基于雷达与卫星的对流触发观测研究和临近预报技术进展

doi:10.11867/j.issn.1001-8166.2019.12.1273

地球科学进展 ›› 2019, Vol. 34 ›› Issue (12): 1273 -1287. doi: 10.11867/j.issn.1001-8166.2019.12.1273

黄亦鹏 ^1, ²(

),李万彪 ²,赵玉春 ¹,白兰强 ³

^1.厦门市气象局海峡气象开放实验室，福建厦门 361012
^2.北京大学物理学院大气与海洋科学系，北京 100871
^3.中山大学大气科学学院，广东广州 510275

收稿日期:2019-09-19 修回日期:2019-11-03 出版日期:2019-12-10
基金资助:
国家自然科学基金项目“海峡西岸前汛期暖区MCSs形成前的对流云发展特征研究”(41905049);福建省气象局开放式基金项目“福建对流触发前边界层辐合线和积云发展特征”(2019KX01)

A Review of Radar- and Satellite-based Observational Studies and Nowcasting Techniques on Convection Initiation

Yipeng Huang ^1, ²( ),Wanbiao Li ²,Yuchun Zhao ¹,Lanqiang Bai ³

^1.Laboratory of Straits Meteorology, Xiamen Meteorological Bureau, Xiamen 361012, China
^2.Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
^3.School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China

Received:2019-09-19 Revised:2019-11-03 Online:2019-12-10 Published:2020-02-12
About author:Huang Yipeng (1990-), male, Longhai City, Fujian Province, Engineer. Research areas include remote sensing of atmosphere and mesoscale meteorology. E-mail: harrisonyp@163.com
Supported by:
the National Natural Science Foundation of China “Development characteristics of convective clouds before the formation of warm-sector MCSs during the presummer rainy season over the west side of the Taiwan Strait”(41905049);The Open Foundation of Fujian Meteorological Bureau “Characteristics of boundary-layer convergence lines and cumulus cloud growth before the convection initiation processes in Fujian Province”(2019KX01)

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对流触发（CI）一直是强对流天气预报中至关重要而又充满挑战的环节。在CI发生之前，高时空分辨率的天气雷达和静止气象卫星常能够观测识别到边界层辐合线和积云快速发展等用于评估CI发生条件的前兆信号，从而为定时定点的CI临近预报提供有力观测支撑。基于此，综述了雷达和卫星在CI观测研究和临近预报技术上的运用进展。首先回顾了近40年来CI观测研究中的里程碑工作以及最新的研究进展，结合CI发生条件梳理了CI研究方式和内容及其随雷达和卫星探测能力提升而呈现的发展趋势。进而介绍了较为成熟的3种基于雷达和卫星观测的CI业务临近预报技术在国际范围内的发展运用。最后提出雷达和卫星观测在CI研究和预报运用中需要进一步解决的问题。

关键词: 对流触发, 天气雷达, 静止气象卫星, 临近预报

Convection often produces severe weather which causes a great loss to human lives and properties. Precisely predicting the convection initiation process is crucial but challenging in operational convection nowcasting (0~2 h forecasting). Before the radar-defined CI occurring (e.g., the first occurrence of ≥35 dBZ echoes), observations at high spatial and temporal resolutions from weather radars and geostationary meteorological satellites can reveal precursor information such as the boundary-layer convergence lines and the rapid growth of newborn cumulus clouds. These radar- and satellite-observed precursor information are helpful for evaluating the pre-CI conditions and thus nowcasting the accurate CI timing and location. This paper reviewed the current status of radar- and satellite-based CI research and nowcasting techniques. The milestone works and the following studies in the last four decades were summarized to demonstrate how radar and satellite observations can be related to CI occurrence. The objectives and approaches of the CI research advance as the improvement in the capability of radars and were explained satellites. The research progress aids in the development of various CI nowcasting techniques. This paper introduced three well-established techniques that have been put into operational application, namely, ANC system, SATCAST algorithm, and UWCI algorithm. Some scientific issues with respect to radar- and satellite-based CI research and nowcasting were also presented.

Key words: Convection initiation, Weather radars, Geostationary meteorological satellites, Nowcasting.

