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基于雷达—雨量计降水融合方法提高极端降水监测能力

李梦迪 戚友存 张哲 管晓丹

李梦迪, 戚友存, 张哲, 等. 2022. 基于雷达—雨量计降水融合方法提高极端降水监测能力[J]. 大气科学, 46(6): 1523−1542 doi: 10.3878/j.issn.1006-9895.2201.21201
引用本文: 李梦迪, 戚友存, 张哲, 等. 2022. 基于雷达—雨量计降水融合方法提高极端降水监测能力[J]. 大气科学, 46(6): 1523−1542 doi: 10.3878/j.issn.1006-9895.2201.21201
LI Mengdi, QI Youcun, ZHANG Zhe, et al. 2022. Improving the Detection Performance of Extreme Precipitation Observations Using a Radar–Gauge Merging Algorithm [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1523−1542 doi: 10.3878/j.issn.1006-9895.2201.21201
Citation: LI Mengdi, QI Youcun, ZHANG Zhe, et al. 2022. Improving the Detection Performance of Extreme Precipitation Observations Using a Radar–Gauge Merging Algorithm [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(6): 1523−1542 doi: 10.3878/j.issn.1006-9895.2201.21201

基于雷达—雨量计降水融合方法提高极端降水监测能力

doi: 10.3878/j.issn.1006-9895.2201.21201
基金项目: 国家重点研发计划项目2018YFC1507505,中国科学院百人计划
详细信息
    作者简介:

    李梦迪,女,1996年出生,硕士研究生,主要从事雷达气象学研究。E-mail: limd18@lzu.edu.cn

    通讯作者:

    戚友存,E-mail: youcun.qi@igsnrr.ac.cn;管晓丹,E-mail: guanxd@lzu.edu.cn

  • 中图分类号: P426

Improving the Detection Performance of Extreme Precipitation Observations Using a Radar–Gauge Merging Algorithm

Funds: National Key Research and Development Program of China (Grant 2018YFC1507505), Hundred Talents Program of Chinese Academy of Sciences
  • 摘要: 高时空分辨率、高精度的降水产品对于极端降水的监测以及防灾减灾具有重要意义。地面雨量计提供点尺度降水精确观测,但无法精细化捕捉对流性强降水的空间分布。雷达观测可以精细地刻画降水的空间分布特征,但雷达定量估计降水(QPE,quantitative precipitation estimation)产品估测精度易受雷达观测偏差和ZR(雷达反射率—降水率)关系等因素影响。因此,本文开展高时空分辨率的雷达—雨量计降水融合算法研究,集成雨量计观测和雷达定量估计降水产品各自的优点。该算法主要步骤包括:雨量站观测数据格点化、局地雨量计订正雷达QPE和雷达—雨量计降水融合三个部分。首先利用克里金插值方法,对雨量站观测的降水进行插值,得到格点降水信息;再通过局地雨量计订正方法系统性地订正雷达QPE产品,以提高雷达QPE产品精度;最后,结合降水类型,通过雷达—雨量计降水融合算法,产生高时空分辨率、高精度的雷达—雨量计降水融合产品。通过郑州“21·7”暴雨、台风“烟花”和2021年8月随州暴雨三个典型的极端降水个例,对雷达—雨量计降水融合算法产生的雷达—雨量计降水融合产品进行了系统地评估和分析。结果表明,在不同的极端降水个例和不同的降水时段,雷达—雨量计降水融合产品精度上优于雷达QPE产品,且在降水的空间分布上较雨量站观测格点插值产品更能精细地刻画降水的结构特征。表明算法得到的雷达—雨量计降水融合产品的准确性较高,对极端降水有较好地捕捉和监测能力。
  • 图  1  雷达—雨量计降水融合算法流程图

    Figure  1.  Flowchart of the radar–gauge merging algorithm

    图  2  2021年7月20日09时(协调世界时,下同)河南地区(a)区域观测站降水量(单位:mm)分布,(b)基于区域观测站的克里金插值降水量(单位:mm)分布,(c)插值场与地面国家站观测1 h降水量散点图。图a、b中的圆点和星状点分别为国家站(86个)和区域站(1179个)测量的1 h降水量分布。红色方框区域为国家站观测和区域站观测插值场相差较大的区域之一

