基于对流云/层状云划分的云分析方法改进

陈锋; 董美莹; 冀春晓

doi:10.3878/j.issn.1006-9895.2009.19240

基于对流云/层状云划分的云分析方法改进

doi: 10.3878/j.issn.1006-9895.2009.19240

浙江省气象科学研究所，杭州 310008

基金项目: 国家自然基金重点项目42030610，浙江省基础公益研究计划项目LGF20D050001，浙江省气象科技计划项目2018ZD01、2020ZD07

详细信息

作者简介:
陈锋，男，1982年出生，博士，主要从事数值模式及资料同化研究。E-mail: fchen_zj@163.com

中图分类号: P456.7
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- 被引次数: 0
出版历程
- 收稿日期: 2019-10-30
- 录用日期: 2020-09-21
- 网络出版日期: 2020-09-14
- 刊出日期: 2021-03-18

Improvement of the Cloud Analysis Method Based on Convective–Stratiform Cloud Partition

Zhejiang Institute of Meteorological Sciences, Hangzhou 310008

Funds: Key Projects of National Natural Science Foundation of China (Grant 42030610), Basic Public Welfare Research Program of Zhejiang Province (Grant LGF20D050001), Meteorological Science and Technology Projects of Zhejiang Meteorological Bureau (Grants 2018ZD01, 2020ZD07)

摘要

摘要: 本文基于实况融合降水和雷达反射率因子，采用模糊逻辑法提出了一个新的对流云/层状云判别方法，进而改进了GSI（Gridpoint Statistical Interpolation）同化系统中的云分析方案（简称CUST方案）。以2019年6月19日影响浙江的一次梅雨过程为例，利用WRF（Weather and Forecast Research）模式与GSI同化系统开展了逐小时循环同化试验，分析了CUST方案对降水的模拟改进作用和可能影响过程，并与其他方案进行了对比，探讨了CUST方案的应用效果。结果表明：（1）新提出的CUST方案可较为准确地划分对流云和层状云，以此作为判别因子改进GSI同化系统中的云分析方案切实可行。（2）CUST方案在对流区域采用对流云分析方案，在非对流区域采用层云分析方案，减小了单纯对流云方案在非对流区域的空报现象、以及单纯层云方案在强对流区域的漏报现象，有效提升了短时降水的模拟能力。（3）CUST方案对模式起报初期（6 h甚至3 h内）的改进效果较为明显，且对小雨量级的改进幅度要大于大雨量级。（4）与基于地表感热和潜热通量确定的对流尺度速度作为对流判据的混合云分析方案（简称CSW方案）相比，CUST方案基于实况资料划分的对流云/层状云更为合理，模拟的降水结果占优，说明CUST方案方法有较好的应用前景。
- 云分析 /
- 对流云—层状云划分 /
- 降水 /
- 数值模拟
Abstract: A new convective–stratiform separation technique based on the hourly precipitation obtained from CMPAS (China Meteorological Administration multisource precipitation analysis system) and radar reflectivity mosaics data obtained from CMARMOS (China Meteorological Administration radar mosaic operation system) is presented in this paper. The technique, which is based on fuzzy logic, is developed to improve the cloud analysis scheme in the Gridpoint Statistical Interpolation (GSI) assimilation system (referred to as the CUST scheme). The improved scheme was tested in a severe Mei-yu rain that occurred on 19 June 2019, in Zhejiang Province. Several hourly-cycle assimilation experiments were performed using the WRF (weather research and forecasting) model and the GSI assimilation system to analyze the impact of the new scheme on the precipitation simulation, and the scheme was compared with other schemes. The results showed the following: (1) The new convective–stratiform separation technique accurately separated the convective–stratiform cloud, and it can be used as a discriminating factor to improve the cloud analysis scheme in the GSI assimilation system. (2) The CUST scheme adopted the convective cloud analysis scheme in the convective region and the stratiform cloud analysis scheme in the non-convective region, which reduced the false alarm rate in the simple convective cloud scheme and the underestimation in the simple stratiform cloud scheme; this effectively improved the simulation of short-term precipitation. (3) The CUST scheme showed significant improvement in the initial stage of the model (within 6 hours or even 3 hours), and the improvement of small-level rain was greater than that of heavy rain. (4) Compared with the hybrid cloud analysis scheme (referred to as the CSW scheme) based on the convective scale velocity determined by the surface sensible heat and latent heat fluxes, the CUST scheme showed a more reasonable result for the convective–stratiform cloud partition and precipitation simulation, which indicates a good application prospect.
- Cloud analysis /
- Convective–stratiform partition /
- Precipitation /
- Numerical simulation