中图分类号:

P456.7

图1 雷达和卫星观测到的CI前兆信号示意图
（a）雷达和（b）卫星常能够在CI过程产生明显降水（C3阶段）之前观测到边界层辐合线 ^{[

14
]}和新生积云快速发展 ^{[

15
]}，为CI预报提供前兆信息

Fig. 1 Conceptual model of radar- and satellite-observed precursor signals before CI occurring
(a) Radar-observed boundary-layer convergence lines ^{[

14
]} and (b) satellite-observed newborn cloud growth ^{[

15
]} can provide precursor information for the occurrence of radar-defined CI processes like C3 stage in (b)

图2 不同地区基于雷达气候学统计的CI空间分布特征
（a）欧洲中部 ^{[

20
]}、（b）美国东南部 ^{[

16
]}和（c）美国中部 ^{[

22
]}；（a）中用白点表示CI位置分布；（b）和（c）中用空间密度表示CI位置分布，其中（b）中色调越亮表示CI发生密度越高；（c）中黄色等值线表示密度超过0.009的区域

Fig. 2 Radar-based CI climatology across different regions
(a) Central Europe ^{[

20
]}, (b) Southeast U.S. ^{[

16
]} and (c) Central U.S. ^{[

22
]}； The spatial distribution of CI locations is shown by white dots in (a) and by point density in (b) and (c), with brighter color indicating more frequent CI occurrence in (b) and the yellow contour in (c) indicating density higher than 0.009 points per km ²

图3 2013年6月4日华北河套地区引发对流的边界层辐合线在不同阶段的雷达反射率图^{[

47
]}
（a）0818 LST，形成阶段（起始10 km）;（b）1358 LST，最大长度阶段；（c）1404 LST，对流触发阶段（首个大于等于10 dBZ回波）；（d）1555 LST；图中红圈给出了辐合线位置，白圈给出了辐合线引发首个大于等于10 dBZ回波的位置；十字灰线交点为河套地区临河雷达站点位置；从内到外的灰色圆圈表示离雷达50 km和100 km的位置

Fig. 3 Radar composite reflectivity of a convective boundary on 4 June 2013 around the bend of the Yellow River in North China^{[

47
]}
The red circles in (a)~(c) highlight the boundary at its formation (first 10 km), maximum length, and convective initiation stages, respectively； The white circle in (c) highlights the first ≥10 dBZ echo of the first convective storm near the boundary； The central intersections denote the radar site, and the range rings are shown at 50 km intervals in gray

图4 使用静止卫星成像仪研究CI的3种方式
（a）可见光图像法 ^{[

48
]}；（b）单一红外窗区通道法 ^{[

60
]}；（c）多光谱通道法 ^{[

3
]}

Fig. 4 Three ways to study CI based on geostationary imagers
(a) Visible imagery ^{[

48
]}； (b) Only IR-window channel ^{[

60
]}； (c) Multispectral channels ^{[

3
]}

图5 基于MSG卫星总结的最能用于刻画CI过程中3种积云发展特征的关注场^{[

63
]}
上标“a”表示Mecikalski等 ^{[

3
]}工作中8个IR关注场中的6个，另外2个为“13.3-10.7 μm” 和“10.8-μm T _B低于0℃的时间”;“T _B”表示亮温（brightness temperature），“t-min”表示t-min时间趋势，“trispectral”表示(8.7-10.8)-(10.8-12.0) μm

Fig. 5 The top interest fields derived from MSG satellite for describing the three physical attributes of CI-related cumulus clouds^{[

63
]}
The superscript “a” indicates six out of the eight interest fields in Mecikalski and Bedka ^{[

3
]}, and the other two are 13.3-10.7 μm and the timing of a 10.8-μm T _B drop below 0 °C. Here, “T _B” means brightness temperature, “t-min” means the t-min time trend, and “trispectral” means (8.7-10.8)-(10.8-12.0) μm

图6 美国GOES卫星上搭载的IR探测仪Sounder与辐射模式模拟的高光谱探测仪HES的光谱分布对比（据参考文献[ 79 ]修改）
GOES卫星探测仪的光谱分辨率为10~100 cm ^-1，而高光谱探测仪HES的光谱分辨率可达1 cm ^-1量级

