设为首页
加入收藏
Icing Potential Index of Aircraft Icing Based on Fuzzy Logic
-
摘要: 使用北京人工影响天气办公室提供的2014-2017年京津冀地区飞行记录积冰个例样本与机载观测数据，2016年全国空中报告积冰、非积冰个例样本和欧洲中期天气预报中心（ECMWF）第5代全球气候大气再分析数据（ERA5），基于模糊逻辑隶属度函数，定义了以气温和相对湿度为判别基础并考虑垂直速度和云量影响的积冰指数Ip（icing potential index），用于判断飞机在空中发生积冰事件的可能性。检验结果表明：该指数对积冰事件的判别准确率为80.2%，与目前国内常用的经典积冰指数（Ic）相比，其判别准确率有明显提升，且漏报率和虚警率均显著降低（分别为9.4%和10.4%），结合数值预报产品可对飞机在空中特定位置发生积冰事件的可能性进行预测。Abstract: Aircraft icing, a cumulative hazard, is one of the major weather hazards affecting aviation. It reduces aircraft efficiency by increasing weight, reducing lift, decreasing thrust, and increasing drag. Icing also seriously impairs aircraft engine performance and causes false indication on flight instruments, loss of radio communications and failures of control panel, brakes, and landing gear. Therefore, the prediction of aircraft icing is one of the key research focuses.In order to establish an aircraft icing potential index with more reasonable threshold and easy to adopt, 372 aircraft icing cases and corresponding observation data from 2014 to 2017 provided by Beijing Weather Modification Office are analyzed based on fuzzy logical principles. The membership function of temperature and relative humidity derived from those data analysis is used to calculate the initial possibility of icing. On this basis, the membership function representing the influence of vertical velocity and cloudiness on the initial possibility of icing is determined using the national pilot reports (PIREPs) in 2016 and corresponding ERA5 reanalysis data to screen different forms of membership functions. Based on membership functions of temperature, relative humidity, vertical velocity and cloudiness, the icing potential index (Ip) can be calculated by using output from numerical weather prediction model.According to ERA5 reanalysis data, 61 icing cases and 45 non-icing cases are used to test the effectiveness of Ip. Results show that the accuracy, missing alarm rate and false alarm rate of Ip are 80.2%, 9.4% and 10.4%. Compared with the commonly used icing index (Ic), the accuracy of Ip is better, the missing alarm and false alarm reduce significantly. However, it should be noted that the difference between aircraft type and flight speed of different aircraft icing cases in this study is not discussed, and it is assumed that effects of vertical velocity and cloud cover on the initial possibility of icing are independent, which need further study.In summary, the established icing potential index (Ip) based on fuzzy logical principles is efficient and feasible, and provides information for pilots to avoid high-risk areas of icing in the air. Combining with the regional numerical weather prediction model, it can output the possibility of icing in certain areas under certain meteorological conditions and provide reference for pilots to avoid high-risk areas of icing in the air.
