设为首页
加入收藏
Auto-analysis of Trough System at 500 hPa Based on Gradient Algorithm
-
摘要: 利用位势高度场资料，针对500 hPa高空槽线天气系统，以天气学定义及其图形学特征分析为基础，在保持天气动力学理论约束的同时，提出基于位势高度梯度进行槽线天气系统自动分析的方法。面向各级气象台站的天气预报业务，利用气象信息综合分析系统 (MICAPS) 第4类位势高度格点数据资料，经过梯度计算、垂线确定、方向划分、邻域对比、节点初选、噪声判别、分类滤除、聚类分析、轴向平均等步骤，解决了一系列相关技术问题，完成了西风槽和横槽两类槽线系统自动分析的程序开发。近年宁夏寒潮强降温等过程的分析试验表明，该算法稳定可靠，中高纬度地区槽线分析能力更强，运行效率进一步提高。Abstract: As the forecast products are increasing rapidly and the efficiency of manual process remains pretty low at present, the demand for intelligent assistant computer system of weather forecast is very urgent. It is the fundamental way to improve the automatic level of operational forecast in order to release the working pressure for detail forecast. The study on auto-analysis technology of weather systems is conducted as it is the key to build an intelligent forecast computer software with the thinking way of human kind. Based on synoptic notion and graphic features of trough system, under the guidance of computer graphics theory, trough system at 500 hPa are analyzed.The mathematic relations between gradient of geopotential height, its perpendicular direction and the nodes of trough system are proven by analysis of trough system conception, synoptic and graphics characteristics. The algorithm of trough system auto-analysis is developed under the strict constrain of atmospheric dynamic theory taking gradient of geopotential height as key element, and searching the relative minimal in its perpendicular direction within an optimal neighborhood which is determined by contrastive analysis. A set of software is developed using the geopotential height field of European Centre for Medium-Range Weather Forecasts at 500 hPa in format 4 of MICAPS which is the operational standard of China Meteorological Administration to meet the need of forecast centers at all levels of the country. The technical problems such as gradient calculating, direction zoning, neighborhood designating, primary nodes selecting, noise filtering, cluster analyzing are solved. Some fake troughs and over flow points can be identified with positioning according to the synoptic principals and basic experience, while the indices of density and location of primary nodes are applied to eliminate different noises, which concentrated in some certain areas. The multiple trough lines are merged by clustering and axial direction averaging. The auto-analysis technology and process of both westerly trough and traverse trough lines are developed and products are output in format 14 of MICAPS for forecasters. The algorithm and corresponding software system of auto-analysis of troughs is evaluated with parameters such as offset, length and general inclination angle. Recent experiments on cold wave samples in Ningxia show that the algorithm and the relevant software are stable, the effect in general is satisfying, and the method shows a better performance, especially in areas of middle and high latitudes. Through trail run, the efficiency of trough auto-analysis is improved further, and shortages of contour pursuing method are eliminated.
-
Key words:
- gradient vector;
- trough line;
- weather system;
- auto-analysis
-
图 1 位势高度场结构
(a) 低涡中西风槽t1与横槽t2，(b) 西风槽线t3
Fig. 1 Structure of geopotential height
(a) troughs from low vortex, (b) westerly trough
图 2 方位与计算 (a) 梯度方位分区，(b) 对应计算区间
Fig. 2 Direction and calculation interval (a) gradient direction, (b) coresponding interval
图 3 计算结果 (a) 位势高度场梯度方向分布，(b) 槽线初选点集
Fig. 3 Result of calculation (a) gradient direction of geopotential height, (b) initial points of troughs
图 4 分析结果 (a) 剔除后的剩余的槽点，(b) 合并后的槽线
Fig. 4 Analysis result (a) selected points after filtering, (b) troughs
图 5 天气系统分析误差三维空间分布及其在二维平面的投影
(透明椭球为误差聚集区)
Fig. 5 Distribution of weather system analysis errors in 3D space and their projections in 2D
(ellipsoid is the aggregation of errors)
图 6 槽线显著误差情况分析 (单位：dagpm)
(a) 14号槽线偏移误差，(b) 36号槽线端点误差，(c) 43号槽线长度误差
Fig. 6 Significant error (unit:dagpm)
(a) offset error of No.14, (b) terminal point error of No.36, (c) length error of No.43