    Figure  2.  Precipitation (units: mm) distributions of (a) the regional gauge station observations, (b) Kriging interpolations based on the regional gauge station observations, and (c) scatterplots of 1-h precipitation (units: mm) from Kriging interpolations and the national gauge station observations in Henan area at 0900 UTC 20 July 2021. In Figs. a, b, dots and stars represent the 1-h precipitation (units: mm) from 86 national gauge stations and 1179 regional gauge stations, respectively. The red box area is one of the areas with large differences between Kriging interpolations based on the regional gauge station observations and the national gauge station observations

    图  3  2021年7月20日09时河南南部(图2中红色方框区域)(a)基于区域地面观测站的克里金插值场降水量分布和(b)原始雷达QPE产品降水量(单位:mm)分布。图a、b中的圆点和星状点分别为国家站和区域站测量的1 h降水量(单位:mm)分布

    Figure  3.  Precipitation (units: mm) distributions of (a) Kriging interpolations based on the regional gauge station observations and (b) radar QPE (quantitative precipitation estimation) in southern Henan area (the red box in Fig. 2) at 0900 UTC 20 July 2021. In Figs. a, b, dots and stars represent the 1-h precipitation (units: mm) from national gauge stations and regional gauge stations, respectively

    图  4  2021年7月20日09时河南地区雷达QPE产品降水量订正(a)前、(b)后分布,雷达QPE产品1 h降水量订正(c)前、(d)后与国家站1 h降水量散点图。图a、b中圆点为国家站测量的1 h降水量(单位:mm)分布

    Figure  4.  Precipitation distributions of radar QPE (a) before and (b) after LGC (local gauge-corrected), scatterplots of 1-h precipitation from radar QPE (c) before and (d) after LGC and the national gauge station observations in Henan area at 0900 UTC 20 July 2021. In Figs. a, b, dots represent 1-h precipitation (units: mm) from the national gauge stations

    图  5  2021年7月20日09时河南地区(a)雷达—雨量计降水融合产品的降水量(单位:mm)分布,(b)雷达—雨量计降水融合产品的1 h降水量与国家站观测的1 h降水量的散点图。图a中圆点为国家站观测的1 h降水量分布

    Figure  5.  (a) Precipitation (units: mm) distribution of the radar–gauge merging QPE and (b) scatterplots of 1-h precipitation from the radar–gauge merging algorithm and the national gauge station in Henan area at 0900 UTC 20 July 2021. In Fig. a, dots represent 1-h precipitation (units: mm) from the national gauge stations

    图  6  2021年7月20日09时郑州地区(a)雷达QPE、(b)区域站克里金插值场、(c)区域站订正后雷达QPE、(d)雷达—雨量计降水融合产品的降水量(单位:mm)分布。圆点和星状点分别为国家站和区域站1 h降水量(单位:mm)分布。白色框区域和黑色框区域分别表示为空间变异性大值区和奇异雨量计观测值区域

    Figure  6.  Precipitation (units: mm) distributions from (a) radar QPE, (b) Kriging interpolations based on the regional gauge station observations, (c) radar QPE after LGC, and (d) radar–gauge merging QPE in Zhengzhou at 0900 UTC 20 July 2021. Dots and stars represent 1-h precipitation (units: mm) from the national and regional gauge stations, respectively. The white boxed area and the black boxed area indicate the area of large spatial variability and the area of odd rain gauge observations, respectively

    图  7  郑州“21·7”暴雨时间段(2021年7月20日00时至7月21日00时)(a)雷达QPE产品1 h降水量、(b)雷达—雨量计降水融合产品1 h降水量与地面国家站观测1 h降水量的散点图

    Figure  7.  Scatterplots of 1-h precipitation from (a) radar QPE, (b) radar–gauge merging QPE and the national gauge station observations in Zhengzhou from 0000 UTC 20 July to 0000 UTC 21 July 2021

    图  8  2021年7月20日00时至22时郑州“21·7”暴雨评分指标时间序列:(a)均方根误差(RMSE);(b)相对误差(RMAE);(c)相对偏差(RMB);(d)相关系数(CC)。蓝色线为雷达QPE产品,红色线为融合产品。图a中叠加了个例1研究区域(30.5°~37.5°N,109.5°~117.5°E)内所有国家站1 h降水量均值(AHP,灰色柱)

