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基于云依赖背景场误差协方差的雷达资料同化及对降雨预报影响研究

陈娴雅 陈耀登 孟德明

陈娴雅,陈耀登,孟德明. 2022. 基于云依赖背景场误差协方差的雷达资料同化及对降雨预报影响研究. 气象学报,80(2):243-256 doi: 10.11676/qxxb2022.011
引用本文: 陈娴雅,陈耀登,孟德明. 2022. 基于云依赖背景场误差协方差的雷达资料同化及对降雨预报影响研究. 气象学报,80(2):243-256 doi: 10.11676/qxxb2022.011
Chen Xianya, Chen Yaodeng, Meng Deming. 2022. Assimilation of radar data based on cloud-dependent background error covariance and its impact on rainfall forecasting. Acta Meteorologica Sinica, 80(2):243-256 doi: 10.11676/qxxb2022.011
Citation: Chen Xianya, Chen Yaodeng, Meng Deming. 2022. Assimilation of radar data based on cloud-dependent background error covariance and its impact on rainfall forecasting. Acta Meteorologica Sinica, 80(2):243-256 doi: 10.11676/qxxb2022.011

基于云依赖背景场误差协方差的雷达资料同化及对降雨预报影响研究

doi: 10.11676/qxxb2022.011
基金项目: 国家自然科学基金面上项目(42075148) 、国家重点研发计划重点专项项目(2017YFC1502102)
详细信息
    作者简介:

    陈娴雅,主要从事资料同化和数值预报研究与应用工作。E-mail:cxy@nuist.edu.cn

    通讯作者:

    陈耀登,主要从事资料同化和数值预报研究与应用工作。E-mail:keyu@nuist.edu.cn

  • 中图分类号: P456.7

Assimilation of radar data based on cloud-dependent background error covariance and its impact on rainfall forecasting

  • 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。

     

  • 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 5  循环同化试验流程

    Figure 5.  Schematic diagram showing the assimilation and forecast cycles

    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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  • 收稿日期:  2021-08-18
  • 修回日期:  2021-11-29
  • 网络出版日期:  2021-12-03

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