基于卫星和雷达的融合雷达反射率方法研究及其在台风“烟花”观测中的应用-海洋预报

基于卫星和雷达的融合雷达反射率方法研究及其在台风“烟花”观测中的应用

单位：江苏省气象台, 江苏南京 210008

分类号：P714⁺.2

出版年·卷·期（页码）：2022·39·第五期（84-93）

摘要：

发展了一种融合葵花8号气象卫星和S波段双偏振天气雷达数据的雷达反射率融合方法，并将其应用在2021年7月超强台风“烟花”的观测分析中。研究结果表明：基于模糊逻辑分类器的质量控制方法能够有效剔除江苏省S波段双偏振天气雷达中存在的非气象杂波。卫星反演得到的雷达反射率可以很好地反映台风“烟花”登陆前后的云系特征和风眼位置，反射率高值区分别位于风眼周围及其北侧的外围螺旋雨带处，与风场高值区存在很好的对应关系。卫星和雷达的融合反射率结果在陆地上与雷达结果更相近，较纯卫星反演结果能更准确地描述陆地的降水云系，且在海陆交界处没有明显的不连续边界出现。卫星和雷达的融合反射率结果还能够较好地描述27日台风“烟花”登陆减弱（云顶降低）时以及28日台风引发飑线的降水云系的特征。

This study develops a blended method for radar reflectivity based on Himawari-8 meteorological satellite and S-band dual-polarization weather radar data, and further applies it to the observation and analysis of super typhoon "In-fa" in 2021. The results show that the quality control method based on fuzzy logic based classifier could effectively eliminate the non-meteorological echo of S-band dual-polarization radar in Jiangsu. The radar reflectivity retrieved from satellite inversion could well reflect the characteristics of cloud structure and the position of wind eye before and after the landfall of typhoon "In-fa". The high values areas of reflectivity are located around the wind eye as well as the spiral rain belt to the north of it, which corresponds well with the high vale areas of wind fields. The blended reflectivity results of satellite and radar are more similar to the radar results on land and could more accurately describe the precipitation cloud system on land based on the satellite inversion results. Meanwhile, the blended reflectivity shows no obvious discontinuity boundary at the junction of land and sea. Furthermore, the blended reflectivity results of satellite and radar could also better characterize the precipitation cloud system when the landfall of typhoon "In-fa" weakens (cloud top lowering down) on the 27th and typhoon "In-fa" induces squall line on the 28th.

参考文献：

[1] 李延江, 陈小雷, 景华, 等.渤海强对流天气监测及概念模型初建[J].海洋预报, 2013, 30(4):45-56. LI Y J, CHEN X L, JING H, et al. The monitoring for the severe convective weather and the preliminary building of its conceptual model in the Bohai Sea[J]. Marine Forecasts, 2013, 30(4):45-56.
[2] 孙虎林, 黄焕卿, 于庆龙, 等. 2012-2017年珠江口海区短时强对流天气灾害的统计分析[J].海洋预报, 2019, 36(4):35-43. SUN H L, HUANG H Q, YU Q L, et al. Statistical analysis on the short-term convective weather disasters in the Pearl River Estuary from 2012 to 2017[J]. Marine Forecasts, 2019, 36(4):35-43.
[3] 李湘, 张腾飞, 胡娟, 等.云南冰雹灾害的多普勒雷达特征统计及预警指标[J].灾害学, 2015, 30(3):88-93, 98. LI X, ZHANG T F, HU J, et al. Doppler radar statistical characteristics and early warning index of Yunnan hail disaster[J]. Journal of Catastrophology, 2015, 30(3):88-93, 98.
[4] 周聪, 闵锦忠, 戚友存, 等.基于线性规划方法处理C波段双线偏振多普勒天气雷达差分相位φ_C数据[J].气象科学, 2019, 39(5):617-625. ZHOU C, MIN J Z, QI Y C, et al. Processing differential phase data from C-band dual-polarimetric Doppler weather radar on the basis of the linear programming[J]. Journal of the Meteorological Sciences, 2019, 39(5):617-625.
[5] BRINGI V N, CHANDRASEKAR V. Polarimetric Doppler weather radar:principles and applications[M]. New York:Cambridge University Press, 2001.
[6] KUMJIAN M R. Principles and applications of dual-polarization weather radar. Part I:description of the polarimetric radar variables[J]. Journal of Operational Meteorology, 2013, 1(19):226-242.
[7] PARK H S, RYZHKOV A V, ZRNIĆ D S, et al. The hydrometeor classification algorithm for the polarimetric WSR-88D:description and application to an MCS[J]. Weather and Forecasting, 2009, 24(3):730-748.
[8] ZHANG S, HUANG X Y, MIN J Z, et al. Improved fuzzy logic method to distinguish between meteorological and nonmeteorological echoes using C-band polarimetric radar data[J]. Atmospheric Measurement Techniques, 2020, 13(2):537-551.
[9] TANG L, ZHANG J, LANGSTON C, et al. A physically based precipitation-nonprecipitation radar echo classifier using polarimetric and environmental data in a real-time national system[J]. Weather and Forecasting, 2014, 29(5):1106-1119.
[10] TANG L, ZHANG J, SIMPSON M, et al. Updates on the radar data quality control in the MRMS quantitative precipitation estimation system[J]. Journal of Atmospheric and Oceanic Technology, 2020, 37(9):1521-1537.
[11] KRAUSE J M. A simple algorithm to discriminate between meteorological and nonmeteorological radar echoes[J]. Journal of Atmospheric and Oceanic Technology, 2016, 33(9):1875-1885.
[12] YANG J, ZHANG Z Q, WEI C Y, et al. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4[J]. Bulletin of the American Meteorological Society, 2017, 98(8):1637-1658.
[13] ZHUGE X Y, ZOU X L. Summertime convective initiation nowcasting over southeastern China based on Advanced Himawari Imager observations[J]. Journal of the Meteorological Society of Japan, 2018, 96(4):337-353.
[14] 黄亦鹏, 李万彪, 赵玉春, 等.基于雷达与卫星的对流触发观测研究和临近预报技术进展[J].地球科学进展, 2019, 34(12):1273-1287. HUANG Y P, LI W B, ZHAO Y C, et al. A review of radar-and satellite-based observational studies and nowcasting techniques on convection initiation[J]. Advances in Earth Science, 2019, 34(12):1273-1287.
[15] RUTLEDGE S A, HILBURN K A, CLAYTON A, et al. Evaluating geostationary lightning mapper flash rates within intense convective storms[J]. Journal of Geophysical Research:Atmospheres, 2020, 125(14):e2020JD032827.
[16] HILBURN K A, EBERT-UPHOFF I, MILLER S D. Development and interpretation of a neural-network-based synthetic radar reflectivity estimator using GOES-R satellite observations[J]. Journal of Applied Meteorology and Climatology, 2021, 60(1):3-21.
[17] DUAN M S, XIA J J, YAN Z W, et al. Reconstruction of the radar reflectivity of convective storms based on deep learning and himawari-8 observations[J]. Remote Sensing, 2021, 13(16):3330.
[18] WILLOUGHBY H E. The dynamics of the tropical cyclone core[J]. Australian Meteorological Magazine, 1988, 36:183-191.

服务与反馈：

【文章下载】【发表评论】【查看评论】【加入收藏】