Fig. 6 The spectral coverages of the GOES sounder bands and a simulated HES-like instrument with high spectral resolution（modified after reference [ 79 ]）
The GOES sounder is at low spectral resolutions (10~100 cm ^-1), whereas a HES-like instrument could have spectral resolutions on the order of 1 cm ^-1

1	Ziegler C L. Deep convection initiation: State of the science, limits of understanding, and future directions［C］// Preprints, 94th American Meteorological Society Annual Meeting. Atlanta, GA, American Meteorological Society, 2014.
2	Roberts R D, Rutledge S. Nowcasting storm initiation and growth using GOES-8 and WSR-88D data［J］. Weather and Forecasting, 2003, 18: 562-584.
3	Mecikalski J R, Bedka K M. Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery［J］. Monthly Weather Review, 2006, 134: 49-78.
4	Roberts R D, Anderson A R, Nelson E, et al. Impacts of forecaster involvement on convective storm initiation and evolution nowcasting［J］. Weather and Forecasting, 2012, 27(5): 1 061-1 089.
5	Walker J R, MacKenzie Jr W M, Mecikalski J R, et al. An enhanced geostationary satellite-based convective initiation algorithm for 0-2-h nowcasting with object tracking［J］. Journal of Applied Meteorology and Climatology, 2012, 51: 1 931-1 949.
6	Mecikalski J R, Williams J, Jewett C, et al. Probabilistic 0-1-h convective initiation nowcasts that combine geostationary satellite observations and numerical weather prediction model data［J］. Journal of Applied Meteorology and Climatology, 2015, 54: 1 039-1 059.
7	Yu Xiaoding, Zhou Xiaogang, Wang Xiuming. The advances in the nowcasting techniques on thunderstorms and severe convection［J］. Acta Meteorologica Sinica, 2012, 70(3): 311-337.
	俞小鼎, 周小刚, 王秀明．雷暴与强对流临近天气预报技术进展［J］.气象学报, 2012, 70(3):311-337.
8	Kain J S, Coniglio M C, Correia J, et al. A feasibility study for probabilistic convection initiation forecasts based on explicit numerical guidance［J］. Bulletin of the American Meteorological Society, 2013, 94(8): 1 213-1 225.
9	Doswell III C A. The distinction between large scale and mesoscale contribution to severe convection: A case study example［J］. Weather and Forecasting, 1987, 2(1): 3-16.
10	Zhang Xiaoling, Chen Yun, Zhang Tao. Meso-scale convective weather analysis and severe convective weather forecasting［J］. Acta Meteorologica Sinica, 2012, 70(4): 642-654.
	张小玲, 谌芸, 张涛.对流天气预报中的环境场条件分析［J］.气象学报, 2012, 70(4): 642-654.
11	Zhu Yuejia, Xing Rui, Zhu Mingjia, et al. Application and verification of joint probability method in potential forecast for severe convective weather in Anhui Province［J］. Advances in Earth Science, 2019, 34(7):731-746.
	朱月佳, 邢蕊, 朱明佳, 等. 联合概率方法在安徽强对流潜势预报中的应用和检验［J］. 地球科学进展, 2019, 34(7):731-746.
12	Ma Leiming, Bao Xuwei. Research progress on physical parameterization schemes in numerical weather prediction models［J］. Advances in Earth Science, 2017, 32(7):679-687.
	马雷鸣, 鲍旭炜. 数值天气预报模式物理过程参数化方案的研究进展［J］. 地球科学进展, 2017, 32(7):679-687.
13	Huang Yipeng. Statistical Study on Precursor Signals of Convection Initiation in Satellite and Radar Observations［D］. Beijing: Peking University, 2018.
	黄亦鹏. 卫星与雷达上对流触发前兆信号的统计特征［D］.北京:北京大学, 2018.
14	Wakimoto R M, Murphey H V. Analysis of convergence boundaries observed during IHOP_2002［J］. Monthly Weather Review, 2010, 138: 2 737-2 760.
15	Hartung D C, Sieglaff J M, Cronce L M, et al. An intercomparison of UW cloud-top cooling rates with WSR-88D radar data［J］. Weather and Forecasting, 2013, 28: 463-480.
16	Haberlie A M, Ashley W S, Pingel T J. The effect of urbanisation on the climatology of thunderstorm initiation［J］. Quarterly Journal of the Royal Meteorological Society, 2015, 141(688): 663-675.
