Vol.29, NO.1, 2018
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Article Navigation >  Journal of Applied Meteorological Science  > 2018  >  29(1): 97-110
Yu Zhenshou, Ji Chunxiao, Yang Chen, et al. Impacts of assimilating wind profiler radar observations on precipitation prediction in Zhejiang Province. J Appl Meteor Sci, 2018, 29(1): 97-110. DOI:  10.11898/1001-7313.20180109.
Citation: Yu Zhenshou, Ji Chunxiao, Yang Chen, et al. Impacts of assimilating wind profiler radar observations on precipitation prediction in Zhejiang Province. J Appl Meteor Sci, 2018, 29(1): 97-110. DOI:  10.11898/1001-7313.20180109.
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Impacts of Assimilating Wind Profiler Radar Observations on Precipitation Prediction in Zhejiang Province

DOI: 10.11898/1001-7313.20180109

  • Zhejiang Institute of Meteorological Sciences, Hangzhou 310008

  • Received Date: 2017-06-30
  • Rev Recd Date: 2017-10-09
  • Publish Date: 2018-01-31
    Abstract
  • Wind profiler radar (WPR) is a new type of wind measuring radar, which has advantages of high spatial resolution, continuity and good instantaneity. With the increase of wind profile radar year by year, it is meaningful to apply this kind of wind field observations to the numerical model to improve the model prediction ability. The meso-scale numerical prediction model WRF and the assimilation system ADAS developed by Center for Analysis and Prediction of Storms, University of Oklahoma, is used to study effects of assimilating observations of 35 wind profiler radars in eastern China on precipitation prediction over Zhejiang. Prior to assimilation, 1 h average sampling product data are subjected to climate extreme inspection, consistency check and vertical thinning for quality control. A spring rainstorm process on 16-17 May 2014 is selected as an example to evaluate effects of WPR data assimilation on the quality of precipitation forecast in detail. And effects of WPR data are also verified by batch experiments starting from 0000 UTC and 1200 UTC during the whole June of 2015. Results show that the model precipitation TS and ETS scores are improved, especially for heavy rainfalls. At the same time, the false alarm ratio (FAR) and frequency of misses (FOM) for heavy and torrential rain decrease after WPR data assimilation, but the FAR of moderate rain increase. The case study shows that WPR data assimilation can adjust the initial field of low layer wind field, increase small scale weather information, and improve the horizontal wind prediction on the whole layers. For 12 h wind forecast field, the result of assimilation of WPR is obviously better than that without the assimilation. In addition, the improvement of the zonal wind is more obvious than that of the meridional wind after WPR data assimilation. The case study shows that 850 hPa wind speed is enhanced by 20%-30%, water vapor flux is increased by 30%-50%, and the atmospheric instability in the rainstorm area and its upstream region is also enhanced after WPR data assimilation. As a result, TS of light rain and heavy rain is increased by 0.06-0.07, and FAR and FOM of rainstorm is reduced by 0.04-0.05. Although the assimilation of wind profiler data can improve the precipitation prediction quality, there are still some problems, such as an unexplained overestimation of regional average precipitation, which needs further investigation.
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  • Fig. 1  Simulated model domains and wind profile radar with radiosonde stations

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    Fig. 2  24 h accumulated precipitation(the shaded) in Zhejiang Province from 0000 UTC 16 May to 0000 UTC 17 May in 2014

    (a)observation(the countor denotes 1 h accumulated precipitation from 1500 UTC 16 May to 1600 UTC 16 May in 2014, unit: mm), (b)WPRDA, (c)CTL

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    Fig. 3  Assessment of radar wind profile data assimilation on the simulated 24 h accumulated precipitation in Zhejiang Province from 0000 UTC 16 May to 0000 UTC 17 May in 2014

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    Fig. 4  The initial field difference at 850 hPa between WPRDA and CTL

    (a)zonal wind(the contour, unit:m·s-1) and vorticity(the shaded, unit:10-5 s-1)(the long dashed line rectangle box denotes the rainstorm area over Zhejiang Province, and the solid line rectangle box denotes the upstream area), (b)meridional wind(the contour, unit:m·s-1) and divergence(the shaded, unit:10-5 s-1)

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    Fig. 5  The initial wind field(the vector) and water vapor flux(the shaded, unit:g·(cm·hPa·s)-1) at 850 hPa

    (a)WPRDA, (b)CTL

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    Fig. 6  Comparisons between the simulated initial horizontal wind profiles and the observation

    (a)zonal wind at Hangzhou, (b)meridional wind at Hangzhou, (c)zonal wind at Quzhou, (d)meridional wind at Quzhou

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    Fig. 7  Comparisons between 12 h forecast horizontal wind profiles and the observation

    (a)zonal wind at Hangzhou, (b)meridional wind at Hangzhou, (c)zonal wind at Quzhou, (d)meridional wind at Quzhou

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    Fig. 8  The simulated 1 h accumulated precipitation in Zhejiang Province from 1500 UTC 16 May to 1600 UTC 16 May in 2014

    (the triangle represents the center of maximum rainfall) (a)WPRDA, (b)CTL

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    Fig. 9  The vertical section along the red slash line shown in Fig. 8a at 1500 UTC 16 May 2014

    (a)the equivalent temperature(the black isoline, unit:K) and relative humidity(the shaded) of WPRDA, (b)the equivalent temperature(the black isoline, unit:K) and relative humidity(the shaded) of CTL, (c)the vertical section of rain water, cloud water, cloud ice, snow and graupel with 0℃, -20℃ layer height of WPRDA, (d)the vertical section of rain water, cloud water, cloud ice, snow and graupel with 0℃, -20℃ layer height of CTL

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    Fig. 10  Evaluation of regional average rainfall in Zhejiang Province by batch experiments from 1 Jun to 30 Jun in 2015

    (a)Zhejiang regional average daily precipitation simulated by CTL00 and WPRDA00 with the observation, (b)Zhejiang regional average daily precipitation simulated by CTL12 and WPRDA12 with the observation, (c)TS and ETS of CTL00 and WPRDA00 forecasts, (d)TS and ETS of CTL12 and WPRDA12 forecasts, (e)FAR and FOM of CTL00 and WPRDA00 forecasts, (f)FAR and FOM of CTL12 and WPRDA12 forecasts

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    Fig. 11  Evaluation of precipitation forecast by BWPRDA and BCTL from 1 Jun to 30 Jun in 2015

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    Table  1  Design for comparative WPR assimilation experiments

    试验类型 各试验设计描述
    控制试验CTL 针对2014年5月16—17日发生在浙江的一次暴雨过程，不同化任何观测资料，直接采用NCEP GFS提供的预报场作为WRF初始场和侧边界场，不同化任何观测资料，从5月16日00:00(世界时，下同)启动，积分24 h，称该试验为CTL
    同化试验WPRDA 试验中其他条件同CTL，只是增加了同化中国东部地区35部风廓线雷达资料(图 1)，称该试验为WPRDA
    批量控制试验
    BCTL    		 针对2015年6月1—30日用NCEP GFS预报场作为WRF的初始场和侧边界场，不同化任何观测资料，从00:00和12:00开始积分24 h的试验, 分别称为CTL00和CTL12，CTL00和CTL12合并称为BCTL
    批量同化试验
    BWPRDA    		 试验中其他条件同CTL00和CTL12，只是增加了同化中国东部地区35部风廓线雷达资料，试验对应称为WPRDA00和WPRDA12，WPRDA00和WPRDA12合并称为BWPRDA
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    • Received : 2017-06-30
    • Accepted : 2017-10-09
    • Published : 2018-01-31
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