Improving the Simulation of Typhoon Mujigae (2015) Based on Radar Data Assimilation

Feng Jianing; Duan Yihong; Xu Jing; Zhang Xinghai; Hu Hao

doi:10.11898/1001-7313.20170402

Vol.28, NO.4, 2017

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Article Navigation > Journal of Applied Meteorological Science > 2017 > 28(4): 399-413

Feng Jianing, Duan Yihong, Xu Jing, et al. Improving the simulation of typhoon Mujigae (2015) based on radar data assimilation. J Appl Meteor Sci, 2017, 28(4): 399-413. DOI: 10.11898/1001-7313.20170402.

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Feng Jianing, Duan Yihong, Xu Jing, et al. Improving the simulation of typhoon Mujigae (2015) based on radar data assimilation. J Appl Meteor Sci, 2017, 28(4): 399-413. DOI: 10.11898/1001-7313.20170402.

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Improving the Simulation of Typhoon Mujigae (2015) Based on Radar Data Assimilation

DOI: 10.11898/1001-7313.20170402

State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing 100081

Received Date: 2017-03-10
Rev Recd Date: 2017-05-19
Publish Date: 2017-07-31

Abstract

Abstract

Typhoon intensity and precise structure are hardly to predict by all kinds of numerical model, and one key problem is the lack of precise initialization data. Through a WRF-based ensemble Kalman filtering (EnKF) data assimilation system, impacts of assimilating China's coastal Doppler radar velocity observations for track, intensity and structure of Typhoon Mujigae (2015) is examined.Furthermore, assimilating sensitivity of observations in relative regions are also explored. The experimental results show that mean track error and max track error is reduced by 15 km and 38 km, respectively. The track error of the EnKF analysis becomes smaller with more cycles of assimilating data, and so do the deterministic forecast driven by EnKF analysis field. Through data assimilation, offshore enhancement process in Mujigae is well simulated. Intensity error in both EnKF analysis and prediction are smaller than 25 hPa after assimilation. After 9 h cycling radar velocity data assimilation, the deterministic forecast shows the typhoon continue to strengthen before landfall, and the typhoon eye is contracted much after data assimilation. The diameter of typhoon eye is reduced by about 70 km, and the eye wall convection asymmetric structure is closer to observation.The sensitivity of radar observation assimilation is tested by different radial distance area. Numerical sensitivity experiments show that radar observations within 100 km of the typhoon's inner core play a dominate role to assimilation results. Typhoon track, intensity and structure are all closer to observation by assimilating radar data within 100 km from typhoon center (about 20% of total observation) showing equivalent effects as assimilating all data. Typhoon is somewhat modified by cycling assimilating observations within 100-200 km from typhoon center. There is no obvious enhancement in typhoon track, intensity and structure after assimilating data 200 km away from inner core. Therefore, radar observation located in typhoon kernel is the key to determine assimilation effects. Because of less data assimilated, the strategy of only assimilating inner core radar data can reduce computing time to 1/3 of all data with somewhat same assimilation result. Efficiency of radar assimilation can be much improved by this radar assimilating strategy, and it can give reference to official typhoon real-time data assimilation and prediction work.
- typhoon modeling;
- data assimilation;
- ensemble Kalman filtering;
- radar data

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Figures and Tables

图 1 模拟试验设计方案(a)海口多普勒天气雷达(Z9898) 及其扫描覆盖范围与台风彩虹实测路径示意图(虚线表示资料同化时段), (b)模拟试验网格区域设置

Fig. 1 Experiment design of modeling (a)location of Haikou Doppler radar(site number is Z9898) and its radial velocity coverage with best track of Typhoon Mujigae(2015)(the dashed line denotes the period during which radar data is assimilated), (b)domain configuration of model

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图 2 雷达超级观测资料分布(a)全部资料，(b)距台风中心小于100 km范围资料，(c)距台风中心100~200 km范围资料，(d)距台风中心200 km以外资料

Fig. 2 Scattering gram of super observations(SO) in assimilation experiments (a)total SO, (b)SO within 100 km from the typhoon center, (c)SO in 100-200 km from the typhoon center, (d)SO out of 200 km from the typhoon center

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图 3 台风彩虹路径分析(EnKF同化试验)与预报(DF_03T22，DF_04T00，DF_04T02，DF_04T04)

Fig. 3 The EnKF analysis and deterministic forecast (DF_03T22, DF_04T00, DF_04T02, DF_04T04) of Typhoon Mujigae(2015)

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图 4 2015年10月3—4日台风彩虹最低海平面气压(a)同化分析，(b)确定性预报

Fig. 4 Minimum sea-level pressure of Typhoon Mujigae(2015) by EnKF analysis(a) and forecast(b) from 3 Oct to 5 Oct in 2015

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图 5 2015年10月3—4日雷达组合反射率因子

Fig. 5 The composite radar reflectivity of Typhoon Mujigae(2015) from 3 Oct to 4 Oct in 2015

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图 6 同化分析增量(a)2015年10月3日20:00 300 hPa位温增量(填色)和风矢量增量，(b)2015年10月4日04:00 300 hPa位温增量(填色)和风矢量增量，(c)2015年10月3日20:00 850 hPa涡度增量，(d)2015年10月4日04:00 850 hPa涡度增量(黑色圆点和绿色圆点分别表示观测和分析场台风位置)

Fig. 6 Analysis increments (a)300 hPa potential temperature increment(the shaded) and wind increment at 2000 UTC 3 Oct 2015, (b)300 hPa potential temperature increment(the shaded) and wind increment at 0400 UTC 4 Oct 2015, (c)850 hPa vorticity increment at 2000 UTC 3 Oct 2015, (d)850 hPa vorticity increment at 0400 UTC 4 Oct 2015 (black and green dots denote typhoon location of observation and analysis)

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图 7 3组敏感性试验雷达超级观测资料盒须图(a)全部资料，(b)距台风中心小于100 km范围资料，(c)距台风中心100~200 km范围资料，(d)距台风中心200 km以外资料(折线为最佳路径近中心最大风速)

Fig. 7 Box-and-whisker plot of radar SO in assimilation experiments (a)total SO, (b)SO within 100 km from the typhoon center, (c)SO in 100-200 km from the typhoon center, (d)SO out of 200 km from the typhoon center(the black curve denotes the maximum wind speed from JTWC best track)

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图 8 不同同化敏感性试验同化分析对比

(a)强度，(b)路径，(c)路径误差，(d)路径相对EnKF同化试验偏差

Fig. 8 Minimum sea surface level pressure(a), track(b), track error(c) and track bias to EnKF(d) of Typhoon Mujigae(2015) in sensitivie experiments

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图 9 2015年10月4日敏感性试验模拟雷达组合反射率因子

Fig. 9 The composite radar reflectivity of Typhoon Mujigae(2015) in sensitive experiments

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图 10 2015年10月3—4日不同试验雷达超级观测资料数量对比

Fig. 10 The number of SO in sensitivie expriments from 3 Oct to 4 Oct in 2015

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