17	Wilson J W, Schreiber W E. Initiation of convective storms by radar-observed boundary layer convergent lines［J］. Monthly Weather Review, 1986, 114: 2 516-2 536.
18	Snively D V, Gallus W A. Prediction of convective morphology in near-cloud-permitting WRF model simulations［J］. Weather and Forecasting, 2014, 29: 130-149.
19	Dixon M, Wiener G. TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology［J］. Journal of Atmospheric and Oceanic Technology, 1993, 10: 785-797.
20	Weckwerth T M, Wilson J W, Hagen M, et al. Radar climatology of the COPS region［J］. Quarterly Journal of the Royal Meteorological Society, 2011, 137(Suppl.1): 31-41.
21	Wulfmeyer V, Behrendt A, Kottmeier C, et al. The Convective and Orographically‐induced Precipitation Study (COPS): The scientific strategy, the field phase, and research highlights[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(Suppl.1): 3-30.
22	Lock N A, Houston A L. Spatiotemporal distribution of thunderstorm initiation in the US Great Plains from 2005 to 2007［J］. International Journal of Climatology, 2015, 35(13): 4 047-4 056.
23	Fabry F, Meunier V, Treserras B P, et al. On the climatological use of radar data mosaics: Possibilities and challenges［J］. Bulletin of the American Meteorological Society, 2017, 98: 2 135-2 148.
24	Goudenhoofdt E, Delobbe L. Statistical characteristics of convective storms in Belgium derived from volumetric weather radar observations［J］. Journal of Applied Meteorology and Climatology, 2013, 52: 918-934.
25	Kaltenboeck R, Steinheimer M. Radar-based severe storm climatology for Austrian complex orography related to vertical wind shear and atmospheric instability［J］. Atmospheric Research, 2015, 158: 216-230.
26	Peter J R, Manton M J, Potts R J, et al. Radar-derived statistics of convective storms in Southeast Queensland［J］. Journal of Applied Meteorology and Climatology, 2015, 54(10): 1 985-2 008.
27	Han Lei, Yu Xiaoding, Zheng Yongguang, et al. Statistic characteristics of severe convective storm during warm-season in Beijing-Tianjin and its vicinity［J］. Chinese Science Bulletin, 2009, 54(11):1 585-1 590.
	韩雷, 俞小鼎, 郑永光, 等.京津及邻近地区暖季强对流风暴的气候分布特征［J］.科学通报, 2009, 54(11):1 585-1 590.
28	Chen M, Wang Y, Gao F, et al. Diurnal variations in convective storm activity over contiguous North China during the warm season based on radar mosaic climatology［J］. Journal of Geophysical Research: Atmospheres, 2012, 117(D20). DOI:10.1029/2012JD018158. doi: 10.1029/2012JD018158
29	Wang Y, Wang H Q, Han L, et al. Statistical characteristics of unsteady storms in radar observations for the Beijing-Tianjin region［J］. Journal of Applied Meteorology and Climatology, 2015, 54(1): 106-116.
30	Chen X C, Zhao K, Xue M. Spatial and temporal characteristics of warm season convection over Pearl River Delta region, China, based on 3 years of operational radar data［J］. Journal of Geophysical Research: Atmospheres, 2014, 119: 12 447-12 465.
31	Chen X C, Zhao K, Xue M, et al. Radar-observed diurnal cycle and propagation of convection over the Pearl River Delta during Mei-Yu season［J］. Journal of Geophysical Research: Atmospheres, 2015, 120: 12 557-12 575.
32	Bai L, Chen G, Huang L. Image processing of radar mosaics for the climatology of convection initiation in South China［J］. Journal of Applied Meteorology and Climatology, 2019. DOI:10.1175/JAMC-D-19-0081.1. doi: 10.1175/JAMC-D-19-0081.1
33	Meng Z, Yan D, Zhang Y. General features of squall lines in East China［J］. Monthly Weather Review, 2013, 141(5): 1 629-1 647.