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图 1 2019年5～7月基于对流云/层状云划分得到的（a）CMPAS降水量、（c）雷达反射率的概率密度，（b）CMPAS降水量、（d）雷达反射率的累积概率密度

Figure 1. Probability density function (PDF) of (a) CMPAS (China Meteorological Administration multisource precipitation analysis system) precipitation and (c) radar reflectivity, and accumulated PDF of (b) CMPAS precipitation and (d) radar reflectivity obtained from the separation of the convective and cloud stratiform cloud from May to July 2019

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图 2 基于函数T的识别参数（a）CMPAS降水量、（b）雷达反射率的模糊基函数

Figure 2. Fuzzy setting based on function T for the separation parameters: (a) CMPAS precipitation; (b) radar reflectivity

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图 3 浙江快速更新同化系统的模拟区域，彩色阴影表示地形高度（单位：m）

Figure 3. Simulation area of Zhejiang WRF-ADAS (Weather and Forecast Research, ARPS (Advanced Regional Prediction System) Data Assimilation System) rapid refresh system, shadings represent terrain height (units: m)

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图 4 循环同化流程示意图

Figure 4. Flowchart of the cycling assimilation system

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图 5 2019年6月19日09时根据（a）CMPAS降水方法、（b）雷达反射率方法、（c）模糊逻辑方法划分的对流云占比分布。黑点表示观测降水量大于20 mm h⁻¹的站点

Figure 5. Proportions of convective cloud according to (a) CMPAS precipitation method, (b) radar reflectivity method, (c) fuzzy logical method at 0900 BJT (Beijing time) 19 June 2019. Black points denote the observation stations with precipitation exceeding 20 mm h⁻¹

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图 6 不同云分析试验方法分析得到的2019年6月19日09时的云冰、云水含量垂直累积量的水平分布（单位：kg m⁻²）：（a）ST试验；（b）CU试验；（c）CUST试验

Figure 6. Horizontal distributions (units: kg m⁻²) of vertically integrated cloud ice content and cloud water content from different cloud analysis experiments at 0900 BJT 19 June 2019: (a) Experiment ST (stratiform cloud analysis); (b) experiment CU (convective cloud analysis); (c) experiment CUST (improved hybrid cloud analysis)

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图 7 2019年6月19日09时不同云分析试验方法分析得到的格点平均云水（q_c）质量混合比和云冰（q_i）质量混合比的垂直分布（单位: g kg⁻¹）

Figure 7. Vertical distributions (units: g kg⁻¹) of grid-averaged cloud ice (q_i) mass mixing ratio and cloud water (q_c) mass mixing ratio obtained from different cloud analysis methods at 0900 BJT 19 June 2019

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图 8 2019年6月19日10时的1 h累计降水量（单位：mm）：（a）ST试验；（b）CU试验；（c）CUST试验；（d）观测；（e）CUST试验与ST试验的差异；（f）CUST试验与CU试验的差异

Figure 8. 1-h accumulated precipitations (units: mm) at 1000 BJT 19 June 2019: (a) Experiment ST run; (b) experiment CU run; (c) experiment CUST run; (d) observation; (e) experiment CUST run minus experiment ST run; (f) experiment CUST run minus experiment CU run

