1.中山大学大气科学学院 / 广东省气候变化与自然灾害研究重点实验室,广东 珠海 519082
2.南方海洋科学与工程广东省实验室(珠海),广东 珠海 519082
3.中国科学院西北生态环境资源研究院甘肃省遥感重点实验室,甘肃 兰州 730000
4.中国科学院西北生态环境资源研究院寒旱区陆面过程与气候变化重点实验室,甘肃 兰州 730000
5.中国科学院西北生态环境资源研究院那曲高寒气候环境观测研究站, 西藏 那曲 852000
6.中国航空工业集团雷华电子技术研究所,江苏 无锡 214063
庞盈(1997年生),女;研究方向:多源观测资料同化;E-mail:pangy6@mail2.sysu.edu.cn
陈生(1979年生),男;研究方向:多源观测资料同化、强对流天气短临预报;E-mail:chensheng@nieer.ac.cn
纸质出版日期:2023-05-25,
网络出版日期:2023-02-27,
收稿日期:2022-07-18,
录用日期:2022-09-14
扫 描 看 全 文
庞盈,陈生,胡俊俊等.不同控制变量方案对广州暴雨过程雷达资料同化和预报的影响[J].中山大学学报(自然科学版),2023,62(03):35-46.
PANG Ying,CHEN Sheng,HU Junjun,et al.Effects of different control variable schemes on radar data assimilation and forecast of a rainfall event in Guangzhou[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(03):35-46.
庞盈,陈生,胡俊俊等.不同控制变量方案对广州暴雨过程雷达资料同化和预报的影响[J].中山大学学报(自然科学版),2023,62(03):35-46. DOI: 10.13471/j.cnki.acta.snus.2022D049.
PANG Ying,CHEN Sheng,HU Junjun,et al.Effects of different control variable schemes on radar data assimilation and forecast of a rainfall event in Guangzhou[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2023,62(03):35-46. DOI: 10.13471/j.cnki.acta.snus.2022D049.
为了探讨WRFDA同化系统中不同控制变量方案(CV5,CV6和CV7)所构建的背景误差协方差对雷达资料同化的影响,本文以2017年5月7日发生在广州的一次暖区暴雨为研究个例,开展了不同控制变量方案的单点观测同化试验和雷达同化试验(Exp-CV5,Exp-CV6和Exp-CV7),并对同化试验结果进行诊断分析。单点试验和单次雷达资料同化试验的分析结果表明,Exp-CV6对湿度场的影响最大且在大气中层的比湿均方根误差最小,而Exp-CV7分析增量的影响更集中且梯度更大,更有利于保留中小尺度信息,对风场的模拟效果最好。经过3 h雷达资料循环同化后,Exp-CV7可以更准确地模拟出对流系统的结构,并对本次暖区暴雨雨带与强降水中心位置的预报效果最佳,其次是Exp-CV6。这主要是因为CV7方案对动力场的调整更有利于局地对流的发展,而CV6方案对湿度场的调整较CV5方案更有优势。
To explore the influence of background error covariance constructed by different control variable schemes (CV5, CV6, and CV7) on radar data assimilation in the WRFDA assimilation system, a warm-sector torrential rainfall in Guangzhou on May 7, 2017 was taken as a case study in this paper. The single and radar observation data assimilation (DA) tests (Exp-CV5, Exp-CV6, and Exp-CV7) of three control variable schemes were carried out, and the results of the assimilation tests were diagnosed and analyzed. After the single and one-cycle (or one-time) radar observation DA experiments, it is found that Exp-CV6 has the greatest influence on the humidity field and the least root mean square error of specific humidity in the middle atmosphere. In addition, the influence of Exp-CV7 analysis increments is more concentrated and the gradient of analysis increments is larger, which is more conducive to retaining medium and small-scale information and has the best simulation effect on wind fields. After cyclic radar DA, Exp-CV7 is able to simulate the structure of the convective system more accurately, and the rainfall coverage and heavy precipitation center forecasted by Exp-CV7 align well with the observations. In conclusion, Exp-CV7 produces the best simulation and forecast for this study case, followed by Exp-CV6. This is mainly because the adjustment of a dynamic field by the CV7 scheme is more beneficial to the development of local convection, while the adjustment of the humidity field by the CV6 scheme is more advantageous than that by the CV5 scheme.
控制变量背景误差协方差雷达资料同化
control variablebackground error covarianceradar data assimilation
陈力强,杨森,肖庆农,2009.多普勒雷达资料在冷涡强对流天气中的同化应用试验[J].气象,35(12): 12-20.
陈耀登,陈晓梦,曾腊梅,等,2016.背景场误差样本模拟对同化及数值预报效果的影响[J].高原气象,35(3): 767-776.
