Vol.30, NO.5, 2019
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Article Navigation >  Journal of Applied Meteorological Science  > 2019  >  30(5): 629-640
Li Fangfang, Chen Qiying, Wu Hongkun. A statistical study of brunt-vaisala frequency with second-level radiosonde data in China. J Appl Meteor Sci, 2019, 30(5): 629-640. DOI:  10.11898/1001-7313.20190511.
Citation: Li Fangfang, Chen Qiying, Wu Hongkun. A statistical study of brunt-vaisala frequency with second-level radiosonde data in China. J Appl Meteor Sci, 2019, 30(5): 629-640. DOI:  10.11898/1001-7313.20190511.
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A Statistical Study of Brunt-vaisala Frequency with Second-level Radiosonde Data in China

DOI: 10.11898/1001-7313.20190511
  • 1.

    School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225

  • 2.

    National Meteorological Center, Beijing 100081

  • Received Date: 2018-12-12
  • Rev Recd Date: 2019-05-05
  • Publish Date: 2019-09-30
    Abstract
  • Based on the second-level sounding data of high vertical resolution in China from June 2014 to May 2017, time and space distribution characteristics of brunt-vaisala frequency in China are analyzed. Results show that the distribution of atmospheric brunt-vaisala frequency increases with height, data of lower stratosphere is larger than the troposphere, and the brunt-vaisala frequency remains constant in the vertical direction in the troposphere and low stratosphere. The brunt-vaisala frequency in the troposphere is greatly affected by the topography, and gradually increases from west to east with the change of longitude, with a small value area in the plateau region. The brunt-vaisala frequency in the low stratosphere is less affected by the topography and mainly changes with latitude, and it's greater in the southern region than that in the northern region. The brunt-vaisala frequency of the transition layer varies greatly with height. The southern part of lower transition layer changes faster with height than the northern part. The middle and southern parts of the upper transition layer change faster with height than the northern part. The brunt-vaisala frequency in the transition layer increases with latitude. The brunt-vaisala frequency doesn't change significantly with seasons at 5-10 km height and low stratosphere, but in the transition layer between troposphere and stratosphere (10-18 km), the seasonal change is significant. It changes most significantly in winter, less significantly in spring and autumn, and minimally in summer. Below 5 km, the seasonal variation is obvious, and it changes the most in winter. The brunt-vaisala frequency below 5 km in the northern region troposphere shows annually variation characteristics, and the peak value is in winter. The brunt-vaisala frequency doesn't change significantly with time in the stratosphere of north and south regions, and changes little with time in the troposphere in north and south regions. The brunt-vaisala frequency shows annually variation characteristics in the lower troposphere over the northern region, with peaks appearing in the winter, and there is also a one-year periodic variation in the transition layer, the peak is in winter and the valley is in summer. The brunt-vaisala frequency changes at the same height of the southern region and the northern region are similar in the transition layer. There is an annual change, the peak is in winter and the valley is in summer, but the central value of the transition layer in the southern region is smaller than the central value in the northern region. In the transition layer, the influence of the brunt-vaisala frequency with the height on the gravity-wave momentum flux is considered. The brunt-vaisala frequency and wind speed calculated by second-level sounding data are finely changed, and the change of atmospheric stability can be grasped more accurately than the conventional sounding.
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    References(25)

  • Fig. 1  The distribution of L-band sounding stations in China

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    Fig. 2  Comparison of two kinds of data at Beijing Station at 0000 UTC 1 Jan 2017

    (a)the comparison of two radiosonde data, (b)the comparison of second-level sounding data before and after interpolation

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    Fig. 3  Vertical profile of the average brunt-vaisala frequency in China from 2014 to 2017

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    Fig. 4  Zonal section of the brunt-vaisala frequency in China from 2014 to 2017(unit:10-4 s-2)

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    Fig. 5  Meridional section of brunt-vaisala frequency in China from 2014 to 2017(unit:10-4 s-2)

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    Fig. 6  Horizontal distribution of brunt-vaisala frequency in China from 2014 to 2017(unit:10-4 s-2)

    (a)troposphere, (b)low stratosphere

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    Fig. 7  Seasonal variation of brunt-vaisala frequency at tropospheric atmospheric(unit:10-4 s-2)

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    Fig. 8  Seasonal variation of brunt-vaisala frequency in low stratosphere(unit:10-4 s-2)

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    Fig. 9  Monthly average variation of atmospheric brunt-vaisala frequency in China from 2014 to 2017(unit:10-4 s-2)

    (a)the north region, (b)the south region

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    Fig. 10  Comparison of gravity flux fluctuations at different brunt-vaisala frequency in transition layer at Beijing Station at 0000 UTC 6 Jun 2014

    (a)momentum flux changes with brunt-vaisala frequency change, (b)brunt-vaisala frequency changes with height, (c)momentum flux changes with buoyancy frequency as constant, (d)brunt-vaisala frequency is a constant

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    Fig. 11  Comparison of gravity flux fluctuations at different brunt-vaisala frequency in troposphere layer at Beijing Station at 0000 UTC 6 Jun 2014

    (a)momentum flux changes with brunt-vaisala frequency change, (b)brunt-vaisala frequency changes with height, (c)momentum flux changes with buoyancy frequency as constant, (d)brunt-vaisala frequency is a constan

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    Fig. 12  The comparison of Ri for two types of data at Xuzhou Station

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    • Received : 2018-12-12
    • Accepted : 2019-05-05
    • Published : 2019-09-30
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