Impact of Lightning Data Assimilation on Forecasting the"7.20"Extreme Heavy Rainfall in Zhengzhou, Henan Province
-
摘要: 研究了闪电资料同化对2021年7月20日河南郑州特大暴雨短临预报的影响。利用天气研究与预报（WRF）模式的三维变分（3DVAR）数据同化系统（WRFDA），开展了两组循环同化试验：（1）同化地面和探空常规观测资料（包括风速、风向、温度和气压）的试验（CONV）；（2）同化常规观测资料和由闪电资料反演的伪相对湿度的试验（LGDA），并与无资料同化的试验（NoDA）进行对比。结果表明，CONV的分析场和NoDA都未能模拟出强对流系统的回波结构，但由于LGDA增加了对整层大气的湿度场的调整，其分析场在闪电发生位置处的水凝物增量较大，相对湿度和反射率的分布情况与中国全球大气再分析资料（CRA）及雷达反射率观测值最接近。降水预报方面，LGDA显著提高了大暴雨雨带（6 h累积降水量≥50 mm）和强降水中心（6 h累积降水量≥200 mm）位置和强度的预报效果，对本次强降水过程的预报起到了积极作用，尤其是对前3 h的降水预报。Abstract: This study evaluates the impact of lightning data assimilation (DA) on short-term forecasts of an extremely heavy rainfall event that occurred in Zhengzhou, Henan Province on July 20, 2021. Based on the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) data assimilation system (WRFDA), two cycling DA experiments were designed: one test (CONV) assimilated ground and sounding observation data (i. e., wind speed, wind direction, temperature, and air pressure), and the other test (LGDA) assimilated both conventional observations and pseudo-relative humidity observations derived from lightning data. The results of assimilated test were compared with those of the test without data assimilation (NoDA). Neither the analysis fields of CONV nor NoDA were able to simulate the echo structure of a strong convective system. Since LGDA increased the adjustment of the humidity field, its analysis field had a large increase in hydrometeor at the location where lightning strikes and the distribution of relative humidity and reflectivity was closest to the China Global Atmosphere Reanalysis data and radar reflectivity observations. The predictive precipitation shows that the spatial coverage of the rainstorm belt (6-h accumulated precipitation≥50 mm), as well as the location and amount of heavy precipitation center (6-h accumulated precipitation≥200 mm), have been well predicted by LGDA, demonstrating the positive impact of lightning DA on this extremely heavy rainfall event, especially the precipitation forecast for the first three hours.
-
图 4 2021年7月20日06时再分析产品CRA（a）与各试验在700 hPa的相对湿度（b~d，单位：%）分布以及沿着34.71 °N（即图a~d中经过郑州站的黑线）方向的相对湿度垂直剖面图（e~h）
黑色三角表示郑州站位置（下同）。
图 5 2021年7月20日06时最大反射率（单位：dBZ）叠加850 hPa水平风场（单位：m·s-1）（a~d），以及沿34.71 °N（图a~d中黑线）的反射率剖面图（e~h）
-
[1] 童文雪, 李刚, 孙娟珍. 针对对流降水预报的BJ-RUC系统1小时更新循环方案研究[J]. 热带气象学报, 2018, 34(2): 177-187. [2] LILLY D K. Numerical prediction of thunderstorms—Has its time come?[J]. Quart J Roy Meteor Soc, 1990, 116(494): 779-798. [3] 孙娟珍, 陈明轩, 范水勇. 雷达资料同化方法: 回顾与前瞻[J]. 气象科技进展, 2016, 6(3): 17-27. [4] LIU C, CECIL D J, ZIPSER E J, et al. Relationships between lightning flash rates and radar reflectivity vertical structures in thunderstorms over the tropics and subtropics[J]. J Geophys Res, 2012, 117(D6): D06212. [5] 李进梁, 吴学珂, 袁铁, 等. 基于TRMM卫星多传感器资料揭示的亚洲季风区雷暴时空分布特征[J]. 地球物理学报, 2019, 62(11): 4 098-4 109. [6] 郄秀书, 刘冬霞, 孙竹玲. 闪电气象学研究进展[J]. 气象学报, 2014, 72(5): 1 054-1 068. [7] 何邓新, 赖安伟, 康兆萍, 等. 闪电资料在快速更新同化系统中的应用研究[J]. 气象, 2021, 47(8): 995-1 008. [8] 徐国强, 黄守友, 赵晨阳. FY-4A闪电资料在对流天气数值预报中的影响研究[J]. 