Vol.26, NO.5, 2015
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Article Navigation >  Journal of Applied Meteorological Science  > 2015  >  26(5): 555-566
Zhang Xinzhong, Chen Junming, Zhao Ping. Impacts of Doppler radar data assimilation on the simulation of severe heavy rainfall events. J Appl Meteor Sci, 2015, 26(5): 555-566. DOI:  10.11898/1001-7313.20150505.
Citation: Zhang Xinzhong, Chen Junming, Zhao Ping. Impacts of Doppler radar data assimilation on the simulation of severe heavy rainfall events. J Appl Meteor Sci, 2015, 26(5): 555-566. DOI:  10.11898/1001-7313.20150505.
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Impacts of Doppler Radar Data Assimilation on the Simulation of Severe Heavy Rainfall Events

DOI: 10.11898/1001-7313.20150505
  • 1.

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

  • 2.

    Chinese Academy of Meteorological Sciences, Beijing 100081

  • Received Date: 2015-04-16
  • Rev Recd Date: 2015-06-25
  • Publish Date: 2015-09-30
    Abstract
  • The impact of Doppler weather radar (DWR) data on the simulation of a heavy rainfall event is examined. The quality control algorithm of DWR developed by Center for Analysis and Prediction of Storms is applied and the threshold for the raw S-band DWR radial velocity is decided. Several commonly seen non-meteorological returns can be removed effectively. The DWR reflectivity data are processed and the regional three-dimensional mosaic is generated using the CINRAD 3D Digital Mosaic System developed by State Key Laboratory of Severe Weather. Retrieval results match well with the observation. The Gridpoint Statistical Interpolation System (GSI) and the Weather Research and forecasting Model version 3.5.1 (WRF) are used to assimilate 46 S-band DWR data to simulate the severe heavy rain cases that occurred in Jun 2013. Numerical experiment results show that about 90% of the radial velocity data after quality control can be assimilated and generate reasonable analysis increments. Results also show that the assimilation of DWR data has a positive impact on the simulation of heavy rainfall. Assimilating radial velocity can enhance the information of mesoscale weather system in initial field and the simulated field, making the simulated wind fields and rainfall location more similar to the observation. Radar reflectivity data are used primarily in a cloud analysis that retrieves the amount of hydrometeors and adjusts in-cloud temperature and moisture. Assimilating radial velocity affects the zonal and vertical winds by adjusting the amount of hydrometers and moisture which have directly influence on generating precipitation. It changes the simulated rainfall intensity. Assimilating radial velocity and reflectivity at the same time can not only reflect the wind filed more reasonably, but also improve the simulation of rainfall intensity and area. In addition, improvements of the precipitation are most notable in the 12-36 h simulation when more effective radar data are available. Both ETS and HSS of experiment assimilating radar data are proved higher than CTRL experiment which only assimilates conventional data.
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    References(29)

  • Fig. 1  Mode domain (square frame) and the distribution of radar sites

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    Fig. 2  The observed accumulated precipitation from 0000 UTC 5 Jun to 0000 UTC 8 Jun in 2013(a) and from 0000 UTC 26 Jun to 1200 UTC 28 Jun in 2013(b)

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    Fig. 3  Variables of background and incremental ground at 0000 UTC 26 Jun in 2013

    (a)700 hPa streamlines before assimilating radar data, (b)700 hPa streamlines after assimilating radar data, (c)500 hPa temperature (the contour, unit:K) with the increment (the shaded), (d)850 hPa water vapor mixing ratio (the contour, unit:g/kg) with the increment (the shaded)

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    Fig. 4  24 h simulated (the contour) and obseverd (the shaded) accumlated precipitation (unit: mm)

    (a) from 0000 UTC 6 Jun to 0000 UTC 7 Jun in 2013, (b) from 0000 UTC 7 Jun to 0000 UTC 8 Jun in 2013, (c) from 0000 UTC 26 Jun to 0000 UTC 27 Jun in 2013, (d) from 0000 UTC 27 Jun to 0000 UTC 28 Jun in 2013

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    Fig. 5  The verification of 24 h simulated precipitation for case 1

    (a) ETS, (b) HSS, (c) BS

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    Fig. 6  24 h simulated (the contour) and obseverd (the shaded) accumlated precipitation (unit: mm)

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    Fig. 7  The verification of 24 h simulated precipitation for case 2

    (a) ETS, (b) HSS

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    Fig. 8  3 h obseverd and simulated accumlated precipitation and the difference between RADVAR-V and CTRL

    (a) observation, (b) simulated precipitation, (c) the difference of 850 hPa wind (the vector, unit:m/s) and divergence (the shaded), (d) the difference of precipitation

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    Fig. 9  The difference between RADVAR-R and CTRL

    (a)850 hPa wind (the vector, unit:m/s) and divergence (the shaded), (b) precipitation, (c)w(unit:10-1m/s), (d) rain water mixing ratio (unit:g/kg)

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    Fig. 10  The difference between RADVAR-VR and CTRL

    (a)850 hPa wind (the vector, unit:m/s) and divergence (the shaded), (b)w(unit: 10-1m/s), (c) rain water mixing ratio (unit: g/kg), (d) precipitation (the shaded) and 850 hPa w(the contour, unit:10-1m/s)

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    Table  1  The configuration of experiments

    试验名称控制试验 (CTRL)敏感性试验
    RADAR-VRADAR-RRADAR-VR
    常规资料有有有有
    雷达径向风无有无有
    雷达反射率因子无无有有
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    • Received : 2015-04-16
    • Accepted : 2015-06-25
    • Published : 2015-09-30
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