    Figure  8.  Time series of (a) root mean square error (RMSE), (b) root mean absolute error (RMAE), (c) relative mean bias (RMB), and (d) correlation coefficient (CC) for the precipitation event in Zhengzhou from 0000 UTC to 2200 UTC 20 July 2021. Blue lines show the radar QPE, and the red lines show the radar–gauge merging QPE. In Fig. a, gray bars represent average hourly precipitation (AHP) of all national gauge stations in the study area (30.5°–37.5°N, 109.5°–117.5°E) of the first case

    图  9  2021年7月20日21时郑州地区不同降水产品的降水量(单位:mm)分布:(a)区域站克里金插值场;(b)原始雷达QPE产品;(c)区域站订正后雷达QPE产品;(d)雷达—雨量计降水融合产品。圆点和星状点分别为国家站和区域站1 h降水量(单位:mm)分布,黑色框为融合产品改善较小区域

    Figure  9.  Precipitation (units: mm) distributions in Zhengzhou at 2100 UTC 20 July 2021: (a) Radar QPE; (b) radar QPE after LGC; (c) Kriging interpolations based on the regional gauge station observations; (d) radar–gauge merging QPE. Dots and stars represent 1-h precipitation (units: mm) from the national and regional gauge stations, respectively. The black boxed area shows the small improvement area of radar–gauge merging QPE

    图  10  台风登陆过程中(2021年7月25日21时)(a)雷达QPE产品、(b)雷达—雨量计降水融合产品的降水量(单位:mm)分布及(c、d)其1 h降水量与国家站观测1 h降水量散点图。图a、b中圆点为国家站降水量分布

    Figure  10.  Precipitation (units: mm) distributions of (a) radar QPE and (b) radar–gauge merging QPE and scatterplots of 1-h precipitation from (c) radar QPE, (d) radar–gauge merging QPE and the national gauge station observations during the typhoon In-Fa landfall (at 2100 UTC 25 July 2021). In Figs. a, b, dots represent the precipitation from the national gauge stations

    图  11  台风“烟花”过程中(2021年7月24日07时)(a)雷达—雨量计降水融合产品降水量(单位:mm)分布及(b)其1 h降水量与国家站1 h降水量的散点图。图a中圆点为国家站1 h降水量分布

    Figure  11.  (a) Precipitation (units: mm) distribution of radar–gauge merging QPE and (b) scatterplots of 1-h precipitation from radar–gauge merging QPE and the national gauge station observations during the typhoon In-Fa landfall (at 0700 UTC 24 July 2021). In Fig. a, the dots represent 1-h precipitation (units: mm) from the national gauge stations

    图  12  图7,但为台风“烟花”时段(2021年7月22日01时至7月28日09时),区域为个例二研究区域

    Figure  12.  As in Fig. 7, but for the period of the typhoon In-Fa (from 0100 UTC 22 July to 0900 UTC 28 July 2021). The area is the study area of the second case

    图  13  图8,但为台风“烟花”时段(2021年7月22日01时至7月28日04时),区域为个例二研究区域

    Figure  13.  As in Fig. 8, but for the period of the typhoon In-Fa (from 0100 UTC 22 July to 0400 UTC 28 July 2021). The area is the study area of the second case

    图  14  图7,但为随州暴雨时间段(2021年8月10日01时至8月13日15时),区域为个例三研究区域

    Figure  14.  As in Fig. 7, but for the precipitation event in Suizhou (from 0100 UTC 10 August to 1500 UTC 13 August 2021). The area is the study area of the third case

    图  15  图8,但为随州暴雨时间段(2021年8月10日01时至8月13日13时),区域为个例三研究区域

    Figure  15.  As in Fig. 8, but for the precipitation event in Suizhou (from 0100 UTC 10 August to 1300 UTC 13 August 2021). The area is the study area of the third case

    图  16  2021年8月(a、b)11日07时、(c、d)11日18时、(e、f)11日23时、(g、h)13日00时随州暴雨雷达QPE产品(左)、雷达—雨量计降水融合产品(右)的降水量(单位:mm)分布。圆点为国家站测量的1 h降水量(单位:mm)分布

    Figure  16.  Precipitation (units: mm) distributions of radar QPE (left) and radar–gauge merging QPE (right) for the precipitation event in Suizhou at (a, b) 0700 UTC 11, (c, d) 1800 UTC 11, (e, f) 2300 UTC 11 and (g, h) 0000 UTC 13 August 2021. The dots represent 1-h precipitation (units: mm) from the national gauge stations

