Assimilation of radar data based on cloud-dependent background error covariance and its impact on rainfall forecasting
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摘要: 传统变分同化方法中使用各向同性和均质的背景场误差协方差，忽略了背景场误差协方差的天气系统依赖性，而在变分框架下引入集合流依赖的背景场误差协方差还需要额外的集合预报。为在变分同化中引入更合理的背景场误差协方差，通过引入云指数构建“云依赖”背景场误差协方差，提出了一种云依赖背景场误差协方差的同化方案，并应用于雷达等多源观测资料同化。基于云依赖背景场误差协方差的资料同化方案，开展了一系列单点观测试验、梅雨期批量循环同化预报试验以及降雨个例详细诊断分析。从单点观测试验看，云依赖背景场误差协方差可以实时动态地调整各格点背景场误差，使分析增量具有明显的各向异性和对云雨特征的依赖；批量循环同化与预报试验表明采用云依赖背景场误差协方差的雷达资料同化可以稳定提高降水预报能力，对大量级降水评分的改善尤为明显；对强对流暴雨过程的诊断进一步表明，云依赖背景场误差协方差的应用改进了动力、热力、水汽和水凝物场的预报。基于云依赖背景场误差协方差的同化方案，能在变分同化框架下引入更符合实时天气特征的背景场误差协方差信息，为更好地同化高分辨率雷达资料提供了基础，有效提高了降雨预报的效果。Abstract: The traditional variational assimilation method uses isotropic and homogeneous background error covariance, which ignores the weather system dependence of the background error covariance, and the introduction of ensemble flow-dependent background error covariance in the variational framework requires additional ensemble forecasts. In order to introduce more reasonable background error covariance in the variational assimilation, a "cloud-dependent" background error covariance is constructed by introducing cloud indices, and a cloud-dependent background error covariance assimilation scheme is proposed and applied to the assimilation of radar and other multi-source observations. Based on the cloud-dependent background error covariance data assimilation scheme, a series of single observation tests and batch cyclic assimilation forecasts during the rainy season as well as detailed diagnostic analysis of rainfall cases are carried out. From the single observation tests, it is found that the cloud-dependent background error covariance can dynamically adjust the background error at each grid point in real time, resulting in significant anisotropy and dependence of the analysis increments on cloud and rain characteristics; the batch cyclic assimilation and forecasting experiments show that the radar assimilation with cloud-dependent background error covariance can steadily improve the precipitation forecasting capability, and the improvement is especially obvious for the large magnitude precipitation scores; The diagnosis of strong convective storms further shows that the application of cloud-dependent background error covariance improves the prediction of dynamical, thermal, water vapor and hydrometeor fields. The assimilation scheme based on cloud-dependent background error covariance can introduce background error covariance information that is more consistent with real-time weather characteristics in the framework of variational assimilation, which provides a basis for better assimilation of high-resolution radar data and effectively improves rainfall forecasting.
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图 1 2016年7月1日00时 （世界时，下同） 云分布 （a. 背景场云量，单位：kg/m2； b. 云指数；c. TBB亮温，单位：K，来自FY-2G；b中A、B、C分别为单点试验选取的晴空、多云及少云区域观测点）
Figure 1. Distribution of cloud amount at 00：00 UTC 1 July 2016 （a. background field cloud amount，unit：kg/m2；b. cloud index；c. TBB，unit：K，from FY-2G；in figure b A，B and C are the selected observation points of clear sky area，cloudy area and less cloudy area in the single observation experiment）