34	Zheng L, Sun J, Zhang X, et al. Organizational modes of mesoscale convective systems over central East China［J］. Weather and Forecasting, 2013, 28(5): 1 081-1 098.
35	Weckwerth T M, Parsons D B. A review of convection initiation and motivation for IHOP_2002［J］. Monthly Weather Review, 2006, 134: 5-22.
36	Wilson J W, Weckwerth T M, Vivekanandan J, et al. Boundary layer clear-air radar echoes: Origin of echoes and accuracy of derived winds［J］. Journal of Atmospheric and Oceanic Technology, 1994, 11: 1 184-1 206.
37	Klingle D L, Smith D R, Wolfson M M. Gust front characteristics as detected by Doppler radar［J］. Monthly Weather Review, 1987, 115(5): 905-918.
38	Boyd J G. Observation of two intersecting radar fine lines［J］. Monthly Weather Review, 1965, 93: 188.
39	Koscielny A J, Doviak R J, Rabin R. Statistical considerations in the estimation of divergence from single-Doppler radar and application to prestorm boundary-layer observations［J］. Journal of Applied Meteorology, 1982, 21(2): 197-210.
40	Wilson J W, Mueller C K. Nowcasts of thunderstorm initiation and evolution［J］. Weather and Forecasting, 1993, 8: 113-131.
41	Bluestein H B, Parker S S. Modes of isolated, severe convective storm formation along the dryline［J］. Monthly Weather Review, 1993, 121(5): 1 354-1 372.
42	Fankhauser J C, Crook N A, Tuttle J, et al. Initiation of deep convection along boundary layer convergence lines in a semitropical environment［J］. Monthly Weather Review, 1995, 123: 291-314.
43	Wilson J W, Megenhardt D L. Thunderstorm initiation, organization, and lifetime associated with Florida boundary layer convergence lines［J］. Monthly Weather Review, 1997, 125: 1 507-1 525.
44	Koch S E, Ray C A. Mesoanalysis of summertime convergence zones in central and eastern North Carolina［J］. Weather and Forecasting, 1997, 12: 56-77.
45	Sills D M L, Brook J R, Levy I, et al. Lake breezes in the southern Great Lakes region and their influence during BAQS-Met 2007［J］. Atmospheric Chemistry and Physics, 2011, 11: 7 955-7 973.
46	Alexander L S, Sills D M, Taylor P A. Initiation of convective storms at low-level mesoscale boundaries in southwestern Ontario［J］. Weather and Forecasting, 2018, 33: 583-598.
47	Huang Y, Meng Z, Li W, et al. General features of radar-observed boundary layer convergence lines and their associated convection over a sharp vegetation-contrast area［J］. Geophysical Research Letters, 2019, 46: 2 865-2 873.
48	Purdom J F W. Some uses of high resolution GOES imagery in the mesoscale forecasting of convection and its behavior［J］. Monthly Weather Review, 1976, 104: 1 474-1 483.
49	Purdom J F W. Subjective interpretations of geostationary satellite data for nowcasting[M]// Browning K. Nowcasting. London: Academic Press, 1982: 149-166.
50	Purdom J F W, Marcus K. Thunderstorm trigger mechanisms over the southeast United States［C］// Preprints, 12th Conference. on Severe Local Storms. San Antonio, TX, American Meteorological Society, 1982: 487-488.
51	Weckwerth T M, Murphey H V, Flamant C. et al. An observational study of convection initiation on 12 June 2002 during IHOP_2002［J］. Monthly Weather Review, 2008, 136: 2 283-2 304.
52	Su T, Zhai G. The role of convectively generated gravity waves on convective initiation: A case study［J］. Monthly Weather Review, 2017, 145: 335-359.
53	Bai L, Meng Z, Huang Y, et al. Convection initiation resulting from the interaction between a quasi-stationary dryline and intersecting gust fronts: A case study［J］. Journal of Geophysical Research: Atmospheres, 2019, 124: 2 379-2 396.
54	Bessho K, Date K, Hayashi M, et al. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites［J］. Journal of the Meteorological Society of Japan, 2016, 94: 151-183.