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图 9 2019年6月19日09～12时ST试验、CU试验、CUST试验模拟的逐10 min累计降水量（a）≥0.1 mm、（b）≥3.0 mm的ETS评分

Figure 9. ETS (Equitable threat score) for 10-min accumulated precipitation (a) ≥0.1 mm, (b) ≥3.0 mm from experiments ST, CU, and CUST simulations from 0900 BJT to 1200 BJT 19 June 2019

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图 10 2019年6月19日08～14时的逐小时循环试验中（a）CUST方案分析得到的对流云格点数目占总格点数目的比例，ST、CU、CUST三种方案分析的（b）云水质量混合比、（c）云冰质量混合比垂直累积的模式内层区域平均

Figure 10. (a) Percentage of convective cloud grids in total cloud grids obtained from the experiment CUST, vertically integrated (b) cloud water (q_c) mass mixing ratio, (c) cloud ice (q_i) mass mixing ratio obtained from hourly cycling experiments ST, CU, and CUST averaged in the inner model domain from 0800 BJT to 1400 BJT 19 June 2019

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图 11 2019年6月19日08～14时逐小时循环试验中ST、CU、CUST方案模拟的1 h累计降水量（P）平均的（a）ETS评分（P≥0.1 mm）、（b）空报率（P≥0.1 mm）、（c）ETS评分（P≥3.0 mm）、（d）FAR空报率（P≥3.0 mm）

Figure 11. Averaged (a) ETS (1-h accumulated precipitation P≥0.1 mm), (b) FAR (false alarm, P≥0.1 mm), (c) ETS (P≥3.0 mm), (d) false alarm (P≥3.0 mm) obtained from hourly cycling experiments ST, CU, and CUST from 0800 BJT to 1400 BJT 19 June 2019

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图 12 2019年6月19日09时（a）上一次循环模式预报的潜热（单位：W m⁻²），（b）上一次循环模式预报的感热（单位：W m⁻²），采用（c）CSW方案、（d）CUST方案分析得到的对流云所占比例分布。图c、d中的黑点表示观测降水量大于20 mm h⁻¹的站点

Figure 12. (a) Latent heat fluxes (units: W m⁻²) from the last cycle, (b) sensible heat fluxes (units: W m⁻²) from the last cycle, proportions of convective cloud obtained from (c) the experiment CSW (Convective-scale velocity) and (d) the experiment CUST at 0900 BJT 19 June 2019. In Figs. c and d, black points denote observation stations with precipitation exceeding 20 mm h⁻¹

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图 13 2019年6月19日09时（a）雷达反射率（单位：dBZ），（b）基于CSW方案、（c）基于CUST方案分析得到的云水、云冰含量垂直累积的水平分布（单位：kg m⁻²）。直线AB、CD分别表示雷达回波最强区域（直线AB）及其近似垂向区域（直线CD）

Figure 13. (a) Radar reflectivity (units: dBZ), horizontal distributions (units: kg m⁻²) of vertically integrated cloud ice and cloud water obtained from (b) the experiment CSW, (c) the experiment CUST at 0900 BJT 19 June 2019. Lines AB and CD represent the strongest radar echo region (line AB) and its approximate vertical region (line CD), respectively

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图 14 2019年6月19日09时沿图13中直线AB（左）和直线CD（右）的垂直剖面：（a、d）观测的雷达反射率因子（填色，单位：dBZ）；（b、e）CSW方案、（c、f）CUST方案分析的反射率因子（填色，单位：dBZ）、云水含量（蓝色等值线，单位：g kg⁻¹）、云冰含量（黑色等值线，单位：g kg⁻¹）

Figure 14. Vertical cross section along the line AB (left) and line CD (right) in Fig. 13 at 0900 BJT 19 June 2019: (a, d) Observed radar reflectivity (shadings, units: dBZ); radar reflectivity (shadings, units: dBZ), cloud water content (blue contours, units: g kg⁻¹), and cloud ice content (black contours, units: g kg⁻¹) obtained from (b, e) the experiment CSW, (c, f) the experiment CUST

