SATELLITE DATA BASED STATISTICAL STUDY OF THE CHARACTERISTICS OF POTENTIAL DISTRIBUTION OF GLOBAL AIRCRAFT ICING
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摘要: 利用云探测卫星CloudSat在2007年12月1日—2008年11月30日全年数据, 构建一种利用CloudSat云分类产品、温度产品、液态水含量产品来联合识别飞机积冰潜势的算法, 并利用该算法对上述时段的全球范围内飞机积冰潜势的出现频率进行统计分析, 旨在为航空安全特别是长途飞行提供一定参考依据。并分析了不同云类型和不同季节的飞机积冰潜势分布特征。结果表明:飞机积冰潜势在全球范围内存在纬向、海陆及季节差异特征。整体上中高纬度地区积冰潜势频率比低纬度地区高, 陆地上空的积冰潜势频率比海洋上空高; 对于不同云类型而言, 中高纬度地区积冰潜势以层云、层积云、高层云和高积云为主, 而低纬度地区积冰潜势以深对流云为主; 对于不同季节而言, 夏季积冰频率较低, 冬春季节频率较高。Abstract: Based on CloudSat data from December 1, 2007 to November 30, 2008, an algorithm for identifying aircraft icing potential associated with subcooled liquid water in the cloud is built. The algorithm is used to statistically analyze the global frequency of aircraft icing potential in the year, and to provide some references for aviation safety, especially for long-distance flights. The distribution characteristics of aircraft icing potentials in different cloud types and seasons are also analyzed. The results are shown as follows.There are latitudinal, sea-land and seasonal differences in aircraft icing potentials around the world. For different latitudes, mid- and high-latitudes are of high-frequency areas, low-latitude areas, especially in the areas near the Tropics of Cancer and Capricorn, are the low-frequency areas; for different cloud types, the potentials of ice accretion in the middle and high latitudes are mainly stratus, stratocumulus, altostratus and altocumulus while in low latitudes, it is dominated by deep convective clouds; for different seasons, the icing frequency is low in summer and is relatively high in winter and spring.
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Key words:
- aircraft icing potential /
- frequency of CIP /
- CLIP /
- CTCR /
- global characteristics /
- CloudSat /
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图 10 春季（3—5月）（a）、夏季（6—8月）（b）、秋季（9—11月）（c）和冬季（12月—次年2月）（d）、的全球积冰潜势频率分布（网格分辩率为3°×2°）及全球各季节频率的纬向变化（e）
表 1 选用的CloudSat/CPR标准数据产品和辅助数据产品
产品名称 产品描述 主要输入数据 特征 2B-CLDCLASS 8种云类型，还包括降水云与混合云的识别 雷达数据、EOSMODIS云覆盖、ECWMF-AUX提供的温度 目前已能判断八种基本云型的反演算法和指标。 2B-CWC-R0 仅源于CPR的云液态水、冰水含量及粒子有效半径 2B-GE0PR0F XALIPSO 白天和夜间，500 m分辨率 ECWMF-AUX 大气温度，比湿，气压廓线 1B-CPR和欧洲气象中心ECWMF数据 白天和夜间，500 m分辨率 
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表 2 各云型液态水含量信息的探测值数和不确定值数
云类型 卷云 高层云 高积云 层云 层积云 积云 雨层云 深对流云 不确定数 1 375 71 813 28 470 2 43 485 18 937 193 530 92 329 探测数 16 500 423 520 90 360 15 204 600 28 645 481 750 146 530 比例 8.3% 17% 31.5% 13.3% 21.3% 66.1% 40.2% 63%
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表 3 美国空军飞机积冰分类标准
粒子有效半径(Re)/μm 液态水含量(LWC)/(mg/cm3) 云有效温度(Tc)/K 积冰强度 > 50 > 50 < 273 低 > 50 > 100 < 273 中 > 20 > 100 < 273 低 > 20 > 200 < 273 中 > 50 > 200 < 273 强 > 100 > 100 < 273 强
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表 4 CIP/CTCR/CLIP积冰潜势识别结果
统计结果 AAA AAB ABA ABB BAA BAB BBA BBB 廓线数 10 039 9 323 43 096 19 809 132 99 11 006 175 050
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表 5 滤去含有不确定值廓线后的CIP/CTCR/CLIP积冰潜势识别结果
统计结果 AAA AAB ABA ABB BAA BAB BBA BBB 廓线数 9 725 1 956 24 51 215 16 38 019 128 548
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表 6 CIP改进后的CTCR/CLIP积冰潜势识别结果
统计结果 AAA AAB ABA ABB BAA BAB BBA BBB 廓线数 42 300 12 079 10 835 17 053 4 105 218 7 033 174 931
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表 7 不同算法得到的指标 单位: %。
指标 CTCR CLIP 改进的CTCR 正确率 99.83 85.00 88.00 积冰一致率 98.82 82.67 92.64 积冰漏识率 76.46 35.41 33.90 积冰有效识别率 23.26 53.40 61.20