55	Banta R M, Barker S C. Thunderstorm genesis zones in the Colorado Rocky Mountains as determined by traceback of geosynchronous satellite images［J］. Monthly Weather Review, 1987, 115(2): 463-476.
56	Barker S C, Wurman J, Banta R M. Thunderstorm producing terrain features［J］. Bulletin of the American Meteorological Society, 1988, 69(3): 272-277.
57	Lima M A, Wilson J W. Convective storm initiation in a moist tropical environment［J］. Monthly Weather Review, 2008, 136(6): 1 847-1 864.
58	Weckwerth T M, Bennett L J, Jay M L, et al. An observational and modeling study of the processes leading to deep, moist convection in complex terrain［J］. Monthly Weather Review, 2014, 142(8): 2 687-2 708.
59	Zhang M, Meng Z, Huang Y, et al. The mechanism and predictability of an elevated convection initiation event in a weak-lifting environment in central-eastern China［J］. Monthly Weather Review, 2019, 147: 1 823-1 841.
60	Sieglaff J M, Cronce L M, Feltz W F, et al. Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends［J］. Journal of Applied Meteorology and Climatology, 2011, 50: 110-126.
61	Huang Y, Meng Z, Li J, et al. Distribution and variability of satellite-derived signals of isolated convection initiation events over central eastern China［J］. Journal of Geophysical Research: Atmospheres, 2017, 122(11): 11 357-11 373.
62	Senf F, Dietzsch F, Hünerbein A, et al. Characterization of initiation and growth of selected severe convective storms over central Europe with MSG-SEVIRI［J］. Journal of Applied Meteorology and Climatology, 2015, 54(1): 207-224.
63	Mecikalski J R, MacKenzie Jr W M, Koenig M, et al. Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part I: Infrared fields［J］. Journal of Applied Meteorology and Climatology, 2010, 49(3): 521-534.
64	Mecikalski J R, Bedka K M, Paech S J, et al. A statistical evaluation of GOES cloud-top properties for nowcasting convective initiation［J］. Monthly Weather Review, 2008, 136: 4 899-4 914.
65	Siewert C W, Koenig M, Mecikalski J R. Application of Meteosat Second Generation data towards improving the nowcasting of convective initiation［J］. Meteorological Applications, 2010, 17: 442-451.
66	Gambill L D, Mecikalski J R. A satellite-based summer convective cloud frequency analysis over the southeastern United States［J］. Journal of Applied Meteorology and Climatology, 2011, 50(8): 1 756-1 769.
67	Harris R J, Mecikalski J R, MacKenzie W M, et al. The definition of GOES infrared lightning initiation interest fields［J］. Journal of Applied Meteorology and Climatology, 2010, 49(12): 2 527-2 543.
68	Mecikalski J R, Li X, Carey L D, et al. Regional comparison of GOES cloud-top properties and radar characteristics in advance of first-flash lightning initiation［J］. Monthly Weather Review, 2013, 141(1): 55-74.
69	Lensky I M, Rosenfeld D. The time-space exchangeability of satellite retrieved relations between cloud top temperature and particle effective radius［J］. Atmospheric Chemistry and Physics, 2006, 6: 2 887-2 894.
70	Rosenfeld D, Woodley W L, Lerner A, et al. Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase［J］. Journal of Geophysical Research, 2008, 113(D4). DOI:10.1029/2007JD008600. doi: 10.1029/2007JD008600
71	Mecikalski J R, Watts P, Koenig M. Use of meteosat seceond generation optimal cloud analysis fields for understanding physical attributes of growing cumulus clouds［J］. Atmospheric Research, 2011, 102: 175-190.
72	Mecikalski J R, MacKenzie Jr W M, Koenig M, et al. Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part II: Use of visible reflectance［J］. Journal of Applied Meteorology and Climatology, 2010, 49: 2 544-2 558.