图 2 温度增量分布 （a. Exp_AVE，b. Exp_CLD，下标数字1、2、3分别表示晴空、多云、少云；等值线范围0.04—0.32℃，间隔0.02℃；色阶为云指数）
Figure 2. Increments of temperature （a. Exp_AVE，b. Exp_CLD，1，2，3 indicate clear sky area，cloudy area and less cloudy area，respectively；contour range： 0.04—0.32℃，interval：0.02°C；shaded are cloud indices）
图 3 2017年6月30日21时 （a、d）、7月1日00时 （b、e） 和03时 （c、f） 多云区域同化温度的温度增量分布 （a—c. Exp_AVE试验， d—f. Exp_CLD试验；等值线范围0.04—0.32℃，间隔：0.02℃；色阶，云指数）
Figure 3. Distributions of temperature increments at a single point of assimilation temperature at point B at 21：00 UTC 30 June 2017 （a，d），00：00 UTC （b，e） and 03：00 UTC （c，f） 1 July 2017 （a—c. Exp_AVE experiment，d—f. Exp_CLD experiment；contour range：0.04—0.32℃， interval：0.02°C；shaded are cloud indices）
图 4 研究区域及同化资料分布情况 （a. 常规观测，b. 雷达站点）
Figure 4. Model domains and the distribution of assimilated observations （a. conventional observations，b. radar sites）
图 6 降水ETS （a、d、g）、BIAS （b、e、h） 评分及两组试验各阈值对应的降水评分改进比率 （c、f、i）（区域为 （24°—36°N，108°—122°E），a—c. 0—3 h，d—f. 0—6 h，g—i. 0—12 h）
Figure 6. ETS （a，d，g），BIAS （b，e，h） scores and average improvements in the percentage （c，f，i） of the EXP_CLD experiment compared to the EXP_AVE experiment for different standard thresholds （the scoring area is the main precipitation area （24°—36°N，108°—122°E）；a—c. 0—3 h，d—f. 0—6 h，g—i. 0—12 h）
图 7 2019年6月5日12时 （a） 500 hPa位势高度 （黑线，单位：dagpm；红线为槽线）、风场 （风羽，单位：m/s）、200 hPa高空急流 （色阶，单位：m/s），（b） 850 hPa位势高度 （实线，单位：dagpm）、风场 （风羽，单位：m/s）、比湿 （色阶，单位：g/kg） 及2019年6月5日15时 （c） 武汉站T-lnp图、（d） 雷达反射率因子 （单位：dBz）
Figure 7. Synoptic pattern at 12：00 UTC 5 June 2019：（a） geopotential height （black contours，unit：dagpm，the red line is trough line） and wind field （wind barbs，unit：m/s） at 500 hPa，upper level jet at 200 hPa （shaded areas，unit：m/s）；（b） 850 hPa geopotential height （black contours，unit：dagpm），wind field （unit：m/s） and specific humidity （shaded，unit：g/kg），and （c） T-lnp plot at Wuhan station and （d） radar reflectivity （unit：dBz） at 15：00 UTC 5 June 2019
图 9 2019年6月5日15时Exp_AVE （a、c） 和Exp_CLD （b、d） 试验散度 （色阶，单位：10−5s−1） 和风场 （风矢，单位：m/s） 沿图8线 AB的垂直剖面 （a、b. 2 h预报场，c、d. 3 h预报场）
Figure 9. Cross sections of divergence （shaded，unit：10−5s−1） and wind field （vector，unit：m/s） forecasts along the black line AB （Fig. 8） for Exp_AVE experiment （a，c） and Exp_CLD experiment （b，d） at 15：00 UTC 5 June 2019 （a，b. 2 h forecast field；c，d. 3 h forecast field）
图 10 2019年6月5日15时Exp_AVE （a、c） 和Exp_CLD （b、d） 试验相对湿度 （色阶，单位：%） 和水汽通量 （箭矢，单位：g/（cm·hPa·s）） 沿图8线AB的垂直剖面 （a、b. 2 h预报场， c、d. 3 h预报场）
Figure 10. Cross sections of relative humidity （shaded，unit：10−5s−1） and water vapor flux （vectors，unit：g/（cm·hPa·s）） forecast along the black line AB （Fig. 8） for Exp_AVE experiment （a、c） and Exp_CLD experiment （b、d） at 15：00 UTC 5 June 2019 （a，b. 2 h forecast field；c，d. 3 h forecasts field）
图 11 2019年6月5日15时Exp_AVE （a、c） 和Exp_CLD （b、d） 试验的雷达反射率 （色阶，单位：dBz） 和相当位温 （蓝实线，单位：K） 沿图8线AB的垂直剖面 （a、b. 2 h 预报场， c、d. 3 h 预报场）
