Vol.33, NO.5, 2022
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Article Navigation >  Journal of Applied Meteorological Science  > 2022  >  33(5): 568-580
Lin Xiaomeng, Wei Yinghua, Zhang Nan, et al. Construction of air-sounding-profile system based on foundation-remote-sensing equipment. J Appl Meteor Sci, 2022, 33(5): 568-580. DOI:  10.11898/1001-7313.20220505.
Citation: Lin Xiaomeng, Wei Yinghua, Zhang Nan, et al. Construction of air-sounding-profile system based on foundation-remote-sensing equipment. J Appl Meteor Sci, 2022, 33(5): 568-580. DOI:  10.11898/1001-7313.20220505.
shu

Construction of Air-sounding-profile System Based on Foundation-remote-sensing Equipment

DOI: 10.11898/1001-7313.20220505

  • Tianjin Meteorological Observatory, Tianjin 300074

  • Received Date: 2022-05-05
  • Rev Recd Date: 2022-07-15
  • Publish Date: 2022-09-15
    Abstract
  • Radiosonde data are indispensable for severe convective weather forecast because they can not only reflect the temperature and humidity structure and dynamic characteristics of local atmosphere, but also indicate the favorable conditions of convection initiation and development. However, the conventional radiosonde cannot capture local environmental parameters instantly due to insufficient space layout and low detection frequency, and the drift of sounding balloon worsens the representativeness of data. Acquiring accurate high-resolution data of temperature, humidity and horizontal wind is in great need. Combining ground-based remote sensing data and automatic weather station (AWS) data, including data of wind profile radar and AWS at Xiqing Station and radiometer at Tieta Station, a Foundation-remote-sensing Air-sounding-profile System (FAS) is constructed based on the strength of specific humidity profile inversion method and WPR-HW method. The inversion results of FAS is verified using European Centre for Mudium-Range Weather Forecasts reanalysis (ERA5) through 10 severe convective weather cases.The comparison shows that the inversion data of specific humidity and convective available potential energy (CAPE) from FAS has good consistency with ERA5 data. The correlation coefficient is 0.93, the root mean square error is 1.4 g·kg-1, the mean absolute deviation is 1.06 g·kg-1, and the mean relative deviation is 11.22% for 130 groups of specific humidity between FAS and ERA5 data. The correlation coefficient is 0.84, and the mean relative deviation is 35% for CAPE in 65 groups. The comparison test verifies the credibility of FAS data (correlation coefficients of specific humidity and CAPE both passing the test of 0.01 level). The time resolution of FAS is much higher. The mean interval time between Beijing radiosonde's occurrence time and severe convection's occurrence time is 7 h 18 min, while FAS's time resolution is just a few minutes. As a result, the mean value of CAPE detected by Beijing radiosonde is 322 J·kg-1, which is too low to indicate local convective potential in the afternoon or evening, while the mean value by FAS is 1451.88 J·kg-1, which can describe the change of atmospheric state during severe convection. FAS has high practical value in short-term forecast, as it can be used to determine the presence of convective potential and distinguish severe convective weather types timely through the configuration of convective parameters. The FAS can also capture elaborate thermodynamic structure of meso-scale and micro-scale weather system before the occurrence of severe convection. The FAS can significantly improve the lead time and accuracy of forecast.
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  • Fig. 1  Comparison of specific humidity processed from Foundation-remote-sensing Air-sounding-profile System and ERA5 data

    (a)the scatter diagram of humidity, (b)the mean of humidity varing with height, (c)the root mean square error of humidity varing with height, (d)the mean relative deviation of humidity varing with height

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    Fig. 2  Comparison of convective available potential energy processed from Foundation-remote-sensing Air-sounding-profile System and ERA5 data

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    Fig. 3  T-lnp on 1 Aug 2020

    (the yellow shaded denotes convective available potential energy)

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    Fig. 4  Retrieved products of ground-based remote sensing data at Xiqing Station of Tianjin and precipitation observed by automatic weather station on 1 Aug 2020

    (a)convective available potential energy and precipitation from 1430 BT to 2030 BT, (b)time-altitude section of horizontal wind field from 1630 BT to 2030 BT (the shaded denotes velocity)

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    Fig. 5  T-lnp on 26 May 2021

    (the yellow shaded denotes convective available potential energy)

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    Fig. 6  Retrieved products of ground-based remote sensing data and observed maximum wind speed at Xiqing Station of Tianjin on 26 May 2021

