the precipitation caused by Typhoon Doksuri in Fujian Province in 2023. Observations from 86 meteorological stations with hourly rainfall records were used to evaluate the model's performance. Six evaluation indices were used, including the correlation coefficient(R), root mean square error(RMSE), mean absolute error(MAE), equitable threat score(ETS), probability of detection(POD), and false alarm ratio(FAR). [Results] (1) The temporal and spatial evolution of precipitation during Typhoon Doksuri was effectively captured by the WRF model. Precipitation intensity increased gradually from July 27 to 29, 2023, with the heaviest rainfall concentrated in the northern and eastern coastal areas of Fujian Province.(2) Significant differences in model performance were observed in terms of R, RMSE, and MAE. The largest errors occurred in Putian City, while smaller errors were found in southwestern Fujian Province. The evaluation result of all six indices showed that the WRF model performed best in simulating daily precipitation compared to hourly, three-hourly, six-hourly, and twelve-hourly precipitation.(3) The R95p index indicated that the WRF model successfully captured the overall spatial distribution of extreme precipitation. However, extreme precipitation intensity was overestimated in certain coastal areas.(4) Despite accurately identifying the coastal regions of Fujian as being most affected, the WRF model failed to accurately simulate the spatial distribution and intensity of precipitation. The simulated precipitation centers showed discrepancies when compared with the observed centers. [Conclusion] Although the WRF model underestimated hourly precipitation, it successfully captured the temporal evolution and spatial distribution of rainfall caused by Typhoon Doksuri in Fujian Province. It reproduced the heavy rainfall centers in central Fujian Province, with daily precipitation peaks reaching up to 350 mm. This highlighted the severity of extreme rainfall caused by Typhoon Doksuri.