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图 15 2019年6月19日10时1 h累计降水量（单位：mm）：（a）CSW试验；（b）CUST试验；（c）观测；（d）CUST试验与CSW试验的差异

Figure 15. 1-hourly accumulated precipitations (units: mm) at 1000 BJT 19 June 2019: (a) Experiment CSW run; (b) experiment CUST run; (c) observation; (d) experiment CUST run minus experiment CSW run

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图 16 2019年6月19日09～12时CSW试验和CUST试验模拟的逐10 min累计降水量（≥0.1 mm）的ETS评分和FAR评分

Figure 16. Scores of ETS and FAR (false alarm) for the 10-min accumulated precipitation (≥0.1 mm) of experiment CSW run and experiment CUST run from 0900 BJT to 1200 BJT 19 June 2019

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表 1 数值试验配置方案

Table 1. Configuration schemes of experiments

试验组	试验名称	云分析方案	对流判别方法	备注
第一组	ST	RUC层云方案	无	对比三个方案，分析改进后的混合云分析方法对模拟结果的影响
	CU	ARPS对流云方案	无
	CUST	混合云分析方案	基于CMPAS降水和雷达反射率的模糊逻辑法
第二组	CSW	混合云分析方案	基于地面感热和潜热通量的对流尺度垂直速度法	对比两个方案，分析不同判别方法对模拟结果的影响
第二组	CUST	混合云分析方案	基于CMPAS降水和雷达反射率的模糊逻辑法	对比两个方案，分析不同判别方法对模拟结果的影响

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表 2 对流云/层状云划分方法检验统计

Table 2. Test statistics of the division method for convective cloud and stratiform cloud

划分方法	准确率	空报率	漏报率
CMPAS降水方法	0.94	0.00	0.73
雷达反射率方法	0.92	0.52	0.29
模糊逻辑方法	0.95	0.09	0.56

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参考文献(51)