    (a)convective available potential energy and maximum wind speed from 1000 BT to 1600 BT, (b)time-altitude section of horizontal wind field from 1200 BT to 1600 BT (the shaded denotes velocity)

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    Table  1  Comparison of 10 convective cases from Beijing radiosonde and Tianjin Foundation-remote-sensing Air-sounding-profile System

    强对流实况 北京探空 天津遥感探空
    日期 时间 间隔 对流有效位能/(J·kg-1) 整层比湿积分/(g·hPa·kg-1) 间隔 对流有效位能/(J·kg-1) 整层比湿积分/(g·hPa·kg-1)
    2020-05-25 17:26 9 h 26 min 0.0 1910.0 小于15 min 3007.0 2318.8
    2020-06-01 16:00 8 h 2.7 1197.1 2268.5 1054.6
    2020-06-25 16:15 8 h 15 min 271.2 3230.7 1118.3 3267.3
    2020-07-05 16:29 15 min 216.9 3631.1 1345.8 5185.6
    2020-07-13 18:30 10 h 30 min 304.2 4369.9 856.4 4008.9
    2020-08-01 19:25 11 h 25 min 451.7 4293.7 1464.4 5728.8
    2020-08-12 13:25 5 h 25 min 561.7 4727.1 1501.9 6147.3
    2020-09-23 06:01 8 h 1 min 515.6 2533.6 842.2 3645.5
    2021-05-26 15:20 7 h 20 min 0.0 1511.3 1341.7 1248.3
    2021-07-19 12:25 4 h 25 min 446.4 4705.2 772.6 6098.4
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    Table  2  Comparison of convective parameters processed from Beijing radiosonde at 0800 BT and Tianjin Remote-Atmospheric Profile System at 1400 BT on 1 Aug 2020