表 1 个例信息
Table 1 Imformation of samples
寒潮强降温过程时段 气温降幅大于10℃常规气象站数 占全区常规站点数比例/% 2012-02-05—07 11 46 2012-11-03—04 15 63 2013-02-28—03-01 20 83 2013-03-09—10 14 58 2014-05-09—10 23 96 2014-10-11—12 18 75 
下载: 导出CSV
-
[1] 陈静.图形模式识别方法及其在中期雪灾天气预报中的应用.应用气象学报, 2002, 13(1):110-117. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20020113&flag=1 [2] 李振海.计算机天气图图形识别.气象, 1994, 20(6):20-23. doi: 10.7519/j.issn.1000-0526.1994.06.004 [3] 袁美英, 徐南平.高低压中心, 槽线识别的初步试验.气象, 1994, 20(6):15-19. doi: 10.7519/j.issn.1000-0526.1994.06.003 [4] 胡文东, 赵光平, 陈晓光, 等.高空基本天气系统类别自动识别与沙尘暴系统识别试验.中国沙漠, 2007, 27(4):633-638. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGSS200704017.htm [5] 胡文东, 黄小玉, 赵光平, 等.高空基本影响天气系统定量化自动分析研究.气象, 2008, 34(6):107-111. doi: 10.7519/j.issn.1000-0526.2008.06.016 [6] 邵建, 胡文东, 冯建民, 等.基于图形分析的宁夏春季干旱监测预测方法研究.中国沙漠, 2013, 33(3):874-881. doi: 10.7522/j.issn.1000-694X.2013.00124 [7] 牟丽雅.基于蚁群系统理论的槽线识别研究与应用.武汉:武汉理工大学, 2010. http://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201603027.htm [8] 谭晓光, 罗兵.天气预报分析型数据模型及生成.应用气象学报, 2014, 25(1):120-128. doi: 10.11898/1001-7313.20140113 [9] 谭晓光.数据仓库技术在天气预报决策中的应用探讨.应用气象学报, 2006, 17(3):325-332. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20060358&flag=1 [10] 张云港, 杨金华.天气图特征提取研究.云南大学学报:自然科学版, 2007, 29(2):167-170. http://www.cnki.com.cn/Article/CJFDTOTAL-YNDZ2007S2015.htm [11] 郑佳锋, 张杰, 朱克云, 等.阵风锋自动识别与预警.应用气象学报, 2013, 24(1):117-125. doi: 10.11898/1001-7313.20130112 [12] 王萍, 孔秀梅, 杨洪敏, 等.天气图相似检索研究.天津大学学报:自然科学与工程技术版, 2004, 37(3):264-268. http://www.cnki.com.cn/Article/CJFDTOTAL-TJDX200403017.htm [13] 周庭泽, 梁平德, 刘爱霞.天气图相似识别系统及其在天气预报中的应用.气象, 1991, 17(6):23-26. doi: 10.7519/j.issn.1000-0526.1991.06.004 [14] 李永尧, 郑陈婷, 王晓明.基于数字图像的天气系统识别.福建师范大学学报:自然科学版, 2009, 25(3):24-27. http://www.cnki.com.cn/Article/CJFDTOTAL-FJSZ200902005.htm [15] 林志强, 周振波, 假拉.高原低涡客观识别方法及其初步应用.高原气象, 2013, 32(6):1580-1588. doi: 10.7522/j.issn.1000-0534.2012.00153 [16] 费增坪, 王洪庆, 张焱, 等.基于静止卫星红外云图的MCS自动识别与追踪.应用气象学报, 2011, 22(1):115-122. doi: 10.11898/1001-7313.20110112 [17] 张一平, 吴蓁, 苏爱芳, 等.基于流型识别和物理量要素分析河南强对流天气特征.高原气象, 2013, 32(5):1492-1502. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201305029.htm [18] 陶岚, 戴建华.下击暴流自动识别算法研究.高原气象, 2011, 30(3):784-797. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201103026.htm [19] 龚志强, 王晓娟, 崔冬林, 等.区域性极端低温事件的识别及其变化特征.应用气象学报, 2012, 23(2):195-204. doi: 10.11898/1001-7313.20120208 [20] 胡志群, 夏文梅, 汤达章, 等.多普勒雷达速度图像识别及散度提取方法研究.高原气象, 2007, 26(4):821-829. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200704019.htm [21] 殷青军, 杨英莲, 徐维新.NOAA卫星资料云雪识别方法的研究.高原气象, 2002, 21(5):526-528. http://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200205014.htm [22] 黄培之, 刘泽慧.地形断面高程极值法的理论研究.测绘通报, 2005, 51(4):11-13. http://www.cnki.com.cn/Article/CJFDTOTAL-CHTB200504003.htm [23] Jenson S K, Domingue J O.Extracting topographic structure from digital elevation data for geographic information system analysis.Photogrammetry Engineering and Remote Sensing, 1988, 54(11):1593-1600. https://pubs.er.usgs.gov/publication/70142175 [24] O'Callaghan J F.The extraction of drainage networks from digital elevation data:Computer Vision.Graphics and Image Processing, 1984, 28(2):323-344. http://www.sciencedirect.com/science/article/pii/S0734189X84800110?via%3Dihub [25] 黄培之, 刘泽慧.基于地形梯度方向的山脊线和山谷线的提取.武汉大学学报:信息科学版, 2005, 30(5):396-399. http://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200505005.htm [26] 李晓丽, 李小红.梯度LBP优化深度图像分析的性别人脸识别.计算机应用研究, 2014, 31(11):3502-3505. doi: 10.3969/j.issn.1001-3695.2014.11.069 [27] 华顺刚, 李新丰, 陈国鹏.利用梯度与人体区域信息的图像分条及缩放.计算机辅助设计与图形图像学报, 2014, 26(7):1167-1175. http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201407017.htm [28] 项德良, 粟毅, 赵凌君, 等.一种基于局部梯度比率特征度量SAR图像相似性的新方法.电子学报, 2014, 42(1):9-13. http://www.cnki.com.cn/Article/CJFDTOTAL-DZXU201401002.htm [29] 刘玉莲, 于宏敏, 任国玉, 等.我国强降雪气候特征及其变化.应用气象学报, 2013, 24(3):304-313. doi: 10.11898/1001-7313.20130306 [30] 张云惠, 杨莲梅, 肖开提, 等.1971—2010年中亚低涡活动特征.应用气象学报, 2012, 23(3):312-321. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20120307&flag=1 -
计量
- 摘要浏览量: 2481
- HTML全文浏览量: 1089
- PDF下载量: 561
- 被引次数: 0
