Impact of different radar data assimilation on a rare strong squall line simulation

doi:10.11755/j.issn.1006-7639（2022）-03-0469

Abstract

Abstract:

The weather research and forecasting (WRF) model along with its three-dimensional data assimilation (3D-Var) system was used to improve the initial field by assimilating radar reflectivity and radial velocity from Doppler radars. A rare strong squall line occurring on 4 March 2018 in Jiangxi Province was simulated and studied by using the adjusted initial field. It is found that only assimilation of rainwater, snow, graupel particles retrieved from radar reflectivity and water vapor derived from radar reflectivity could not make a stable improvement in forecasting the composite reflectivity, especially got an opposite effect on forecasting surface gale and precipitation. The assimilation of reflectivity data could significantly improve forecast skill when radar radial velocity was assimilated jointly. The reason is that assimilation of radar reflectivity can efficiently adjust initial hydrometeors and thermal field, but it has little effect on initial dynamic field. As the simulation time went by, the adjustment of dynamic field was unreasonable, a false divergence wind field appeared in the upper troposphere, thus a stratiform cloud area appeared in front of the squall line, which was not captured in reality. So the model could not improve the simulation of vertical wind shear and cold pool as well as rear inflow, and then there was a large gap between forecasted results and observations. Only assimilation of radar radial velocity could improve simulation results, and assimilation of both could efficiently adjust initial hydrometeors and thermal fields as well as dynamic field, and make the physical configuration more compatible with reality.The vertical wind shear and wind field structure were more favorable to occurrence of the strong squall line, and then formed a strong cold pool close to reality, further the forecasting results of radar composite reflectivity, surface wind and precipitation of the squall line agreed much better with observations compared with only assimilation of radar radial velocity.

Key words: rare squall line, thunderstorm gale, Doppler radar data, data assimilation

摘要：

选用WRF（weather research and forecasting）模式及其3D-Var（three-dimensional variation）同化系统,针对2018年3月4日发生在江西的一次罕见强飑线天气,探讨同化多普勒雷达不同观测资料对极端雷暴大风天气模拟预报的影响。结果表明：仅同化由雷达反射率反演的雨水、雪和霰粒子以及由其估算的水汽不能稳定改善模式对飑线雷达反射率的预报效果,尤其对地面大风和降水的预报起反效果;当联合同化雷达反射率与雷达径向风资料后,显著改进了模式对飑线发展演变过程中雷达反射率、地面降水和地面大风的预报效果,雷达反射率的同化呈现显著正效果。原因是仅同化雷达反射率对初始水成物及热力场影响较大,而对动力场调整微弱,随着积分时间增加,热力场对动力场的反馈作用不真实,高层出现虚假辐散风场,飑线前侧模拟出虚假层状云区,且未能改进飑线系统低层垂直风切变、冷池以及对流层中下层后侧入流的模拟,模拟的飑线移动和演变过程与实况有很大差距;当联合同化雷达反射率与雷达径向风资料后明显调整了初始动力、热力和水成物场,物理配置更符合实际,形成更有利于强飑线发生的垂直风切变和风场结构,产生与实况接近的强冷池,模拟结果与实况的吻合度明显高于仅同化雷达径向风资料的试验。

关键词: 罕见飑线, 雷暴大风, 多普勒雷达观测资料, 资料同化

CLC Number:

P435+.1

CAO Qian, LEI Guilian, YI Yanhong, ZHANG Yizhi, LIU Liangyu, PENG Wangminzi. Impact of different radar data assimilation on a rare strong squall line simulation[J]. Journal of Arid Meteorology, 2022, 40(3): 469-484.

曹倩, 雷桂莲, 易艳红, 章毅之, 刘良玉, 彭王敏子. 不同雷达观测资料同化对一次罕见飑线天气模拟的影响[J]. 干旱气象, 2022, 40(3): 469-484.

Figures/Tables 13

Tab.1 Experimental scheme

试验名称	试验简介
NODA	不同化雷达资料,将内外层积分得到的12：00预报结果直接当作初始场,向后积分5 h
DARF	以内外层积分得到的12：00预报场作为同化初始场,在12：00—13：00每隔6 min同化雷达反射率资料, 得到13：00分析场后,向后积分4 h
DAVE	同DARF试验,12：00—13：00每隔6 min同化雷达径向风资料
DARV	同DARF试验,12：00—13：00每隔6 min联合同化雷达反射率和径向风资料

Fig.1 Evolution of observed radar composite reflectivity （Unit：dBZ） on March 4, 2018 （a） 12：36 BST, （b） 13：12 BST, （c） 14：00 BST, （d） 15：00 BST

Fig.2 The distribution of gale greater than grade 8 observed and forecasted by different experiments in Jiangxi from 13：00 BST to 17：00 BST March 4, 2018（Unit：m·s-1）（a） observation, （b） NODA, （c） DARF, （d） DAVE, （e） DARV

Fig.3 Observed and forecasted radar composite reflectivity by different experiments on March 4, 2018 （Unit： dBZ）

Fig.4 The distribution of 3-hour accumulated rainfall observed and forecasted by different experiments from 13：00 BST to 16：00 BST March 4, 2018（Unit： mm）（a） observation, （b） NODA, （c） DARF, （d） DAVE, （e） DARV

Fig.5 The radar composite reflectivity （Unit： dBZ） of observation and initial analysis fields of different experiments at 13：00 BST March 4, 2018 （The solid blue lines are section lines perpendicular to the squall line）（a） observation, （b） NODA, （c） DARF, （d） DAVE, （e） DARV

Fig.6 Vertical cross sections of radar composite reflectivity （color shaded, Unit：dBZ） and graupel mixing ratio （black contours, Unit：g·kg-1） of observation and initial analysis fields of different experiments along the blue line in Fig.5 at 13：00 BST March 4, 2018 （The black dotted line is 0 ℃ isotherm. the same as bellow）（a） observation, （b） NODA, （c） DARF, （d） DAVE, （e） DARV

Fig.7 Vertical cross sections of rain water （black contours） and snow （color shaded） mixing ratio （Unit：g·kg-1） of initial analysis fields of different experiments along the blue line in Fig.5 at 13：00 BST March 4, 2018 （a）NODA,（b）DARF,（c）DAVE,（d）DARV

Fig.8 The radial velocity （Unit：m·s-1） on the elevation of 0.5º from Yichun Doppler radar and its simulations in initial analysis fields of different experiments at 13：00 BST March 4, 2018 （The black star is location of the radar station）（a） observation, （b） NODA, （c） DARF, （d） DAVE, （e） DARV

Fig.9 Vertical cross sections of pseudo-equivalent potential temperature （contours） and perturbation potential temperature（color shaded） along the blue line in Fig.5 in initial analysis fields of different experiments at 13：00 BST March 4, 2018 （Unit： K）（a）NODA,（b）DARF,（c）DAVE,（d）DARV

Fig.10 Observed and simulated radar reflectivity factor （color shaded, Unit：dBZ） and wind fields （vectors, Unit：m·s-1）simulated by NODA, wind field increment （vectors, Unit：m·s-1） simulated by DARF and DAVE and DARV compared with NODA at 15：00 BST 4 March, 2018

Fig.11 The hourly evolution of 0-3 km vertical wind shear vector (arrows) and vertical wind shear speed (color shaded, Unit: m·s-1) simulated by different experiments on March 4, 2018

Fig.12 Observed and simulated hourly surface variable temperature fields of different experiments on March 4, 2018 （Unit：℃）

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