-
Key words:
- aircraft icing;
- icing index;
- prediction of icing possibility
-
图 1 2014—2017年北京人影办积冰个例分布
Fig. 1 Distribution of icing cases from 2014 to 2017 provided by Beijing Weather Modification Office
图 2 2016年全国空中报告积冰个例(a)和非积冰个例(b)分布
Fig. 2 Distribution of icing cases(a) and non-icing cases(b) from 2016 national pilot reports(PIREPs)
图 5 各相对湿度区间对应积冰个例
Fig. 5 Sample size of icing cases in each relative humidity interval
图 8 云中液态水含量Sa和云量Sb的隶属度函数
Fig. 8 Membership functions of specific cloud liquid water content Sa and cloud cover Sb
表 1 垂直运动项权重系数(k1)和过冷却液态水含量项权重系数(k2)的组合
Table 1 Combination of weight coefficients k1 and k2
组合序号 k1 k2 1 1.0 0.0 2 0.8 0.2 3 0.6 0.4 4 0.5 0.5 5 0.4 0.6 6 0.2 0.8 7 0.0 1.0 
下载: 导出CSV
表 2 筛选结果
Table 2 Screening results
k1, k2组合 Vmap Smap 准确率/% 漏报率/% 虚警率/% 高值率/% Va 76.79 12.50 10.71 51.61 1, 0 Vb 75.00 12.50 12.50 51.61 Vc 76.79 8.93 14.29 64.52 0.8, 0.2 Va Sa 76.79 12.50 10.71 45.16 Sb 76.79 12.50 10.71 48.39 Vb Sa 75.00 12.50 12.50 45.16 Sb 75.00 12.50 12.50 48.39 Vc Sa 76.79 8.93 14.29 58.06 Sb 76.79 8.93 14.29 64.52 0.6, 0.4 Va Sa 78.57 12.50 8.93 41.94 Sb 82.14 8.93 8.93 48.39 Vb Sa 76.79 12.50 10.71 41.94 Sb 80.36 8.93 10.71 48.39 Vc Sa 78.57 8.93 12.50 45.16 Sb 80.36 7.14 12.50 48.39 0.5, 0.5 Va Sa 78.57 12.50 8.93 38.71 Sb 82.14 8.93 8.93 45.16 Vb Sa 76.79 12.50 10.71 38.71 Sb 80.36 8.93 10.71 45.16 Vc Sa 78.57 8.93 12.50 38.71 Sb 80.36 7.14 12.50 45.16 0.4, 0.6 Va Sa 76.79 12.50 10.71 38.71 Sb 80.36 8.93 10.71 45.16 Vb Sa 76.79 12.50 10.71 38.71 Sb 80.36 8.93 10.71 45.16 Vc Sa 78.57 8.93 12.50 38.71 Sb 80.36 7.14 12.50 45.16 0.2, 0.8 Va Sa 78.57 12.50 8.93 38.71 Sb 82.14 8.93 8.93 45.16 Vb Sa 78.57 12.50 8.93 38.71 Sb 82.14 8.93 8.93 45.16 Vc Sa 78.57 10.71 10.71 38.71 Sb 82.14 7.14 10.71 45.16 0, 1 Sa 75.00 16.07 8.93 38.71 Sb 80.36 10.71 8.93 45.16
下载: 导出CSV
表 3 积冰指数Ip的值对应的积冰可能性
Table 3 Possibility of icing corresponding to the value of icing potential index
Ip 积冰可能性 [0, 0.1] 无 (0.1, 0.4] 低 (0.4, 0.6] 中 (0.6, 0.8] 较高 (0.8, 1] 高
下载: 导出CSV
-
[1] 黄仪方, 朱志愚.航空气象.成都:西南交通大学出版社, 2002. [2] 刘风林, 孙立潭, 李士君.飞机积冰诊断预报方法研究.气象与环境科学, 2011, 34(4):26-30. doi: 10.3969/j.issn.1673-7148.2011.04.005 [3] Forbes G S, Hu Y, Brown B G, et al.Examination of Conditions in the Proximity of Pilotreports of Icing During STORM-FEST.Preprints, Fifth Int Conf on Aviation Weather Systems.Vienna:Amer Meteor Soc, 1993:282-286. [4] Thompson G, Bruintjes R T, Brown B G, et al.Intercomparison of in-flight icing algorithms:Part Ⅰ:WISP94 realtime icing prediction and evaluation program.Wea Forecasting, 1997, 12(4):878-889. doi: 10.1175/1520-0434(1997)012<0878:IOIFIA>2.0.CO;2 [5] Kelsch M, Wharton L.Comparing PIREPs with NAWAU turbulence and icing forecasts:Issues and results.Wea Forecasting, 1996, 11(3):385-390. doi: 10.1175/1520-0434(1996)011<0385:CPWNTA>2.0.CO;2 [6] 王洪芳, 刘健文, 纪飞, 等.飞机积冰业务预报技术研究.气象科技, 2003, 31(3):140-146. doi: 10.3969/j.issn.1671-6345.2003.03.003 [7] 刘开宇, 申红喜, 李秀连, 等."04.12.21"飞机积冰天气过程数值特征分析.