73	Cintineo J L, Pavolonis M J, Sieglaff J M, et al. Evolution of severe and nonsevere convection inferred from GOES-derived cloud properties［J］. Journal of Applied Meteorology and Climatology, 2013, 52: 2 009-2 023.
74	Cintineo J L, Pavolonis M J, Sieglaff J M, et al. An empirical model for assessing the severe weather potential of developing convection［J］. Weather and Forecasting, 2014, 29: 639-653.
75	Matthee R, Mecikalski J R. Geostationary infrared methods for detecting lightning-producing cumulonimbus clouds［J］. Journal of Geophysical Research: Atmospheres, 2013, 118: 6 580-6 592.
76	Mecikalski J R, Rosenfeld D, Manzato A. Evaluation of geostationary satellite observations and the development of a 1-2 h prediction model for future storm intensity［J］. Journal of Geophysical Research: Atmospheres, 2016, 121: 6 374-6 392.
77	Menzel W P, Holt F C, Schmit T J, et al. Application of GOES-8/9 soundings to weather forecasting and nowcasting［J］. Bulletin of the American Meteorological Society, 1998, 79: 2 059-2 077.
78	Li Z, Li J, Menzel W P, et al. GOES sounding improvement and applications to severe storm nowcasting［J］. Geophysical Research Letters, 2008, 35(3). DOI:10.1029/2007GL032797. doi: 10.1029/2007GL032797
79	Schmit T J, Li J, Ackerman S A, et al. High-spectral- and high-temporal-resolution infrared measurements from geostationary orbit［J］. Journal of Atmospheric and Oceanic Technology, 2009, 26: 2 273-2 292.
80	Schmit T J, Feltz W F, Menzel W P, et al. Validation and use of GOES sounder moisture information［J］. Weather and Forecasting, 2002, 17: 139-154.
81	Committee on Earth Science and Applications from Space. Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond［M］. Washington DC: National Academies Press, 2007.
82	Li J, Liu C Y, Zhang P, et al. Applications of full spatial resolution space-based advanced infrared soundings in the preconvection environment［J］. Weather and Forecasting, 2012, 27: 515-524.
83	Sieglaff J M, Schmit T J, Menzel W P, et al. Inferring convective weather characteristics with geostationary high spectral resolution IR window measurements: A look into the future［J］. Journal of Atmospheric and Oceanic Technology, 2009, 26: 1 527-1 541.
84	Li J, Li J, Schmit T J, et al. Warning information in preconvection environment from geostationary advanced infrared sounding system—A simulation study using the IHOP case［J］. Journal of Applied Meteorology and Climatology, 2011, 50: 766-783.
85	Yang J, Zhang Z, Wei C, et al. Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4［J］. Bulletin of the American Meteorological Society, 2017, 98(8): 1 637-1 658.
86	Menzel W P, Schmit T J, Zhang P, et al. Satellite-based atmospheric infrared sounder development and applications［J］. Bulletin of the American Meteorological Society, 2018, 99: 583-603.
87	Mueller C, Saxen T, Roberts R, et al. NCAR auto-nowcast system［J］. Weather and Forecasting, 2003, 18: 545-561.
88	Bedka K M, Mecikalski J R. Application of satellitederived atmospheric vectors for estimating mesoscale flows［J］. Journal of Applied Meteorology, 2005, 44: 1 761-1 772.
89	Zinner T, Mannstein H, Tafferner A. Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data［J］. Meteorology and Atmospheric Physics, 2008, 101(3/4): 191-210.
90	Jewett C P, Mecikalski J R. Adjusting thresholds of satellite-based convective initiation interest fields based on the cloud environment［J］. Journal of Geophysical Research: Atmospheres, 2013, 118(22): 12 649-12 660.