    对流参数 08：00北京探空 14：00天津遥感探空廓线
    对流有效位能/(J·kg-1) 451.7 1455.4
    对流抑制能量/(J·kg-1) 297.0 5.1
    K指数 37 39
    抬升指数/℃ -1.73 -4.37
    自由对流高度/hPa 554.9 943.2
    整层比湿积分/(g·hPa·kg-1) 4293.7 5618.5
    DownLoad: Download CSV
  • [1]
    Wei D, Sun J S, Lei L, et al. Comparative analysis of three kinds of sounding data in the application of the severe convective weather. Meteor Mon, 2011, 37(4): 412-422. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201104006.htm
    [2]
    Ye D Z. Application of radiosonde data. Meteor Mon, 1977, 3(11): 5-7. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX197711003.htm
    [3]
    Lei L, Sun J S, Wei D, et al. Distinguishing the category of the summer convective weather by sounding data in Beijing. Meteor Mon, 2011, 37(2): 136-141. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201102003.htm
    [4]
    Wang X F, Ding Y H. Study on method of short-range forecast of severe convective weather in Beijing Area. Chinese J Atmos Sci, 1994, 18(2): 173-183. doi:  10.3878/j.issn.1006-9895.1994.02.05
    [5]
    Chen M, Fan S Y, Zheng Z F, et al. The performance of the proximity sounding based on the BJ-RUC system and its preliminary implementation in the convective potential forecast. Acta Meteor Sinica, 2011, 69(1): 181-194. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201101016.htm
    [6]
    Tian S C, Du C X. Effect of merging of the convective cloud clusters on occurrence of heavy rainfall. Chinese J Atmos Sci, 1983, 7(1): 70-79. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK198301007.htm
    [7]
    Liao X N, Yu X D, Yu B. Analysis on infrequent big hail event in Beijing Area. Meteor Mon, 2008, 34(2): 12-19. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200802003.htm
    [8]
    Ye A F, Wu Z F, Xiao W J, et al. A study of application of CAPE in severe convective weather prediction. J Appl Trop Meteor, 2006, 22(5): 70-76. https://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200605011.htm
    [9]
    Han F, Yang L, Zhou C X, et al. An experimental study of the short-time heavy rainfall event forecast based on ensemble learning and sounding data. J Appl Meteor Sci, 2021, 32(2): 188-199. doi:  10.11898/1001-7313.20210205
    [10]
    Zeng M J, Wang G C, Wu H Y, et al. Study of the forecasting method for the classified severe convection weather based on a meso-scale numerical model. Acta Meteor Sinica, 2015, 73(5): 868-882. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201505005.htm
    [11]
    Wei D, Sun J S, Lei L, et al. Reliability analysis of quantitative applications of sounding constructed from data detected by microwave radiometer and wind profiler. Climatic and Environmental Research, 2011, 16(6): 697-706. doi:  10.3878/j.issn.1006-9585.2011.06.03
    [12]
    Liu H Y, Xue J S, Shen T L, et al. Study on sounding balloon drifting and its impact on numerical predictions. J Appl Meteor Sci, 2005, 16(4): 518-526. doi:  10.3969/j.issn.1001-7313.2005.04.013
    [13]
    Zhang X F, Wang Z C, Mao J J, et al. Experiments on improving temperature and humidity profile retrieval for ground-based microwave radiometer. J Appl Meteor Sci, 2020, 31(4): 385-396. doi:  10.11898/1001-7313.20200401
    [14]
    Wei Y H, Wang Y C, Zhu L L, et al. Confidence and characteristics of short-duration heavy rainfall environmental parameters based on merging data in Tianjin. Desert Oasis Meteor, 2020, 14(5): 84-91. https://www.cnki.com.cn/Article/CJFDTOTAL-XJQX202005011.htm
    [15]
    Tian F Y, Zheng Y G, Zhang T, et al. Sensitivity analysis of short-duration heavy rainfall related diagnostic parameters with point-area verification. J Appl Meteor Sci, 2015, 26(4): 385-396. doi:  10.11898/1001-7313.20150401
    [16]
    Kong F C, Li J B, Wang Y. Analysis on atmospheric profiles retrieved by microwave radiometer at genting venue of Beijing Olympic Winter Games. Meteor Mon, 2021, 47(9): 1062-1072. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202109003.htm
    [17]
    Chen S C, Li X B, Cui M, et al. Error analysis of detection data of microwave radiometer and wind profiler radar under different weather conditions. J Meteor Environ, 2021, 31(1): 67-72. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX202101009.htm
    [18]
    Xu G R, Cui C G, Zhou Z M, et al. Atmospheric boundary layer heights estimated from radiosonde observations in the Qinghai-Tibet Plateau and its downstream areas. Torrential Rain Disaster, 2014, 33(3): 217-227. doi:  10.3969/j.issn.1004-9045.2014.03.004
    [19]
    Wang Z C, Zhang X F, Mao J J, et al. Comparison analysis on detection performance of ground-based microwave radiometers under different weather conditions. J Appl Meteor Sci, 2018, 29(3): 282-295. doi:  10.11898/1001-7313.20180303
    [20]
    Westwater E R, Han Y, Irisov V G, et al. Remote sensing of boundary layer temperature profiles by a scanning 5-mm microwave radiometer and RASS: Comparison experiments. J Atmos Oceanic Technol, 1999, 16(7): 805-818. doi:  10.1175/1520-0426(1999)016<0805:RSOBLT>2.0.CO;2
    [21]
    Lin X M, Wei Y H, Chen H, et al. The effect assessment of wind field inversion based on WPR in precipitation. J Appl Meteor Sci, 2020, 31(3): 361-372. doi:  10.11898/1001-7313.20160307