    表  1  极端降水个例信息

    Table  1.   Extreme precipitation events summary

    个例名称时间区域范围特点
    1郑州“21·7”暴雨2021年7月20日00时
    (协调世界时,下同)
    至21日00时
    (30.5°~37.5°N,109.5°~117.5°E)“列车效应”导致多个对流云团反复在郑州附近发展移动,导致降水强度大、维持时间长,引起局地极端降水天气;郑州2021年7月21日09时的小时降雨量达到了201.9 mm。
    2台风“烟花”2021年7月22日01时
    至28日09时
    (28.0°~34.0°N,117.0°~124.0°E)2021年7月25日04:30前后第一次登陆,2021年7月26日01:50前后第二次登陆,最强风力为42 m s−1(14级) 。
    32021年8月随州暴雨2021年8月10日01时
    至13日15时
    (30.0°~32.5°N,110.0°~115.0°E)强降水过程总雨量大、极端性强、突发性强。降雨主要集中在随州市南部,累计雨量超过200 mm。
    下载: 导出CSV
  • [1] Bayabil H K, Fares A, Sharif H O, et al. 2019. Effects of spatial and temporal data aggregation on the performance of the multi-radar multi-sensor system [J]. JAWRA Journal of the American Water Resources Association, 55(6): 1492−1504. doi: 10.1111/1752-1688.12799
    [2] Cao Xuejian, Qi Youcun, Ni Guangheng. 2021. Significant impacts of rainfall redistribution through the roof of buildings on urban hydrology [J]. Journal of Hydrometeorology, 22(4): 1007−1023. doi: 10.1175/JHM-D-20-0220.1
    [3] Chen Mengye, Nabih S, Brauer N S, et al. 2020. Can remote sensing technologies capture the extreme precipitation event and its cascading hydrological response? A case study of hurricane Harvey using EF5 modeling framework [J]. Remote Sensing, 12(3): 445. doi: 10.3390/rs12030445
    [4] 程开宇, 张磊磊, 康颖, 等. 2016. 多源卫星降水数据在瓯江流域的适用性分析 [J]. 水电能源科学, 34(12): 15−19.

    Cheng Kaiyu, Zhang Leilei, Kang Ying, et al. 2016. Applicability analysis of various satellite-based precipitation in Oujiang Basin [J]. Water Resources and Power (in Chinese), 34(12): 15−19.
    [5] Daly C, Neilson R P, Phillips D L. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain [J]. J. Appl. Meteor, 33(2): 140−158. doi: 10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2
    [6] 丁一汇. 2018. 气候变化与城市化效应对中国超大城市极端暴雨的影响 [J]. 中国防汛抗旱, 28(2): 1−2.

    Ding Yihui. 2018. Impacts of climate change and urbanization on extreme rainfall events in megacities in China [J]. China Flood & Drought Management, 28(2): 1−2.
    [7] 房彬, 班显秀, 郭学良, 等. 2010. 雷达—雨量计—粒子激光探测仪联合估算降水量 [J]. 大气科学, 34(3): 513−519. doi: 10.3878/j.issn.1006-9895.2010.03.05

    Fang Bin, Ban Xianxiu, Guo Xueliang, et al. 2010. Area rainfall estimation by using radar, raingauge, and particle laser-based optical measurement instrument [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 34(3): 513−519. doi: 10.3878/j.issn.1006-9895.2010.03.05
    [8] Feki H, Slimani M, Cudennec C. 2017. Geostatistically based optimization of a rainfall monitoring network extension: Case of the climatically heterogeneous Tunisia [J]. Hydrology Research, 48(2): 514−541. doi: 10.2166/nh.2016.256
    [9] 郭春辉, 王旭, 袁微. 2014. 多普勒天气雷达径向干扰回波的识别与消除[J]. 气象水文海洋仪器, 31(2): 24–29, 32