[1]	Albers S C, McGinley J A, Birkenheuer D L, et al. 1996. The Local Analysis and Prediction System (LAPS): Analyses of clouds, precipitation, and temperature [J]. Wea. Forecasting, 11(3): 273−287. doi:10.1175/1520-0434(1996)011<0273:TLAAPS>2.0.CO;2
[2]	Alexander G D, Cotton W R. 1998. The use of cloud-resolving simulations of mesoscale convective systems to build a mesoscale parameterization scheme [J]. J. Atmos. Sci., 55(12): 2137−2161. doi:10.1175/1520-0469(1998)055<2137:TUOCRS>2.0.CO;2
[3]	Alexander C R, Weygandt S S, Smirnova T G, et al. 2010. High Resolution Rapid Refresh (HRRR): Recent enhancements and evaluation during the 2010 convective season [C]//25th Conference on Severe Local Storms. Denver: American Meteorological Society, 2010.
[4]	Benjamin S G, Dévényi D, Weygandt S S, et al. 2004. An hourly assimilation–forecast cycle: The RUC [J]. Mon. Wea. Rev., 132(2): 495−518. doi:10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2
[5]	Benjamin S G, Weygandt S S, Brown J M, et al. 2007. From the radar enhanced RUC to the WRF-based Rapid Refresh [C]//18th Conference on Numerical Weather Prediction. Park City: American Meteorology Society.
[6]	Caniaux G, Redelsperger J L, Lafore J P. 1994. A numerical study of the stratiform region of a fast-moving squall line. Part I: General description and water and heat budgets [J]. J. Atmos. Sci., 51(14): 2046−2074. doi:10.1175/1520-0469(1994)051<2046:ANSOTS>2.0.CO;2
[7]	曹俊武, 刘黎平, 葛润生. 2005. 模糊逻辑法在双线偏振雷达识别降水粒子相态中的研究 [J]. 大气科学, 29(5): 827−836. doi: 10.3878/j.issn.1006-9895.2005.05.15 Cao J W, Liu L P, Ge R S. 2005. A study of fuzzy logic method in classification of hydrometeors based on polarimetric radar measurement [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 29(5): 827−836. doi: 10.3878/j.issn.1006-9895.2005.05.15
[8]	Chen F, Dudhia J. 2001. Coupling an advanced land surface–hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity [J]. Mon. Wea. Rev., 129(4): 569−585. doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2
[9]	陈敏, 范水勇, 郑祚芳, 等. 2011. 基于BJ-RUC系统的临近探空及其对强对流发生潜势预报的指示性能初探 [J]. 气象学报, 69(1): 181−194. doi: 10.11676/qxxb2011.016 Chen M, Fan S Y, Zheng Z F, et al. 2011. The performance of the proximity sounding based on the BJ-RUC system and its preliminary implementation in the convective potential forecast [J]. Acta Meteor. Sinica (in Chinese), 69(1): 181−194. doi: 10.11676/qxxb2011.016
[10]	陈葆德, 王晓峰, 李泓, 等. 2013. 快速更新同化预报的关键技术综述 [J]. 气象科技进展, 3(2): 29−35. doi: 10.3969/j.issn.2095-1973.2013.02.003 Chen B D, Wang X F, Li H, et al. 2013. An overview of the key techniques in rapid refresh assimilation and forecast [J]. Adv. Meteor. Sci. Technol. (in Chinese), 3(2): 29−35. doi: 10.3969/j.issn.2095-1973.2013.02.003
[11]	程兴宏, 刘瑞霞, 申彦波, 等. 2014. 基于卫星资料同化和LAPS-WRF模式系统的云天太阳辐射数值模拟改进方法 [J]. 大气科学, 38(3): 577−589. doi: 10.3878/j.issn.1006-9895.2013.13159 Cheng X H, Liu R X, Shen Y B, et al. 2014. Improved method of solar radiation simulation on cloudy days with LAPS-WRF model system based on satellite data assimilation [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 38(3): 577−589. doi: 10.3878/j.issn.1006-9895.2013.13159
[12]	Chin H N S. 1994. The impact of the ice phase and radiation on a midlatitude squall line system [J]. J. Atmos. Sci., 51(22): 3320−3343. doi:10.1175/1520-0469(1994)051<3320:TIOTIP>2.0.CO;2