Figure 11. Cross sections of radar reflectivity （shaded，unit：dBz） and equivalent potential temperature （blue solid lines，unit：K） forecast along the black line AB （Fig. 8） for Exp_AVE experiment （a，c） and Exp_CLD experiment （b，d） at 15：00 UTC 5 June 2019 （a，b. 2 h forecast field；c，d. 3 h forecast field）
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[1] 陈明轩,肖现,高峰等. 2016. 基于雷达四维变分分析系统的强对流高分辨率模拟个例分析和批量检验. 气象学报,74(3):421-441 doi: 10.11676/qxxb2016.031Chen M X,Xiao X,Gao F,et al. 2016. A case study and batch verification on high resolution numerical simulations of severe convective events using an analysis system based on rapid-refresh 4-D variational radar data assimilation. Acta Meteor Sinica,74(3):421-441 (in Chinese) doi: 10.11676/qxxb2016.031 [2] 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. 高原气象,35(3):767-776 doi: 10.7522/j.issn.1000-0534.2014.00156Chen Y D,Chen X M,Zeng L M,et al. 2016. Influence of background sample error simulation on data assimilation and forecast. Plateau Meteor,35(3):767-776 (in Chinese) doi: 10.7522/j.issn.1000-0534.2014.00156 [3] 范水勇,王洪利,陈敏等. 2013. 雷达反射率资料的三维变分同化研究. 气象学报,71(3):527-537 doi: 10.11676/qxxb2013.032Fan S Y,Wang H L,Chen M,et al. 2013. Study of the data assimilation of radar reflectivity with the WRF 3DVar. Acta Meteor Sinica,71(3):527-537 (in Chinese) doi: 10.11676/qxxb2013.032 [4] 龚建东,张林,王金成. 2020. 背景误差水平相关结构对四维变分资料同化的影响研究. 气象学报,78(6):988-1001 doi: 10.11676/qxxb2020.062Gong J D,Zhang L,Wang J C. 2020. An impact study of background error horizontal correlation structure on 4DVar. Acta Meteor Sinica,78(6):988-1001 (in Chinese) doi: 10.11676/qxxb2020.062 [5] 何静,陈敏,仲跻芹等. 2019. 雷达反射率三维拼图观测资料在北方区域数值模式预报系统中的同化应用研究. 气象学报,77(2):210-232 doi: 10.11676/qxxb2019.005He J,Chen M,Zhong J Q,et al. 2019. A study of three-dimensional radar reflectivity mosaic assimilation in the regional forecasting model for North China. Acta Meteor Sinica,77(2):210-232 (in Chinese) doi: 10.11676/qxxb2019.005 [6] 黄昕,周玉淑,冉令坤等. 2021. 一次新疆伊犁河谷特大暴雨过程的环境场及不稳定条件分析. 大气科学,45(1):148-164Huang X,Zhou Y S,Ran L K,et al. 2021. Analysis of the environmental field and unstable conditions on a rainstorm event in the Ili Valley of Xinjiang. Chinese J Atmos Sci,45(1):148-164 (in Chinese) [7] 江源,刘黎平,庄薇. 2009. 多普勒天气雷达地物回波特征及其识别方法改进. 应用气象学报,20(2):203-213 doi: 10.3969/j.issn.1001-7313.2009.02.010Jiang Y,Liu L P,Zhuang W. 2009. Statistical characteristics of clutter and improvements of ground clutter identification technique with Doppler Weather Radar. J Appl Meteor Sci,20(2):203-213 (in Chinese) doi: 10.3969/j.issn.1001-7313.2009.02.010 [8] 刘成思,薛纪善. 2005. 关于集合Kalman滤波的理论和方法的发展. 热带气象学报,21(6):628-633 doi: 10.3969/j.issn.1004-4965.2005.06.009Liu C S,Xue J S. 2005. The ensemble Kalman filter theory and method development. J Trop Meteor,21(6):628-633 (in Chinese) doi: 10.3969/j.issn.1004-4965.2005.06.009 [9] 闵锦忠,吴乃庚. 2020. 近二十年来暴雨和强对流可预报性研究进展. 大气科学,44(5):1039-1056Min J Z,Wu N G. 2020. Advances in atmospheric predictability of heavy rain and severe convection. Chinese J Atmos Sci,44(5):1039-1056 (in Chinese) [10] 潘玉洁,赵坤,潘益农等. 2012. 用双多普勒雷达分析华南一次飑线系统的中尺度结构特征. 