    Guo Chunhui, Wang Xu, Yuan Wei. 2014. Identification and removal of radial interference echo of Doppler weather radar [J]. Meteorological, Hydrological and Marine Instrument (in Chinese), 31(2): 24–29, 32. doi:10.3969/j.issn.1006-009X.2014.02.007
    [10] Haiden T, Pistotnik G. 2009. Intensity-dependent parameterization of elevation effects in precipitation analysis [J]. Advances in Geosciences, 20: 33−38. doi: 10.5194/adgeo-20-33-2009
    [11] Haiden T, Kann A, Wittmann C, et al. 2011. The Integrated Nowcasting through Comprehensive Analysis (INCA) system and its validation over the eastern Alpine region [J]. Wea. Forecasting, 26(2): 166−183. doi: 10.1175/2010WAF2222451.1
    [12] Hong Yang, Hsu K L, Sorooshian S, et al. 2004. Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system [J]. J. Appl. Meteor., 43(12): 1834−1853. doi: 10.1175/JAM2173.1
    [13] 胡志群, 刘黎平, 楚荣忠, 等. 2008. X波段双线偏振雷达不同衰减订正方法对比及其对降水估测影响研究 [J]. 气象学报, 66(2): 251−261. doi: 10.11676/qxxb2008.024

    Hu Zhiqun, Liu Liping, Chu Rongzhong, et al. 2008. Comparison of different attenuation correction methods and their effects on estimated rainfall using x-band dual linear polarimetric radar [J]. Acta Meteorologica Sinica (in Chinese), 66(2): 251−261. doi: 10.11676/qxxb2008.024
    [14] Huffman G J, Bolvin D T, Nelkin E J, et al. 2007. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales [J]. Journal of Hydrometeorology, 8(1): 38−55. doi: 10.1175/JHM560.1
    [15] Kitzmiller D, Miller D, Fulton R, et al. 2013. Radar and multisensor precipitation estimation techniques in national weather service hydrologic operations [J]. Journal of Hydrologic Engineering, 18(2): 133−142. doi: 10.1061/(ASCE)HE.1943-5584.0000523
    [16] 李巧, 戚友存, 朱自伟, 等. 2021. 复杂地形下C波段雷达定量降水估计算法 [J]. 气象学报, 79(4): 689−702. doi: 10.11676/qxxb2021.038

    Li Qiao, Qi Youcun, Zhu Ziwei, et al. 2021. Quantitative precipitation estimation algorithm for C-band radar situated in complex topographical regions [J]. Acta Meteorologica Sinica (in Chinese), 79(4): 689−702. doi: 10.11676/qxxb2021.038
    [17] 刘黎平, 张扬, 丁晗. 2021. Ka/Ku双波段云雷达反演空气垂直运动速度和雨滴谱方法研究及初步应用 [J]. 大气科学, 45(5): 1099−1113. doi: 10.3878/j.issn.1006-9895.2104.20200

    Liu Liping, Zhang Yang, Ding Han. 2021. Vertical air motion and raindrop size distribution retrieval using a Ka/Ku dual-wavelength cloud radar and its preliminary application [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(5): 1099−1113. doi: 10.3878/j.issn.1006-9895.2104.20200
    [18] 刘晓阳, Diallo T D, 毛节泰, 等. 2005. GMS-5卫星估计中国西部地区月降水 [J]. 大气科学, 29(4): 518−525. doi: 10.3878/j.issn.1006-9895.2005.04.03

    Liu Xiaoyang, Diallo T D, Mao Jietai, et al. 2005. Monthly precipitation estimation over western China using GMS satellite data [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 29(4): 518−525. doi: 10.3878/j.issn.1006-9895.2005.04.03
    [19] 刘元波, 傅巧妮, 宋平, 等. 2011. 卫星遥感反演降水研究综述 [J]. 地球科学进展, 26(11): 1162−1172. doi: 10.11867/j.issn.1001-8166.2011.11.1162

    Liu Yuanbo, Fu Qiaoni, Song Ping, et al. 2011. Satellite retrieval of precipitation: An overview [J]. Advances in Earth Science (in Chinese), 26(11): 1162−1172. doi: 10.11867/j.issn.1001-8166.2011.11.1162
    [20] 柳云雷, 李昌兴, 张乐坚, 等. 2020. 基于高分辨率高程数据统计分析新一代天气雷达组网的地形遮挡影响 [J]. 气象学报, 78(4): 705−720. doi: 10.11676/qxxb2020.037

    Liu Yunlei, Li Changxing, Zhang Lejian, et al. 2020. Statistical analysis of terrain blockage impacts on the CINRAD network based on DEM data [J]. Acta Meteorologica Sinica (in Chinese), 78(4): 705−720. doi: 10.11676/qxxb2020.037
    [21] Lu Naimeng, You Ran, Zhang Wenjian. 2004. A fusing technique with satellite precipitation estimate and raingauge data [J]. Acta Meteorologica Sinica, 18(2): 141−146.
    [22] Maggioni V, Meyers P C, Robinson M D. 2016. A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) era [J]. Journal of Hydrometeorology, 17(4): 1101−1117. doi: 10.1175/JHM-D-15-0190.1
    [23] 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 [J]. 大气科学, 44(5): 1058−1075. doi: 10.3878/j.issn.1006-9895.2003.19186