    [22]
    He P. Phased Array Wind Profile Rader. Beijing: China Meteorological Press, 2020.
    [23]
    Stanley G B, Barry E S, Eduard J S, et al. The value of wind profiler data in US weather forecasting. Bull Amer Meteor Soc, 2004, 85(12): 1871-1886. doi:  10.1175/BAMS-85-12-1871
    [24]
    He P. Foundation of Power Spectrum Estimation. Beijing: China Meteorological Press, 2015.
    [25]
    Dong L P, Wu L, Wang L, et al. Preliminary comparison research of the wind profile radar network data. Meteor Mon, 2014, 40(9): 1145-1151. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201409013.htm
    [26]
    Li F, Ruan Z, Wang H Y, et al. A calibration method of wind profile radar echo intensity with Doppler velocity spectrum. J Appl Meteor Sci, 2021, 32(3): 315-331. doi:  10.11898/1001-7313.20210305
    [27]
    Furumoto J, Iwai S, Fujii H, et al. Estimation of humidity profiles with the L-band boundary layer radar-RASS measurement. J Meteor Soc Japan, 2005, 83(5): 895-908. doi:  10.2151/jmsj.83.895
    [28]
    Furumoto J, Imura S, Tsuda T, et al. The variational assimilation method for the retrieval of humidity profiles with the wind-profiling radar. Atmos Oceanic Technol, 2007, 24(9): 1525-1545. doi:  10.1175/JTECH2074.1
    [29]
    Bianco L, Cimini D, Marzano F S, et al. Combining microwave radiometer and wind profiler radar measure for high-resolution atmospheric humidity profiling. Atmos Oceanic Technol, 2005, 22(7): 949-965. doi:  10.1175/JTECH1771.1
    [30]
    Stankov B B, Gossard E E, Weber B L, et al. Humidity gradient profiles from wind profiling radars using the NOAA/ETL advanced Signal Processing System(SPS). Atmos Oceanic Technol, 2003, 20(1): 3-22. doi:  10.1175/1520-0426(2003)020<0003:HGPFWP>2.0.CO;2
    [31]
    Imura S, Furumoto J, Tsuda T, et al. Estimation of humidity profiles by combining co-locate VHF and UHF wind-profiling radar observation. J Meteor Soc Japan, 2007, 85(3): 301-319. doi:  10.2151/jmsj.85.301
    [32]
    Clayson C A, Lakshmi K. On turbulence and mixing in the free at mosphere inferred from high-resolution soundings. Atmos Oceanic Technol, 2008, 25: 833-852. doi:  10.1175/2007JTECHA992.1
    [33]
    Sun K Y, Ruan Z, Wei M, et al. Preliminary estimation of specific humidity profiles with wind profile radar. J Appl Meteor Sci, 2013, 24(4): 407-415. doi:  10.3969/j.issn.1001-7313.2013.04.003
    [34]
    Cao Y J, Liu J M, Liang H, et al. Water vapor resource vertical stratification characteristics retrieved basing on the radiosonde datum. J Nat Res, 2011, 26(9): 1603-1612. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZX201109016.htm
    [35]
    Zhu G H, Zhang C Y, Weng N Q, et al. Retrieve the atmospheric humidity profile from wind profile radar data. J Atmosph Environ Opt, 2011, 6(5): 337-341. https://www.cnki.com.cn/Article/CJFDTOTAL-GDJY201105005.htm
    [36]
    Zhang Q C, Gong D L, Feng J J. Analysis and evaluation of retrieval products of RPG-HATPRO-G3 ground-based microwave radiometers. J Marine Meteor, 2017, 37(1): 104-110. https://www.cnki.com.cn/Article/CJFDTOTAL-SDQX201701012.htm
    [37]
    Tang R M, Cheng Y Y, Ye J Y. The comparison of water vapor content retrieved by radiosonde, ground station and satellite data. J Meteor Sci, 2010, 30(3): 373-377. doi:  10.3969/j.issn.1009-0827.2010.03.013
    [38]
    Ruan Z, Gao Z Y, Li F, et al. Integration and application of wind profiles for wind-profiling radar and weather radar. Meteor Mon, 2017, 43(10): 1213-1223. doi:  10.7519/j.issn.10000526.2017.10.005
    [39]
    Lin X M, He P, Huang X Y. A method to suppress the precipitation interference on horizontal wind of wind profile radar. J Appl Meteor Sci, 2015, 26(1): 66-75. doi:  10.11898/1001-7313.20150107
    [40]
    Dong J Y, Cui Y, Ruan Z, et al. Retrieval and experiments of atmospheric vertical motions in convective precipitation clouds. J Appl Meteor Sci, 2022, 33(2): 167-179. doi:  10.11898/1001-7313.20220204
    [41]
    Yao W, Ma Y, Gao L N. Comparison of relative humidity data between L-band and 59-701 sounding system. J Appl Meteor Sci, 2017, 28(2): 218-226. doi:  10.11898/1001-7313.20170209
    [42]
    Ma R Y, Sun J H, Yang X L. An eight-year climatology of the warm-season severe thunderstorm environments over North China. Atmos Res, 2021, 254: 1-13.
    [43]
    Zhang N, Wang Y, Lin X M. Mesoscale observational analysis of isolated convection associated with the interaction of the sea breeze front and the gust front in the context of the urban heat humid island effect. Atmosphere, 2022, 603: 1-15.
    [44]
    Zhang N, Wang Y. Mechanisms for the isolated convections triggered by the sea breeze front and the urban heat island. Meteor Atmos Phys, 2021, 133: 1143-1157. doi:  10.1007/s00703-021-00800-6
    [45]
    Meng X G, Guo J J, Han Y Q. Preliminarily assessment of ERA5 reanalysis data. J Marine Meteor, 2018, 38(1): 91-99. https://www.cnki.com.cn/Article/CJFDTOTAL-SDQX201801011.htm
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    • Received : 2022-05-05
    • Accepted : 2022-07-15
    • Published : 2022-09-15
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