91	Mecikalski J R, Minnis P, Palikonda R. Use of satellite derived cloud properties to quantify growing cumulus beneath cirrus clouds［J］. Atmospheric Research, 2013, 120/121: 192-201.
92	Merk D, Zinner T. Detection of convective initiation using Meteosat SEVIRI: Implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM［J］. Atmospheric Measurement Techniques, 2013, 6(8): 1 903.
93	Song Zhenni. Grid-based early warning of convection initiation with FY2C satellite［J］. Science & Technology Information, 2014, (5):76-77.
	宋珍妮.基于网格的FY2C卫星对流初生预警［J］．科技信息, 2014, (5):76-77.
94	Liu Jinghua, Wang Bin, Han Lei, et al. Forecasting convective initiation of a convective weather event in Beijing-Tianjin region［J］. Acta Scientiarum Naturalium Universitatis Pekinensi, 2012, 48(1):42-46.
	刘京华, 王彬, 韩雷, 等．京津地区一次强对流天气的初生预警技术研究［J］.北京大学学报:自然科学版, 2012, 48(1):42-46.
95	Sobajima A. Rapidly Development Cumulus Areas Derivation Algorithm［R］. Japan Meteorological Agency Algorithm Theoretical Basis Document, Meteorological Satellite Center, Tokyo, Japan, 2012.
96	Li Wusheng, Wang Hongqing, Wang Yu, et al. Convective initiation forecasting and statistical evaluation based on satellite data［J］. Acta Scientiarum Naturalium Universitatis Pekinensi, 2014, 50(5):819-824.
	李五生, 王洪庆, 王玉, 等.基于卫星资料的对流初生预报及效果评估［J］.北京大学学报:自然科学版, 2014, 50(5):819-824.
97	Han H, Lee S, Im J, et al. Detection of convective initiation using meteorological imager onboard communication, ocean, and meteorological satellite based on machine learning approaches［J］. Remote Sensing, 2015, 7: 9 184-9 204.
98	Guo Wei, Cui Linli, Gu Wen, et al. Summer convective initiation forecasting in Shanghai based on Himawari-8 satellite［J］. Meteorological Monthly, 2018, 44(9):1 229-1 236.
	郭巍, 崔林丽, 顾问, 等．基于葵花８卫星的上海市夏季对流初生预报研究［J］．气象, 2018, 44(9):1 229-1 236.
99	Zhuge X, Zou X. Summertime convective initiation nowcasting over southeastern China based on Advanced Himawari Imager observations［J］. Journal of the Meteorological Society of Japan, 2018, 96(4): 337-353.
100	Lee S, Han H, Im J, et al. Detection of deterministic and probabilistic convection initiation using Himawari-8 advanced Himawari Imager data［J］. Atmospheric Measurement Techniques, 2017, 10(5): 1 859.
101	Han D, Lee J, Im J, et al. A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite data［J］. Remote Sensing, 2019, 11(12): 1 454.
102	Pavolonis M J, Heidinger A K. Daytime cloud overlap detection from AVHRR and VIIRS［J］. Journal of Applied Meteorology, 2004, 43: 762-778.
103	Pavolonis M J, Heidinger A K, Uttal T. Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation, and comparisons［J］. Journal of Atmospheric and Oceanic Technology, 2005, 44: 804-826.
104	Pavolonis M J. Advances in extracting cloud composition information from spaceborne infrared radiances: A robust alternative to brightness temperatures. Part I: Theory［J］. Journal of Applied Meteorology and Climatology, 2010, 49: 1 992-2 012.
105	Xu Hui. Nowcasting convective storm initiation using the data of FY2C box-averaging method［J］. Electronic Design Engineering, 2012, 20(21):40-42.
	徐慧.基于 FY2C 数据的平均 box 方法预报对流初生［J］．电子设计工程, 2012, 20(21):40-42.
106	Zhang Yunqi, Zhang Fuqing. A review on the ensemble-based data assimilations for severe convective storms［J］. Advances in Meteorological Science and Technology, 2018, 8(3):38-52.
	张云济, 张福青. 集合资料同化方法在强雷暴天气预报中的应用［J］. 气象科技进展, 2018, 8(3):38-52.

[1]	周海光. 机载多普勒天气雷达及应用研究进展[J]. 地球科学进展, 2010, 25(5): 453-462.
[2]	仲凌志,刘黎平,葛润生. 毫米波测云雷达的特点及其研究现状与展望[J]. 地球科学进展, 2009, 24(4): 383-391.
[3]	周海光. 多普勒天气雷达风场反演技术研究进展[J]. 地球科学进展, 2002, 17(3): 384-390.

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