气象, 2005, 31(12):23-27. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qx200512004 [8] 何新党, 刘永寿, 苟文选, 等.基于云微物理参数的飞机积冰多因子预测方法.航空计算技术, 2012, 42(1):72-75. doi: 10.3969/j.issn.1671-654X.2012.01.020 [9] McDonough F, Bernstein B, Politovich M, et al.The Forecast Icing Potential Algorithm.Aiaa Aerospace Sciences Meeting & Exhibit, 2013. [10] 杨超.基于再分析数据的飞机积冰预测研究.广汉: 中国民用航空飞行学院空中交通管理学院, 2017: 24-27. [11] Zadeh L A.Probability measures of fuzzy events.Journal of Mathematical Analysis and Application, 1968, 23:421-427. doi: 10.1016/0022-247X(68)90078-4 [12] 张秉祥, 李国翠, 刘黎平, 等.基于模糊逻辑的冰雹天气雷达识别算法.应用气象学报, 2014, 25(4):415-426. doi: 10.3969/j.issn.1001-7313.2014.04.004 [13] 郑永光, 周康辉, 盛杰, 等.强对流天气监测预报预警技术进展.应用气象学报, 2015, 26(6):641-657. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20150601&flag=1 [14] 王洪, 孔凡铀, Jung Y, 等.面向资料同化的S波段双偏振雷达质量控制.应用气象学报, 2018, 29(5):36-48. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180504&flag=1 [15] 李丰, 刘黎平, 王红艳, 等.S波段多普勒天气雷达非降水气象回波识别.应用气象学报, 2012, 23(2):147-158. doi: 10.3969/j.issn.1001-7313.2012.02.003 [16] Leondes C.T.Fuzzy Logic and Expert Systems Applications.New York:Academic Press, 1998:57-60. [17] Bernstein B C, Omeron T A, McDonough F, et al.The relationship between aircraft icing and synoptic-scale weather conditions.Wea Forecasting, 1997, 12(4):742-762. doi: 10.1175/1520-0434(1997)012<0742:TRBAIA>2.0.CO;2 [18] Korolev A V, Isaac G A, Cober S G, et al.Microphysical characterization of mixed-phase clouds.Q J Roy Meteor Soc, 2003, 129(587):39-65. doi: 10.1256/qj.01.204 [19] Cober S G, Isaac G A, Strapp J W.Characterizations of aircraft icing environments that include supercooled large drops.J Appl Meteor, 2001, 40(11):1984-2002. doi: 10.1175/1520-0450(2001)040<1984:COAIET>2.0.CO;2 [20] Rosenfeld D, Woodley W L.Deep convective clouds with sustained supercooled liquid water down to -37.5℃.Nature, 2000, 405(6785):440-442. doi: 10.1038/35013030 [21] Bernstein B C, Mcdonough F, Politovich M K, et al.Current icing potential:Algorithm description and comparison with aircraft observations.J Appl Meteor, 2005, 44(7):969-986. doi: 10.1175/JAM2246.1 [22] Margarida B.Comparison of in-flight aircraft icing algorithms based on ECMWF forecasts.Meteorol Appl, 2015, 22(4):705-715. doi: 10.1002/met.1505 [23] 李佰平, 戴建华, 孙敏, 等.一种改进的飞机自然结冰潜势算法研究.气象, 2018, 44(11):1377-1390. http://d.old.wanfangdata.com.cn/Periodical/qx201811001 [24] 廖捷, 熊安元.我国飞机观测气象资料概况及质量分析.应用气象学报, 2010, 21(2):206-213. doi: 10.3969/j.issn.1001-7313.2010.02.010 [25] 仲跻芹, 陈敏, 范水勇, 等.AMDAR资料在北京数值预报系统中的同化应用.应用气象学报, 2010, 21(1):19-28. doi: 10.3969/j.issn.1001-7313.2010.01.003 [26] 李军霞, 李培仁, 陶玥, 等.山西春季层状云系数值模拟及与飞机探测对比.应用气象学报, 2014, 25(1):22-32. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20140103&flag=1 [27] 杨有林, 纪晓玲, 张肃诏, 等.基于雷达回波强度面积谱识别降水云类型.应用气象学报, 2018, 29(6):690-700. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20180605&flag=1 -
图(8) / 表(3)
计量
- 摘要浏览量: 3911
- HTML全文浏览量: 2047
- PDF下载量: 136
- 被引次数: 0