气象学报,70(4):736-751 doi: 10.11676/qxxb2012.060Pan Y J,Zhang K,Pan Y N,et al. 2012. Dual-Doppler analysis of a squall line in southern China. Acta Meteor Sinica,70(4):736-751 (in Chinese) doi: 10.11676/qxxb2012.060 [11] 沈艳,潘旸,宇婧婧等. 2013. 中国区域小时降水量融合产品的质量评估. 大气科学学报,36(1):37-46 doi: 10.3969/j.issn.1674-7097.2013.01.005Shen Y,Pan Y,Yu J J,et al. 2013. Quality assessment of hourly merged precipitation product over China. Trans Atmos Sci,36(1):37-46 (in Chinese) doi: 10.3969/j.issn.1674-7097.2013.01.005 [12] 孙娟珍,陈明轩,范水勇. 2016. 雷达资料同化方法:回顾与前瞻. 气象科技进展,6(3):17-27Sun J Z,Chen M X,Fan S Y. 2016. Radar data assimilation methods:Review and future perspective. Adv Meteor Sci Technol,6(3):17-27 (in Chinese) [13] 许小永,刘黎平,郑国光. 2006. 集合卡尔曼滤波同化多普勒雷达资料的数值试验. 大气科学,30(4):712-728 doi: 10.3878/j.issn.1006-9895.2006.04.16Xu X Y,Liu L P,Zheng G G. 2006. Numerical experiment of assimilation of Doppler radar data with an ensemble Kalman filter. Chinese J Atmos Sci,30(4):712-728 (in Chinese) doi: 10.3878/j.issn.1006-9895.2006.04.16 [14] 杨毅,邱崇践,龚建东等. 2008. 三维变分和物理初始化方法相结合同化多普勒雷达资料的试验研究. 气象学报,66(4):479-488 doi: 10.3321/j.issn:0577-6619.2008.04.001Yang Y,Qiu C J,Gong J D,et al. 2008. Three-dimensional variational combined with physical initialization for assimilation of Doppler radar data. Acta Meteor Sinica,66(4):479-488 (in Chinese) doi: 10.3321/j.issn:0577-6619.2008.04.001 [15] 朱立娟,龚建东,黄丽萍等. 2017. GRAPES三维云初始场形成及在短临预报中的应用. 应用气象学报,28(1):38-51 doi: 10.11898/1001-7313.20170104Zhu L J,Gong J D,Huang L P,et al. 2017. Three-dimensional cloud initial field created and applied to GRAPES numerical weather prediction nowcasting. J Appl Meteor Sci,28(1):38-51 (in Chinese) doi: 10.11898/1001-7313.20170104 [16] 庄照荣,王瑞春,王金成等. 2019. GRAPES_Meso背景误差特征及应用. 应用气象学报,30(3):316-331 doi: 10.11898/1001-7313.20190306Zhuang Z R,Wang R C,Wang J C,et al. 2019. Characteristics and application of background errors in GRAPES _Meso. J Appl Meteor Sci,30(3):316-331 (in Chinese) doi: 10.11898/1001-7313.20190306 [17] Aksoy A,Dowell D C,Snyder C. 2009. A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part Ⅰ:Storm-scale analyses. Mon Wea Rev,137(6):1805-1824 doi: 10.1175/2008MWR2691.1 [18] Chen Y D,Fang K M,Chen M,et al. 2021. Diurnally varying background error covariances estimated in RMAPS-ST and their impacts on operational implementations. Atmos Res,257:105624 doi: 10.1016/j.atmosres.2021.105624 [19] Chen Y D,Rizvi S R H,Huang X Y,et al. 2013. Balance characteristics of multivariate background error covariance and their impact on analyses and forecasts in Tropics and Arctic. Meteor Atmos Phys,121:79-98 doi: 10.1007/s00703-013-0251-y [20] Gao J D,Stensrud D J. 2012. Assimilation of reflectivity data in a convective-Scale,cycled 3DVAR Framework with hydrometeor classification. J Atmos Sci,69(3):1054-1065 doi: 10.1175/JAS-D-11-0162.1 [21] Lee J C K,Huang X Y. 2020. Background error statistics in the tropics:Structures and impact in a convective-scale numerical weather prediction system. Quart J Roy Meteor Soc,146(730):2154-2173 doi: 10.1002/qj.3785 [22] Li Y Z,Wang X G,Xue M. 2012. Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVAR system for the prediction of Hurricane Ike (2008). Mon Wea Rev,140(11):3507-3524 doi: 10.1175/MWR-D-12-00043.1 [23] Lin E L,Yang Y,Qiu X B,et al. 2021. Impacts of the radar data assimilation frequency and large-scale constraint on the short-term precipitation forecast of a severe convection case. Atmos Res,257:105590 doi: 10.1016/j.atmosres.2021.105590 [24] Liu L P,Zhang Z Q,Yu D R,et al. 2012. Comparison of precipitation observations from a prototype space-based cloud radar and ground-based radars. Adv Atmos Sci,29(6):1318-1329 doi: 10.1007/s00376-012-1233-6 [25] Liu Y A,Sun Z B,Chen M S,et al. 2018. Assimilation of atmospheric infrared sounder radiances with WRF-GSI for improving typhoon forecast. Front Earth Sci,12(3):457-467 doi: 10.1007/s11707-018-0728-6 [26] Ménétrier B,Montmerle T. 2011. Heterogeneous background-error covariances for the analysis and forecast of fog events. Quart J Roy Meteor Soc,137(661):2004-2013 doi: 10.1002/qj.802 [27] Meng D M,Chen Y D,Jun L,et al. 2021. Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction. Mon Wea Rev,149(9):3155-3171 [28] Michel Y,Auligne T,Montmerle T,et al. 2011. Heterogeneous convective-scale background error covariances with the inclusion of hydrometeor variables. Mon Wea Rev,139(9):2994-3015 doi: 10.1175/2011MWR3632.1 [29] Montmerle T,Berre L. 2010. Diagnosis and formulation of heterogeneous background-error covariances at the mesoscale. Quart J Roy Meteor Soc,136(651):1408-1420 doi: 10.1002/qj.655 [30] Parrish D F,Derber J C. 1992. The national meteorological center's spectral statistical-interpolation analysis system. Mon Wea Rev,120(8):1747-1736 doi: 10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2 [31] Pereira M B,Berre L. 2006. The use of an ensemble approach to study the background error covariances in a global NWP model. Mon Wea Rev,134(9):2466-2489 doi: 10.1175/MWR3189.1 [32] Shao A M,Qiu C J,Liu L P. 2004. Kinematic structure of a heavy rain event from dual-Doppler radar observations. Adv Atmos Sci,21(4):609-616 doi: 10.1007/BF02915728 [33] Snyder C,Zhang F Q. 2003. Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon Wea Rev,131(8):1663-1677 doi: 10.1175//2555.1 [34] Sun J Z,Trier S B,Xiao Q N,et al. 2012. Sensitivity of 0-12-h warm-season precipitation forecasts over the Central United States to model initialization. Wea Forecasting,27(4):832-855 doi: 10.1175/WAF-D-11-00075.1 [35] 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. Mon Wea Rev,144(1):149-169 doi: 10.1175/MWR-D-14-00205.1 [36] 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 Appl Meteor Climatol,52(4):889-902 doi: 10.1175/JAMC-D-12-0120.1 [37] Xiao Q N,Kuo Y H,Sun J Z,et al. 2007. An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at Landfall. J Appl Meteor Climatol,46(1):14-22 doi: 10.1175/JAM2439.1 [38] Xu D S,Shao A M,Qiu C J. 2012. Doppler radar data assimilation with a local SVD-En3DVar method. Acta Meteor Sinica,26(6):717-734 doi: 10.1007/s13351-012-0604-3 [39] Zhao K,Huang H,Wang M J,et al. 2019. Recent progress in dual-polarization radar research and applications in China. Adv Atmos Sci,36(9):961-974 doi: 10.1007/s00376-019-9057-2 -
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