[13]	Churchill D D, Houze Jr R A. 1984. Development and structure of winter monsoon cloud clusters on 10 December 1978 [J]. J. Atmos. Sci., 41(6): 933−960. doi:10.1175/1520-0469(1984)041<0933:DASOWM>2.0.CO;2
[14]	Donner L J. 1988. An initialization for cumulus convection in numerical weather prediction models [J]. Mon. Wea. Rev., 116(2): 377−385. doi:10.1175/1520-0493(1988)116<0377:AIFCCI>2.0.CO;2
[15]	Ferrier B S. 1994. A double-moment multiple-phase four-class bulk ice scheme. Part I: Description [J]. J. Atmos. Sci., 51(2): 249−280. doi:10.1175/1520-0469(1994)051<0249:ADMMPF>2.0.CO;2
[16]	Hong S Y, Noh Y, Dudhia J. 2006. A new vertical diffusion package with an explicit treatment of entrainment processes [J]. Mon. Wea. Rev., 134(9): 2318−2341. doi: 10.1175/MWR3199.1
[17]	Houze Jr R A. 1997. Stratiform precipitation in regions of convection: A meteorological paradox? [J]. Bull. Amer. Meteor. Soc., 78(10): 2179−2196. doi:10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2
[18]	胡金磊, 郭学良. 2013. 基于雷达资料的云分析在冰雹云短时预报中的应用 [J]. 气象科技, 41(4): 682−689. doi: 10.3969/j.issn.1671-6345.2013.04.016 Hu J L, Guo X L. 2013. Application of cloud analysis with radar data in hail cloud nowcasting [J]. Meteorological Science and Technology (in Chinese), 41(4): 682−689. doi: 10.3969/j.issn.1671-6345.2013.04.016
[19]	Hu M, Xue M, Brewster K. 2006. 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part I: Cloud analysis and its impact [J]. Mon. Wea. Rev., 134(2): 675−698. doi: 10.1175/MWR3092.1
[20]	Hu M, Shao H, Stark D, et al. 2015. Gridpoint statistical interpolation advanced user’ s guide, version 3.4.0.0 [EB/OL]. Developmental Testbed Center, (2015-07). http://www.dtcenter.org/com-GSI/users/docs/users_guide/AdvancedGSIUserGuide_v3.4.0.0.pdf [2020-05-07].
[21]	Iacono M J, Delamere J S, Mlawer E J, et al. 2008. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models [J]. J. Geophys. Res. Atmos., 113(D13): D13103. doi: 10.1029/2008JD009944
[22]	Ikeda K, Rasmussen R, Liu C H, et al. 2010. Simulation of seasonal snowfall over Colorado [J]. Atmos. Res., 97(4): 462−477. doi: 10.1016/j.atmosres.2010.04.010
[23]	Jiménez P A, Dudhia J, González-Rouco J F, et al. 2012. A revised scheme for the WRF surface layer formulation [J]. Mon. Wea. Rev., 140(3): 898−918. doi: 10.1175/MWR-D-11-00056.1
[24]	Kessler E. 1969. On the distribution and continuity of water substance in atmospheric circulations [M]//Kessler E. On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Boston: American Meteorological Society, 84. doi: 10.1007/978-1-935704-36-2_1
[25]	Lang S, Tao W K, Simpson J, et al. 2003. Modeling of convective–stratiform precipitation processes: Sensitivity to partitioning methods [J]. J. Appl. Meteor., 42(4): 505−527. doi:10.1175/1520-0450(2003)042<0505:MOCSPP>2.0.CO;2
[26]	雷蕾, 孙继松, 王国荣, 等. 2012. 基于中尺度数值模式快速循环系统的强对流天气分类概率预报试验 [J]. 气象学报, 70(4): 752−765. doi: 10.11676/qxxb2012.061 Lei L, Sun J S, Wang G R, et al. 2012. An experimental study of the summer convective weather categorical probability forecast based on the rapid updated cycle system for the Beijing area (BJ-RUC) [J]. Acta Meteor. Sinica (in Chinese), 70(4): 752−765. doi: 10.11676/qxxb2012.061