陈耀登,赵幸,闵锦忠,等,2015.青藏高原和华东地区背景误差协方差特征的对比研究[J].大气科学学报,38(5): 650-657.
范水勇,陈敏,仲跻芹,等,2009.北京地区高分辨率快速循环同化预报系统性能检验和评估[J].暴雨灾害,28(2): 119-125.
高士博,2018.雷达资料同化在强对流天气预报中的应用研究[D].南京:南京信息工程大学.
卢长浩,陈耀登,孟德明,2019.两种动力控制变量对比分析及其对台风同化和预报的影响[J].大气科学学报,42(6): 916-925.
马旭林,庄照荣,薛纪善,等, 2009. GRAPES非静力数值预报模式的三维变分资料同化系统的发展[J].气象学报,67(1): 50-60.
孙娟珍,陈明轩,范水勇,2016.雷达资料同化方法:回顾与前瞻[J].气象科技进展,6(3): 17-27.
童文雪.2017.针对短时对流降水预报的WRF变分同化系统改进与应用研究[D].南京:南京信息工程大学.
王叶红,陈超君,赵玉春,2016.华中区域模式三维变分中夏季背景误差协方差统计与对比试验[J].暴雨灾害,35(4): 359-370.
夏雪,2016.多变量相关的背景误差协方差及其对同化和预报效果的影响[D].南京:南京信息工程大学.
张思嘉,2019.一次广州特大暴雨事件的局地对流触发机制研究[D].南京:南京信息工程大学.
BANNISTER R N, 2008. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances[J]. Quarterly Journal of the Royal Meteorological Society, 134(637): 1951-1970.
BARKER D M, HUANG W, GUO Y, et al,2004. A three-dimensional variational data assimilation system for MM5: Implementation and initial results[J]. Monthly Weather Review, 132(4): 897-914.
CARTWRIGHT T J, KRISHNAMURTI T N, 2007. Warm season mesoscale superensemble precipitation forecasts in the southeastern United States[J]. Weather and forecasting, 22(4): 873-886.
CHEN Y D, RIZVI S R H, HUANG X Y, et al,2013. Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions[J]. Meteorology and Atmospheric Physics, 121(1/2): 79-98.
CHEN Y D, XIA X, MIN J Z, et al,2016. Balance characteristics of multivariate background error covariance for rainy and dry seasons and their impact on precipitation forecasts of two rainfall events[J]. Meteorology and Atmospheric Physics, 128(5): 579-600.
COURTIER P, THÉPAUT J N,HOLLINGSWORTH A,1994. A strategy for operational implementation of 4D‐Var, using an incremental approach[J]. Quarterly Journal of the Royal Meteorological Society, 120(519): 1367-1387.
GAO X Y, GAO S H,2020. Impact of multivariate background error covariance on the WRF-3DVAR assimilation for the yellow sea fog modeling[J]. Advances in Meteorology, 2020:8816185.
LI X, ZENG M J, WANG Y, et al. 2016. Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radar wind data: Case study of a squall line[J]. Advances in Atmospheric Sciences, 33(10): 1143-1157.
MAIELLO I,FERRETTI R, GENTILE S, et al. 2014. Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF–3DVAR[J]. Atmospheric Measurement Techniques,7(9): 2919-2935.
PARRISH D F,DERBER J C,1992. The National Meteorological Center's spectral statistical-interpolation analysis system[J].Monthly Weather Review,120(8):1747-1763.
SUN J Z, CROOK N A,1997. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments[J]. Journal of Atmospheric Sciences, 54(12): 1642-1661.
SUN J Z, WANG H L, 2013. WRF-ARW variational storm-scale data assimilation: Current capabilities and future developments[J]. Advances in Meteorology, 2013: 815910.
SUN J Z,WANG H L,TONG W X,et al, 2016. Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting[J].Monthly Weather Review,144(1): 149-169.
WANG C, CHEN Y D, CHEN M, et al, 2020. Data assimilation of a dense wind profiler network and its impact on convective forecasting[J]. Atmospheric Research,238: 104880.
WANG H L, SUN J Z, FAN S Y, et al, 2013. Indirect assimilation of radar reflectivity with WRF 3D-Var and its impact on prediction of four summertime convective events[J]. Journal of Applied Meteorology and Climatology, 52(4): 889-902.
XIAO Q N, KUO Y H, SUN J Z, et al, 2005. Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case[J]. Journal of Applied Meteorology, 44(6): 768-788.
XIAO Q N, SUN J Z, 2007. Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002[J]. Monthly Weather Review, 135(10): 3381-3404.
ZHANG J, WANG S X, 2006. An automated 2D multipass Doppler radar velocity dealiasing scheme[J]. American Meteorological Society, 23: 1239-1248.
0
浏览量
1
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构