八大低云多发机场的云诊断算法应用初探
Preliminary Study on the Application of Cloud Diagnosis Algorithm in Eight Airports with Multiple Low Clouds
摘要: 使用FNL (0.25˚*0.25˚ NCEP GFS)的6小时数值预报资料，基于C云量诊断法(C方法)、WR95及优化的WR95方法(WR95opt)分别计算我国八大低云多发机场的450米以下最低层云，使用机场天气报告检验诊断效果，对比分析表明：不分时次和季节时，以及在大部分时次和季节，WR95和WR95opt在大部分机场的准确率、漏报率和TS评分高于C方法；对于WR95和WR95opt，WR95opt的准确率和漏报率相对较高，WR95在TS评分方面整体上优于WR95opt。三种方法在不同季节、不同时次准确率、漏报率和TS评分有所不同，但不同季节变化幅度远小于不同时次。在选取的八个机场中，三种方法在华东地区中南部的4个机场表现普遍好于其他地区的机场。
Abstract: Using the 6-hour numerical weather prediction data of FNL (0.25˚*0.25˚ NCEP GFS), based on C cloud amount diagnosis method (C method), WR95 and optimized WR95 method (WR95opt), the lowest layer clouds below 450 meters in the top eight low cloud-prone airports in China are cal-culated respectively. The diagnostic results were tested by aerodrome routine weather reports (METAR). The comparative analysis showed that the accuracy rate, missing report rate and TS score of WR95 and WR95opt in most airports were higher than those of C method regardless of time and season, and in most time and season. For WR95 and WR95opt, the accuracy rate and missing report rate of WR95opt are higher than those of WR95, but the TS score of WR95 is overall higher than that of WR95opt. The accuracy rate, missing report rate and TS score of the three methods are different in different seasons and different times, but the variation range in different seasons is far smaller than that in different times. Among the selected eight airports, the performance of the three methods in four airports in the central-south part of East China is generally better than that in other regions.
文章引用：裴昭昭, 苏艳华, 徐记亮. 八大低云多发机场的云诊断算法应用初探[J]. 地球科学前沿, 2022, 12(4): 456-464. https://doi.org/10.12677/AG.2022.124047

参考文献

[1] FAA.RTCA Inc. (2005) Document RTCA/DO-254, Design Assurance Guidance for Airborne Electronic Hardware (AC 20152).
[2] 民航局空管办. AP-117-TM-02R1. 民用航空气象地面观测规范[S]. 北京: 中国民用航空局空管行业管理办公室, 2012.
[3] 民航局空管办. AP-117-TM-2019-01. 民用航空气象预报规范[S]. 北京: 中国民用航空局空管行业管理办公室, 2019.
[4] 韩成鸣, 李耀东, 史小康. 云分析预报方法研究进展[J]. 地球科学进展, 2015, 30(4): 505-516.
[5] 陈超, 郭晓军, 邱晓斌, 等. 中国华北地区云垂直结构及云水含量卫星遥感研究[J]. 气象与环境学报, 2015, 31(5): 159-164.
[6] Poore, K.D., Wang, J.H. and Rossow, W.B. (1995) Cloud Layer Thicknesses from a Combination of Surface and Upper-Air Observations. Journal of Climate, 8, 550-568. [Google Scholar] [CrossRef] 
[7] Wang, J.H. and Rossow, W.B. (1995) Determination of Cloud Vertical Structure from Upper-Air Observations. Journal of Applied Meteorology, 34, 2243-2258. [Google Scholar] [CrossRef] 
[8] 赵仕伟, 赵增亮, 史荟燕, 等. WR95探空分析算法在中尺度数值模式中的应用研究[C]//第26届中国气象学会年会人工影响天气与大气物理学分会场论文集. 杭州: 中国气象学会, 2009: 165-173.
[9] 蔡淼, 欧建军, 周毓荃, 等. L波段探空识别云区方法的研究[J]. 大气科学, 2014, 38(2): 213-222.
[10] 黄海亮, 靳双龙, 王式功, 等. 相似预报方法在山西省云量预报中的应用[J]. 干旱气象, 2018, 36(5): 845-851.
[11] 赵中军, 靳双龙, 刘晓琳, 等. 环渤海地区云量的精细化预报方法[J]. 兰州大学学报(自然科学版), 2019, 55(4): 541-548.
[12] 杜晖, 殷启元, 贾晓红, 等. 动态时变参数方法在云量精细化预报中的应用研究[J]. 热带气象学报, 2020, 36(6): 834-845.
[13] 陈雪娇, 刘奇俊, 马占山. GRAPES全球模式云方案的诊断研究[J]. 气象学报, 2021, 79(1): 65-78.
[14] Xu, K.M. and Randall, D.A. (1996) A Semiempirical Cloudiness Parameterization for Use in Climate Models. Journal of the Atmospheric Sciences, 53, 3084-3102. [Google Scholar] [CrossRef] 
[15] 张寅, 罗亚丽, 管兆勇. NCEP全球预报系统在ARM SGP站点预报大气温度、湿度和云量的检验[J]. 大气科学, 2012, 36(1): 170-184.
[16] 张金强. 地基探测与NCEP GFS模式预报云量在ARM SGP站点的对比[J]. 气象与环境科学, 2016, 39(1): 12-18.
[17] Zhang, J.Q., Chen, H.B., Li, Z.Q., et al. (2010) Analysis of Cloud Layer Structure in Shouxian, China Using RS92 Radiosonde Aided by 95 GHz Cloud Radar. Journal of Geophysical Research, 115, D00K30. [Google Scholar] [CrossRef] 
[18] 何佳, 惠建忠, 何险峰, 等. Python在短临气象预报检验中的应用[J]. 气象科技, 2021, 49(5): 738-745.