    Min Jinzhong, Wu Naigeng. 2020. Advances in atmospheric predictability of heavy rain and severe convection [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 44(5): 1058−1075. doi: 10.3878/j.issn.1006-9895.2003.19186
    [24] 潘旸, 沈艳, 宇婧婧, 等. 2012. 基于最优插值方法分析的中国区域地面观测与卫星反演逐时降水融合试验 [J]. 气象学报, 70(6): 1381−1389. doi: 10.11676/qxxb2012.116

    Pan Yang, Shen Yan, Yu Jingjing, et al. 2012. Analysis of the combined gauge-satellite hourly precipitation over China based on the OI technique [J]. Acta Meteorologica Sinica (in Chinese), 70(6): 1381−1389. doi: 10.11676/qxxb2012.116
    [25] 潘旸, 谷军霞, 宇婧婧, 等. 2018. 中国区域高分辨率多源降水观测产品的融合方法试验 [J]. 气象学报, 76(5): 755−766. doi: 10.11676/qxxb2018.034

    Pan Yang, Gu Junxia, Yu Jingjing, et al. 2018. Test of merging methods for multi-source observed precipitation products at high resolution over China [J]. Acta Meteorologica Sinica (in Chinese), 76(5): 755−766. doi: 10.11676/qxxb2018.034
    [26] Qi Youcun, Zhang Jian. 2017. A physically based two-dimensional seamless reflectivity mosaic for radar QPE in the MRMS system [J]. Journal of Hydrometeorology, 18(5): 1327−1340. doi: 10.1175/JHM-D-16-0197.1
    [27] Qi Youcun, Zhang Jian, Kaney B, et al. 2014. Improving WSR-88D radar QPE for orographic precipitation using profiler observations [J]. Journal of Hydrometeorology, 15(3): 1135−1151. doi: 10.1175/JHM-D-13-0131.1
    [28] Qi Youcun, Martinaitis S, Zhang Jian, et al. 2016. A real-time automated quality control of hourly rain gauge data based on multiple sensors in MRMS system [J]. Journal of Hydrometeorology, 17(6): 1675−1691. doi: 10.1175/JHM-D-15-0188.1
    [29] Salman A M, Li Yue. 2018. Flood risk assessment, future trend modeling, and risk communication: A review of ongoing research [J]. Natural Hazards Review, 19(3): 04018011. doi: 10.1061/(ASCE)NH.1527-6996.0000294
    [30] 师春香, 潘旸, 谷军霞, 等. 2019. 多源气象数据融合格点实况产品研制进展 [J]. 气象学报, 77(4): 774−783. doi: 10.11676/qxxb2019.043

    Shi Chunxiang, Pan Yang, Gu Junxia, et al. 2019. A review of multi-source meteorological data fusion products [J]. Acta Meteorologica Sinica (in Chinese), 77(4): 774−783. doi: 10.11676/qxxb2019.043
    [31] 孙跃, 肖辉, 杨慧玲, 等. 2021. 基于遥感数据光流场的2021年郑州“7·20”特大暴雨动力条件和水凝物输送特征分析 [J]. 大气科学, 45(6): 1384−1399. doi: 10.3878/j.issn.1006-9895.2109.21155

    Sun Yue, Xiao Hui, Yang Huiling, et al. 2021. Analysis of dynamic conditions and hydrometeor transport of Zhengzhou superheavy rainfall event on 20 July 2021 based on optical flow field of remote sensing data [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(6): 1384−1399. doi: 10.3878/j.issn.1006-9895.2109.21155
    [32] Villarini G, Mandapaka P V, Krajewski W F, et al. 2008. Rainfall and sampling uncertainties: A rain gauge perspective [J]. J. Geophys. Res., 113(D11): D11102. doi: 10.1029/2007JD009214
    [33] 王家华. 1999. 克里金地质绘图技术: 计算机的模型和算法 [M]. 北京: 石油工业出版社. Wang Jiahua. 1999. Kriging Geological Mapping Technique: Computer Models and Algorithms [M]. Beijing: Petroleum Industry Press (in Chinese).
    [34] Xie Pingping, Xiong Anyuan. 2011. A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses [J]. J. Geophys. Res., 116(D21): D21106. doi: 10.1029/2011JD016118
    [35] Xie Pingping, Chen Mingyue, Yang Song, et al. 2007. A gauge-based analysis of daily precipitation over East Asia [J]. Journal of Hydrometeorology, 8(3): 607−626. doi: 10.1175/JHM583.1
    [36] 许时光, 牛铮, 沈艳, 等. 2014. CMORPH卫星降水数据在中国区域的误差特征研究 [J]. 遥感技术与应用, 29(2): 189−194. doi: 10.11873/j.issn.1004-0323.2014.2.0189