气象, 2020, 46(9): 1 165-1 177. [9] CHEN Z, SUN J, QIE X, et al. A method to update model kinematic states by assimilating satellite‐observed total lightning data to improve convective analysis and forecasting[J]. J Geophys Res, 2020, 125(22): e2020J-e33330J. [10] GAN R, YANG Y, QIU X, et al. Assimilation of the maximum vertical velocity converted from total lightning data through the EnSRF method[J]. J Geophys Res, 2021, 126(9): e2020J-e34300J. [11] FIERRO A O, MANSELL E R, ZIEGLER C L, et al. Application of a lightning data assimilation technique in the WRF-ARW model at cloud-resolving scales for the tornado outbreak of 24 May 2011[J]. Mon Wea Rev, 2012, 140(8): 2 609-2 627. [12] QIE X, ZHU R, YUAN T, et al. Application of total-lightning data assimilation in a mesoscale convective system based on the WRF model[J]. Atmos res, 2014, 145: 255-266. [13] FIERRO A O, GAO J, ZIEGLER C L, et al. Assimilation of flash extent data in the variational framework at convection-allowing scales: Proof-of-concept and evaluation for the short-term forecast of the 24 May 2011 tornado outbreak[J]. Mon Wea Rev, 2016, 144(11): 4 373- 4 393. [14] LIU P, YANG Y, GAO J, et al. An approach for assimilating FY4 lightning and cloud top height data using 3DVAR[J]. Frontiers in Earth Science, 2020: 288. [15] HU J, FIERRO A O, WANG Y, et al. Exploring the assimilation of GLM-derived water vapor mass in a cycled 3DVAR framework for the short-term forecasts of high-impact convective events[J]. Mon Wea Rev, 2020, 148(3): 1 005-1 028. [16] LIU P, YANG Y, LAI A, et al. Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method[J]. Remote Sensing, 2021, 13(16): 3 090. [17] 梁旭东, 夏茹娣, 宝兴华, 等. 2021年7月河南极端暴雨过程概况及多尺度特征初探[J]. 科学通报, 2022, 67(10): 997-1 011. [18] XU L, CHENG W, DENG Z, et al. Assimilation of the FY-4A AGRI Clear-Sky Radiance Data in a Regional Numerical Model and Its Impact on the Forecast of the"21·7"Henan Extremely Persistent Heavy Rainfall[J]. Adv Atmos Sci, 2023, 40: 920-936. [19] 史文茹, 李昕, 曾明剑, 等". 7· 20"郑州特大暴雨的多模式对比及高分辨率区域模式预报分析[J]. 大气科学学报, 2021, 44(5): 688-702. [20] FIERRO A O, CLARK A J, MANSELL E R, et al. Impact of storm-scale lightning data assimilation on WRF-ARW precipitation forecasts during the 2013 warm season over the contiguous United States[J]. Mon Wea Rev, 2015, 143(3): 757-777. [21] LORENC A C. Analysis methods for numerical weather prediction[J]. Quart J Roy Meteor Soc, 1986, 112(474): 1 177-1 194. [22] PARRISH D F, DERBER J C. The National Meteorological Center's spectral statistical-interpolation analysis system[J]. Mon Wea Rev, 1992, 120(8): 1 747-1 763. [23] SUN J, WANG H, TONG W, et al. Comparison of the Impacts of Momentum Control Variables on High-Resolution Variational Data Assimilation and Precipitation Forecasting[J]. Mon Wea Rev, 2016, 144(1): 149-169. [24] WANG C, CHEN Y, CHEN M, et al. Data assimilation of a dense wind profiler network and its impact on convective forecasting[J]. Atmos Res, 2020, 238: 104880. [25] HU J, FIERRO A O, WANG Y, et al. Assessment of Storm‐Scale Real Time Assimilation of GOES‐16 GLM Lightning‐Derived Water Vapor Mass on Short Term Precipitation Forecasts During the 2020 Spring Forecast Experiment[J]. J Geophys Res, 2021, 126(21): e2021J-e34603J. -
下载:
点击查看大图
图(8)
计量
- 文章访问数: 118
- HTML全文浏览量: 33
- PDF下载量: 13
- 被引次数: 0
粤公网安备 4401069904700003号