[27]	李佳, 陈葆德, 黄伟, 等. 2017. 对流尺度数值预报中的云物理初始化方法改进及个例试验 [J]. 气象学报, 75(5): 771−783. doi: 10.11676/qxxb2017.059 Li J, Chen B D, Huang W, et al. 2017. Cloud physics initialization for convection scale NWP: Scheme improvements and a case study [J]. Acta Meteorologica Sinica (in Chinese), 75(5): 771−783. doi: 10.11676/qxxb2017.059
[28]	Lin Y, Ray P S, Johnson K W. 1993. Initialization of a modeled convective storm using Doppler radar derived fields [J]. Mon. Wea. Rev., 121(10): 2757−2775. doi:10.1175/1520-0493(1993)121<2757:IOAMCS>2.0.CO;2
[29]	潘旸, 沈艳, 宇婧婧, 等. 2015. 基于贝叶斯融合方法的高分辨率地面—卫星—雷达三源降水融合试验 [J]. 气象学报, 73(1): 177−186. doi: 10.11676/qxxb2015.010 Pan Y, Shen Y, Yu J J, et al. 2015. An experiment of high-resolution gauge–radar–satellite combined precipitation retrieval based on the Bayesian merging method [J]. Acta Meteorological Sinica (in Chinese), 73(1): 177−186. doi: 10.11676/qxxb2015.010
[30]	潘旸, 谷军霞, 徐宾, 等. 2018. 多源降水数据融合研究及应用进展 [J]. 气象科技进展, 8(1): 143−152. doi: 10.3969/j.issn.2095-1973.2018.01.019 Pan Y, Gu J X, Xu B, et al. 2018. Advances in multi-source precipitation merging research [J]. Advances in Meteorological Science and Technology (in Chinese), 8(1): 143−152. doi: 10.3969/j.issn.2095-1973.2018.01.019
[31]	邱金晶, 陈锋, 董美莹, 等. 2015. 浙江省快速更新同化系统的建立与检验评估 [J]. 气象科技进展, 5(6): 6−12. doi: 10.3969/j.issn.2095-1973.2015.06.001 Qiu J J, Chen F, Dong M Y, et al. 2015. Establishment and evaluation of Zhejiang WRF-ADAS rapid refresh system [J]. Advances in Meteorological Science and Technology (in Chinese), 5(6): 6−12. doi: 10.3969/j.issn.2095-1973.2015.06.001
[32]	屈右铭, 陆维松, 蔡荣辉, 等. 2010. GRAPES-Meso云分析系统的设计与试验 [J]. 气象, 36(10): 37−45. doi: 10.7519/j.issn.1000-0526.2010.10.006 Qu Y M, Lu W S, Cai R H, et al. 2010. Design and experiment of GRAPES-Meso cloud analysis system [J]. Meteor. Mon. (in Chinese), 36(10): 37−45. doi: 10.7519/j.issn.1000-0526.2010.10.006
[33]	盛春岩, 浦一芬, 高守亭. 2006. 多普勒天气雷达资料对中尺度模式短时预报的影响 [J]. 大气科学, 30(1): 93−107. doi: 10.3878/j.issn.1006-9895.2006.01.08 Sheng C Y, Pu Y F, Gao S T. 2006. Effect of Chinese Doppler radar data on nowcasting output of mesoscale model [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 30(1): 93−107. doi: 10.3878/j.issn.1006-9895.2006.01.08
[34]	Skamarock W C, Klemp J B, Dudhia J, et al. 2008. A description of the advanced research WRF version 3 [R]. NCAR Technical Note, NCAR/TN-475+STR.
[35]	Steiner M, Houze Jr R A, Yuter S E. 1995. Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data [J]. J. Appl. Meteor., 34(9): 1978−2007. doi:10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2
[36]	Tao W K, Simpson J. 1989. Modeling study of a tropical squall type convective line [J]. J. Atmos. Sci., 46(2): 177−202. doi:10.1175/1520-0469(1989)046<0177:MSOATS>2.0.CO;2
[37]	Tao W K, Simpson J, Sui C H, et al. 1993. Heating, moisture, and water budgets of tropical and midlatitude squall lines: Comparisons and sensitivity to longwave radiation [J]. J. Atmos. Sci., 50(5): 673−690. doi:10.1175/1520-0469(1993)050<0673:HMAWBO>2.0.CO;2
[38]	Tao W K, Lang S, Simpson J, et al. 2000. Vertical profiles of latent heat release and their retrieval for TOGA COARE convective systems using a cloud resolving model, SSM/I, and ship-borne radar data [J]. J. Meteor. Soc. Japan, 78(4): 333−355. doi: 10.2151/jmsj1965.78.4_333