    Xu Shiguang, Niu Zheng, Shen Yan, et al. 2014. A research into the characters of CMORPH remote sensing precipitation error in China [J]. Remote Sensing Technology and Application (in Chinese), 29(2): 189−194. doi: 10.11873/j.issn.1004-0323.2014.2.0189
    [37] 杨洁帆, 胡向峰, 雷恒池, 等. 2021. 太行山东麓层状云微物理特征的飞机观测研究 [J]. 大气科学, 45(1): 88−106. doi: 10.3878/j.issn.1006-9895.2004.19202

    Yang Jiefan, Hu Xiangfeng, Lei Hengchi, et al. 2021. Airborne observations of microphysical characteristics of stratiform cloud over eastern side of Taihang mountains [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(1): 88−106. doi: 10.3878/j.issn.1006-9895.2004.19202
    [38] 姚亚庆. 2016. 1950–2015年我国农业气象灾害时空特征研究 [D]. 西北农林科技大学硕士学位论文. Yao Yaqing. 2016. The spatial and temporal characteristics of argo-meteorological disasters during 1950–2015 in China [D]. M. S. thesis (in Chinese), Northwest A & F University.
    [39] Yilmaz K K, Adler R F, Tian Yudong, et al. 2010. Evaluation of a satellite-based global flood monitoring system [J]. Int. J. Remote Sens., 31(14): 3763−3782. doi: 10.1080/01431161.2010.483489
    [40] Yu Jingjing, Li Xiaofeng, Lewis E, et al. 2020. UKGrsHP: A UK high-resolution gauge–radar–satellite merged hourly precipitation analysis dataset [J]. Climate Dyn., 54(19): 2919−2940. doi: 10.1007/s00382-020-05144-2
    [41] Zhang Jian, Howard K, Langston C, et al. 2011. National mosaic and multi-sensor QPE (NMQ) system: Description, results, and future plans [J]. Bull. Amer. Meteor. Soc., 92(10): 1321−1338. doi: 10.1175/2011BAMS-D-11-00047.1
    [42] Zhang Jian, Qi Youcun, Langston C, et al. 2014. A real-time algorithm for merging radar QPEs with rain gauge observations and orographic precipitation climatology [J]. Journal of Hydrometeorology, 15(5): 1794−1809. doi: 10.1175/JHM-D-13-0163.1
    [43] Zhang Jian, Howard K, Langston C, et al. 2016. Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities [J]. Bull. Amer. Meteor. Soc., 97(4): 621−638. doi: 10.1175/BAMS-D-14-00174.1
    [44] Zhang Wenxia, Zhou Tianjun. 2020. Increasing impacts from extreme precipitation on population over China with global warming [J]. Science Bulletin, 65(3): 243−252. doi: 10.1016/j.scib.2019.12.002
    [45] Zhang Zhe, Qi Youcun, Li Donghuan, et al. 2021. A real-time algorithm to identify convective precipitation adjacent to or within the bright band in the radar scan domain [J]. Journal of Hydrometeorology, 22(5): 1139−1151. doi: 10.1175/JHM-D-20-0005.1
    [46] Zhu Ziwei, Qi Youcun, Cao Qing, et al. 2020. Particle size distribution characteristics within different regions of mature squall-line based on the analysis of global precipitation measurement dual-frequency precipitation radar retrieval [J]. IEEE Geoscience and Remote Sensing Letters, 19: 3500205. doi: 10.1109/LGRS.2020.3019384
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出版历程
  • 收稿日期:  2021-11-02
  • 录用日期:  2022-04-07
  • 网络出版日期:  2022-06-14
  • 刊出日期:  2022-11-24

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