Vol.26, NO.1, 2015
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Article Navigation >  Journal of Applied Meteorological Science  > 2015  >  26(1): 45-56
Gao Yudong, Wan Qilin, Xue Jishan, et al. Effects of assimilating radar rainfall rate estimation on torrential rain forecast. J Appl Meteor Sci, 2015, 26(1): 45-56. DOI:  10.11898/1001-7313.20150105.
Citation: Gao Yudong, Wan Qilin, Xue Jishan, et al. Effects of assimilating radar rainfall rate estimation on torrential rain forecast. J Appl Meteor Sci, 2015, 26(1): 45-56. DOI:  10.11898/1001-7313.20150105.
shu

Effects of Assimilating Radar Rainfall Rate Estimation on Torrential Rain Forecast

DOI: 10.11898/1001-7313.20150105
  • 1.

    Key Laboratory of Regional Numerical Weather Prediction, Guangzhou 510080

  • 2.

    Institute of Tropical and Marine Meteorology, CMA, Guangzhou 510080

  • 3.

    Chinese Academy of Meteorological Sciences, Beijing 100081

  • Received Date: 2014-05-06
  • Rev Recd Date: 2014-10-13
  • Publish Date: 2015-01-31
    Abstract
  • Meso-scale weather system, such as torrential rain, is neither easily detected nor effectively simulated. Main causes consist of the insufficient observation and the inaccurate initial filed, which are prepared for the routine weather prediction and the hazardous weather simulation. To solve these problems, high resolution rainfall rate data estimated by doppler radar Z-I relationship is calibrated with AWS data by variational method. The forecast experiment on a torrential rain case captured by the radar in Guangzhou indicates that the east center of precipitation omitted in the original Z-I estimation is forecasted after the calibration. Even though the minor amount of rainfall rate is inclined to be overestimated, relative errors of calibration significantly decline as the increase of rain rate value. As a result, high resolution datasets of calibration rain rate are demonstrated to possess a more accurate single point value than the estimation of Z-I relationship and a more reasonable gradient than AWS data. Meanwhile, according to the distribution of instantaneous precipitation, calibration rainfall rate datasets imply lots of information on the atmospheric dynamic and moisture, which are the major factors to arouse a convective rainstorm.To verify various advantages of mixed characteristics, a set of experiments are performed using FSU (Florida State University) cumulus parameterization scheme as the observational operator, based on GRAPES (Global/Regional Analysis and Prediction System) Regional Three Dimensional Variation System. Compared with NCEP (National Centers for Environmental Prediction) global analysis data, the convergence in lower level and the divergence in higher level after assimilation are conspicuously strengthened, which sequentially lead the unstable energy in atmosphere to be elevated. Showalter index and K index diagnose indicate a heavy rain in the dense data region as well. In addition, the vertical transportation of moisture forced by the convergence sustains a strong convection and ameliorates the cumulative precipitation. The storm path prediction is obviously improved. Results of simulation experiment express that not only the hourly distribution and center of precipitation are similar to the observation, also, the meso-scale convective system development and demise are impressively depicted.
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    References(27)

  • Fig. 1  Guangzhou radar estimations of rainfall rate (the shaded) and automatic weather station rainfall rate (number)(unit:m·h-1) at 1000 UTC 28 May 2009(a) before variational calibration, (b) after variational calibration

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    Fig. 2  Relative errors between radar estimation of rainfall rate and automatic weather station rainfall rate at 1000 UTC 28 Mar 2009 (a) before variational calibration, (b) after variational calibration

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    Fig. 3  Disturbance of linear observational operator with respect to specific humidity and vertical velocity

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    Fig. 4  The distribution of specific humidity (the shaded), divergence (contour, unit:10-5s-1) and horizontal wind (vector) before and after assimilation

    (a)850 hPa horizontal distribution of background wind and specific humidity, (b)850 hPa horizontal distribution of analysis wind and specific humidity, (c)850 hPa horizontal distribution of increment divergence and specific humidity, (d) vertical distribution of increment divergence and specific humidity along 23°N

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    Fig. 5  The distribution of moisture advection (the shaded, unit: 10-6 m·s-2), moisture flux divergence (the shaded, unit: 10-6 g·cm-2·hPa·s) and flow field (streamline) at 850 hPa before and after assimilation

    (a) moisture advection of ExpC, (b) moisture advection of ExpA, (c) moisture flux divergence and flow field of ExpC, (d) moisture flux divergence and flow field of ExpA

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    Fig. 6  Showalter index and K index

    (a) Showalter index of ExpC, (b) Showalter index of ExpA, (c)K index of ExpC, (d)K index of ExpA

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    Fig. 7  Precipitation from 1000 UTC to 1500 UTC on 28 Mar 2009(unit:mm)

    (a) ExpC, (b) ExpA, (c) observation, (d) hourly maximum precipitation

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    Fig. 8  The distribution of hourly rainfall at surface

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    • Received : 2014-05-06
    • Accepted : 2014-10-13
    • Published : 2015-01-31
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