[39]	Thompson G, Rasmussen R M, Manning K. 2004. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis [J]. Mon. Wea. Rev., 132(2): 519−542. doi:10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2
[40]	Thompson G, Field P R, Rasmussen R M, et al. 2008. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization [J]. Mon. Wea. Rev., 136(12): 5095−5115. doi: 10.1175/2008MWR2387.1
[41]	王洪, 王东海, 万齐林. 2015. 多普勒雷达资料同化在“7.21”北京特大暴雨个例中的应用 [J]. 气象学报, 73(4): 679−696. doi: 10.11676/qxxb2015.048 Wang H, Wang D H, Wan Q L. 2015. Application of assimilating Doppler weather radar data in the “7.21” Beijing excessive storm [J]. Acta Meteor. Sinica (in Chinese), 73(4): 679−696. doi: 10.11676/qxxb2015.048
[42]	王瑾, 刘黎平. 2009. CINRAD/CD雷达反射率因子同化对中尺度数值模式云微物理量场调整的分析 [J]. 高原气象, 28(1): 173−185. Wang J, Liu L P. 2009. Assimilation of microphysical adjustments using reflectivity of CINRAD/CD Doppler radar for mesoscale model [J]. Plateau Meteorology (in Chinese), 28(1): 173−185.
[43]	Weygandt S S, Benjamin S G, Dévényi D, et al. 2006. Cloud and hydrometeor analysis using metar, radar, and satellite data within the RUC/Rapid-Refresh model [C]//12th Conference on Aviation Range and Aerospace Meteorology. Atlanta: American Meteorological Society.
[44]	肖艳姣, 刘黎平. 2007. 三维雷达反射率资料用于层状云和对流云的识别研究 [J]. 大气科学, 31(4): 645−654. doi: 10.3878/j.issn.1006-9895.2007.04.09 Xiao Y J, Liu L P. 2007. Identification of stratiform and convective cloud using 3D radar reflectivity data [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 31(4): 645−654. doi: 10.3878/j.issn.1006-9895.2007.04.09
[45]	Xu K W. 1995. Partitioning mass, heat, and moisture budgets of explicitly simulated cumulus ensembles into convective and stratiform components [J]. J. Atmos. Sci., 52(5): 551−573. doi:10.1175/1520-0469(1995)052<0551:PMHAMB>2.0.CO;2
[46]	许娈, 董美莹, 陈锋. 2017. 基于逐时降水站点资料空间插值方法对比研究 [J]. 气象与环境学报, 33(1): 34−43. doi: 10.3969/j.issn.1673-503X.2017.01.005 Xu L, Dong M Y, Chen F. 2017. Comparison study of spatial interpolation methods based on hourly precipitation data from automatic weather stations [J]. Journal of Meteorology and Environment (in Chinese), 33(1): 34−43. doi: 10.3969/j.issn.1673-503X.2017.01.005
[47]	Xue M, Wang D H, Gao J D, et al. 2003. The advanced regional prediction system (ARPS), storm-scale numerical weather prediction and data assimilation [J]. Meteor. Atmos. Phys., 82(1–4): 139−170. doi: 10.1007/s00703-001-0595-6
[48]	薛谌彬, 陈娴, 吴俞, 等. 2017. 雷达资料同化在局地强对流预报中的应用 [J]. 大气科学, 41(4): 673−690. doi: 10.3878/j.issn.1006-9895.1608.15288 Xue C B, Chen X, Wu Y, et al. 2017. Application of radar data assimilation in local severe convective weather forecast [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 41(4): 673−690. doi: 10.3878/j.issn.1006-9895.1608.15288
[49]	Zhang J. 1999. Moisture and diabatic initialization based on radar and satellite observations [D]. Ph. D. dissertation, University of Oklahoma, 194pp.
[50]	Zhang J, Carr F H, Brewster K. 1998. ADAS cloud analysis [C]//Preprints 12th Conference on Numerical Weather Prediction. Phoenix: American Meteorological Society, 185–188.
[51]	朱立娟. 2012. GRAPES短临预报的云初始场形成与雷达VAD质控的关键技术研究 [D]. 中国气象科学研究院博士学位论文, 145pp. Zhu L J. 2012. The key technology research on cloud initial field and radar VAD quality control for GRAPES nowcasting [D]. Ph. D. dissertation (in Chinese), Chinese Academy of Meteorological Sciences, 145pp.