The Influence of Assimilating Dropsonde Data on Typhoon Track and Midlatitude Forecasts
The Influence of Assimilating Dropsonde Data on Typhoon Track and Midlatitude Forecasts
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DOI：
10.1175/2010mwr3377.1
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发表时间：
2011-03
影响因子：
3.2
通讯作者：
M. Weissmann;F. Harnisch;Chun‐Chieh Wu;P. Lin;Y. Ohta;K. Yamashita;Yeonchun Kim;E. Jeon;T. Nakazawa;S. Aberson
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文献类型：
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作者：
M. Weissmann;F. Harnisch;Chun‐Chieh Wu;P. Lin;Y. Ohta;K. Yamashita;Yeonchun Kim;E. Jeon;T. Nakazawa;S. Aberson
在2008年秋季的观测系统研究和可预报性实验（THORPEX）太平洋亚洲区域运动（T-PARC）期间收集了一个独特的有针对性的下投式探空仪观测数据集。这场运动还得到了加强台湾地区附近台风监测的投下式探空仪观测业务方案的补充。首次有多达四架不同的飞机可用于台风观测，并收集了超过1500个额外的水深。本研究探讨在T-PARC的两个主要的台风事件同化额外的观测值的影响台风路径预报的欧洲中期天气预报中心（ECMWF），日本气象厅（JMA），国家环境预报中心（NCEP）的全球模式，和有限区域天气研究和预报（WRF）模式。此外，T-PARC观测对ECMWF中纬度预报的影响进行了研究。各模式对台风路径的预报都有改进的趋势，但改进的程度从NCE和WRF的20%~40%不等，到ECMWF和JMA的影响都很小，这可能与后两种模式在没有投下探空仪的情况下路径预报误差较小有关。可能是由于更广泛地使用卫星数据和ECMWF和JMA的四维变分数据同化（4D-Var）而不是三维变分数据同化（3D-Var）。模式的不同行为强调了所获得的收益强烈依赖于首猜场和同化系统的质量。
A unique dataset of targeted dropsonde observations was collected during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) in the autumn of 2008. The campaign was supplemented by an enhancement of the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. For the first time, up to four different aircraft were available for typhoon observations and over 1500 additional soundings were collected. This study investigates the influence of assimilating additional observations during the two major typhoon events of T-PARC on the typhoon track forecast by the global models of the European Centre for MediumRange Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the National Centers for Environmental Prediction (NCEP), and the limited-area Weather Research and Forecasting (WRF) model. Additionally, the influence of T-PARC observations on ECMWF midlatitude forecasts is investigated. All models show an improving tendency of typhoon track forecasts, but the degree of improvement varied fromabout20%to40%inNCEPandWRFtoacomparablylowinfluenceinECMWFandJMA.Thisislikely related to lower track forecast errors without dropsondes in the latter two models, presumably caused by a more extensive use of satellite data and four-dimensional variational data assimilation (4D-Var) of ECMWF and JMA compared to three-dimensional variational data assimilation (3D-Var) of NCEP and WRF. The different behavior of the models emphasizes that the benefit gained strongly depends on the quality of the first-guess field and the assimilation system.