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[1] SMITH W L J, MINNIS P, FLEEGER C, et al. Determining the flight icing threat to aircraft with single-layer cloud[J]. J Appl Meteorol Climatol, 2012, 51(10): 1 794-1 810. [2] BURROWS D, STANKOV B, WESTWATER E, et al. Winter icing and storms project(WISP)[J]. Bull Amer Meteor Soc, 1992, 73(7): 951-974. [3] MINNIS P, KRATZ D P, COAKLEY J A J, et al. Clouds and the Earth's Radiant Energy System(CERES) algorithm theoretical basis document, volume Ⅲ: Cloud analyses and radiance inversions (subsystem 4)[J]. NASA RP, 1995, 1376: 135-176. [4] MINNIS P, SUN-MACK S, YOUNG DF, et al. CERES edition-2 cloud property retrievals using TRMMVIRS and terra and aqua MODIS data, Part Ⅰ: Algorithms[J]. IEEE Trans Geosci Remote Sens, 2011, 49(11): 4 374-4 400. [5] RAUBER R M, TOKAYA. An explanation for the existence of supercooled water at the top of cold clouds[J]. J Atmos Sci, 2010, 48(8): 1 005-1 023. [6] CURRY J A, LIU G. Assessment of aircraft icing potential using satellite data[J]. J Appl Meteor, 2010, 31(6): 605-621. [7] ELLROD G, NELSON J P, Remote sensing of aircraft icing regions using GOES multispectral imager data[C]//Preprints, 15th Conf on Weather Analysis and Forecasting. Norfolk, VA, Amer Meteor Soc, 1996: 9-12. [8] THOMPSON G, BULLOCK R, LEET F. Using satellite data to reduce spatial extent of diagnosed icing[J]. Wea Forec, 1997, 12(1): 185-190. [9] SMITH W L, MINNIS P, YOUNG D F. An icing product derived from operational satellite data[C]//Preprints Ninth Conf, on Aviation, Range and Aerospace Meteorology. Orlando, FL, Amer Meteor Soc, 2000: 256-259. [10] SMITH W L, BERNSTEIN B C, MCDONOUGH F, et al. Comparison of super-cooled liquid water cloud properties derived from satellite and aircraft measurements[C]//Proc In-Flight Icing/De-icing Int Conf. Chicago, IL, Federal Aviation Administration, 2003: 2 156. [11] MINNIS P, SMITH Jr W L, NGUYEN L, et al. A real-time satellite based icing detection system[C]//Proc 14th IntConf Clouds and Precipition. Bologna, Italy, 2004: 18-24. [12] BERNSTEIN B C, MCDONOUGH F, POLITOVICH M, et al. Current icing potential: Algorithm description and comparison with aircraft observations[J]. J Appl Meteor, 2005, 44(7): 969-986. [13] HU Y, RODIER S, XU K, et al. Occurence liquid water content and fraction of supercooled water clouds from combined CALIOP/IIR/MODIS measurement[J]. J Geophys Res, 2010, 115 (D4):1 485-1 490. [14] WESTBROOK C. Illingworth A Evidence that ice froms primarily in supercooled liquid clouds at temperatures>-27 ℃[J]. Geophys Res Lett, 2011, 38, L14808. [15] SHUPE M D, MATROSOV S Y, UTTAL T. Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA[J]. J Atmos Sci, 2006, 63(2): 679-711. [16] LUKE E P, KOLLIAS P, SHUPE M D. Detection of supercooled liquid in mixed-phased clouds using radar Doppler spectra[J]. J Geophys Res: Atmos(1984—2012), 2010, 115(D19): 5 548-5 554. [17] 孙晓光. 毫米波测云雷达数据处理及航空气象保障应用研究[D]. 南京: 解放军理工大学, 2011: 24-35. [18] 吴举秀, 魏鸣, 王以琳.利用毫米波测云雷达反演层状云中过冷水[J].干旱气象, 2015, 33(2): 227-235. [19] 王新炜, 白洁, 刘健文, 等. SBDART辐射传输模式及其在飞机潜在积冰区反演中的应用[J].气象科技, 2003, 31(3): 152-155. [20] 王新炜, 张军, 王胜国.中国飞机积冰的气候特征[J].气象科学, 2002, 22(3): 343-350. [21] MICHAEL P. Detect cloud icing conditions using CloudSat datasets[C]//23th Conference on Weather Analysis and Forecasting. 8B. 2, 2009. [22] MINNIS P, KRATZ D P, COAKLEY Jr J A, et al. Cloud optical property retrieval (subsystem 4.3)[J]. Clouds and the Earth's Radiant Energy System (CERES) algorithm theoretical basis document, 1995, 3: 135-176. [23] DELANOË J, HOGAN R J. Combined CloudSat-CALIPSO-MADIS retrievals of the properties of iceclouds[J]. J Geophys Res Atmos, 2010, 115(D4): 1 307-1 314. [24] ZHANG D, WANG Z, LIU D. A global view of mid-level liquid-layer topped stratiform cloud distribution and phase partition from CALIPSO and CloudSatmeasurements[J]. J Geophys Res Atmos, 2010, 115(D4): 288-303. [25] BERNSTEIN B C, MCDONOUGHF, POLITM P, et al. Current icing potential (CIP): Algorithm description and comparison with aircraft observations[J]. J ApplMeteorol, 2005, 44(9): 969-986. [26] 王磊, 李成才, 赵增亮, 等.飞机积冰云微物理特征分析及监测技术研究[J].气象, 2014, 40(2): 196-205. -
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