STUDY ON THE APPLICATION OF AMVS IN CLOUD-FREE REGIONS BASED ON FY-2E SATELLITE IN TYPHOON ANALYSIS AND FORECAST
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摘要: 为了研究“二阶差分法”反演的晴空区风矢同化在台风分析和预报中的作用，以1509号台风“灿鸿”和1211号台风“海葵”为例，首先利用WRF-3DVAR系统对晴空风矢进行同化，探讨了晴空风矢的引入对模式初始场的影响。然后利用WRF模式对两个个例分别进行48 h的预报试验。通过对比控制试验和同化试验，结果表明，同化晴空风矢资料能够对初始风场和位势高度场进行合理的调整，在台风周围引导气流的作用下，台风路径与实况更靠近，从而提高了台风路径的预报效果。除此之外，同化晴空风矢对台风强度以及风场预报也有一定的改善作用，还可更准确地预报出降水的落区及雨强，提高降水预报质量。因此，晴空风矢的引入，有利于改善模式的初始场，从而提高WRF模式对台风的预报能力。Abstract: Atmospheric motion vectors (AMVs) in cloud-free regions are derived by using the Second Order method. The purpose of this study is to investigate the effects of AMVs data assimilation on typhoon's initial fields and forecast fields. Taking Typhoons Chan-hom (1509) and Haikui (1211) as illustrating cases, this paper assimilates the AMVs in the WRF model by using the WRF-3Dvar system, and then discusses effects of AMVs on the assimilated initial fields. At last, with the initial fields attained by assimilation, the processes of Typhoons Chan-hom and Haikui have been studied respectively by 48-hour numerical experiments through the WRF model. Comparing the control with the assimilation experiment shows that, after assimilating the AMVs, the wind and geopotential height of initial fields around the typhoons become more reasonable, and thus the steering current guiding them to move to the correct location becomes stronger. As a result, the numerical track predictions can be improved. In addition, the assimilation of AMVs can improve the predictions of typhoon intensity and wind fields in such way that it can yield precipitation forecasts with more accurate location and intensity. Therefore, assimilating AMVs in cloud-free regions can well improve the initial fields in the WRF model, thus improving its forecasting ability.
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图 4 三个等压面上的位势高度增量
单位: gpm。
a、d. 200 hPa; b、e. 500 hPa; c、f. 800 hPa；a~c. 10日12时“灿鸿”；d~f. 6日00时“海葵”。
图 8 24 h累积降水量
单位：mm。a、d.控制试验；b、e.同化试验；c、f.实况。
a~c. “灿鸿”2015年7月11日00时—12日00时(UTC); d~f. “海葵”2012年8月7日00时—8日00时(UTC)。表 1 质量控制前后的晴空风场与NCEP风场对比的BIAS、SD、RMSE
个例 日期 质控前 质控后 BIAS SD/(m/s) RMSE/(m/s) BIAS SD/(m/s) RMSE/(m/s) V/(m/s) Ang/° V/(m/s) Ang/° “灿鸿” 12z09Jul 11.52 11.71 12.21 21.86 0.43 11.30 0.88 4.99 18z09Jul 11.65 -29.52 4.09 20.01 1.33 -3.88 1.94 5.47 00z10Jul 11.56 -25.17 5.12 18.90 1.94 4.59 3.76 6.71 06z10Jul 11.26 -13.28 10.37 20.53 1.10 7.45 2.65 5.13 12z10Jul 12.14 20.47 2.07 18.35 1.09 -2.42 0.37 4.11 平均 11.63 -7.16 6.77 19.93 1.18 3.41 1.92 5.28 “海葵” 12z05Aug 10.91 -2.99 7.51 19.93 1.01 -2.06 1.28 3.70 18z05Aug 11.91 -10.68 7.79 21.03 0.89 0.47 1.89 4.60 00z06Aug 11.50 4.89 6.69 17.80 -0.57 2.00 3.98 7.02 平均 11.44 2.93 7.33 19.59 0.44 0.14 2.38 5.11 
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表 2 质量控制前后的晴空风场与探空风对比的BIAS、SD、RMSE
个例 日期 质控前 质控后 BIAS SD/(m/s) RMSE/(m/s) BIAS SD/(m/s) RMSE/(m/s) V/(m/s) Ang/° V/(m/s) Ang/° “灿鸿” 12z09Jul 9.35 66.24 12.70 21.01 0.26 23.99 2.69 6.87 00z10Jul 7.30 12.89 12.78 18.50 1.42 5.69 2.05 4.63 12z10Jul 7.71 51.33 9.86 15.64 0.20 6.77 2.20 3.84 平均 8.12 43.49 11.78 18.38 0.63 12.15 2.31 5.11 “海葵” 12z05Aug 7.78 33.23 8.09 14.31 -0.12 -0.39 1.65 3.85 00z06Aug 7.23 41.62 9.49 16.47 -0.07 -6.62 1.56 3.51 平均 7.51 37.43 8.79 14.89 -0.10 -3.51 1.60 3.68
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表 3 试验方案
个例 试验名称 同化方案 同化时间 “灿鸿” CTRL 不同化晴空风矢资料 无 AMVs 每6 h循环同化质量控制后的晴空风矢 201507091200 UTC—101200 UTC “海葵” CTRL 不同化晴空风矢资料 无 AMVs 每6 h循环同化质量控制后的晴空风矢 201208051200 UTC—060000 UTC
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表 4 24 h累积雨量的均方根误差(RMSE)及相关系数(R)
台风 站点数 试验名称 RMSE/mm R “灿鸿” 323 CTRL 50.08 0.47 AMVs 21.95 0.75 “海葵” 141 CTRL 32.80 0.76 AMVs 32.64 0.78
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