部分遮挡是影响雷达观测资料质量的重要误差源之一。基于雷达回波的概率特征, 本文提出一种不依赖高精度地形信息的雷达部分遮挡区域识别算法。该算法无须以人工方式建立样本数据库, 只需直接检测实际业务应用中雷达连续观测的反射率因子, 统计雷达探测范围内的每个格点出现大于某一反射率因子阈值(定义为Z阈值)的概率, 并通过设置适当的概率阈值(定义为C阈值)来确定不同遮挡性质的雷达探测区域。应用该算法, 使用2012年5-10月金华、衢州及上饶雷达连续探测资料, 识别得到金华、衢州和上饶三部雷达距离地面3 km高度层上CAPPI的完全遮挡区域、部分遮挡区域和无遮挡区域; 同时, 通过对比2013年4月29日金华、衢州、杭州、上饶、黄山雷达3 km高度重叠探测区域的雷达反射率因子, 检验了雷达部分遮挡区域识别结果的有效性, 并分析了算法阈值的敏感性; 最后, 结合剔除部分区域弱回波的方案, 对雷达组网拼图算法进行优化改进。结果表明: 通过选择适当的Z阈值和C阈值算法, 可以有效识别雷达部分遮挡区域; 算法阈值的设置是识别部分遮挡区域的关键, 较为适中的Z阈值有利于C阈值的选择; 优化后的组网拼图算法可以有效解决部分遮挡问题, 有效地提高了组网拼图数据的质量。
Partial blockage is one of the most important error sources of radar observation. A new shielding region identification algorithm without any dependence on the terrain information with high resolution is proposed. The algorithm first computes the probability of every grid's probability of emerging specified reflectivity exceeding a threshold which is defined as Z threshold only through checking the continuous operational radar observation data without using any sample database established manually, and then selects adequate probability threshold which is defined as C threshold to delineate the region with different shielding characters. Applying the algorithm with the continuous operational observation dataset during the period between May and October in 2012 at Jinahua Quhou and Shangrao, the completely shielding region, partially shielding region and no shielding region on 3 km CAPPI are derived separately. With the comparison of the observation data at April 29 in 2013 in the overlapping region on the 3 km CAPPI between Jinhua, Quzhou, Shangrao, Hangzhou and Huangshan, the validation of the identification results of the three radar sites are tested and the parameter sensibility of the Z and C threshold is analyzed, too. The multi-radar mosaic algorithm is optimized applying the partial shielding region results of the radar sites above to enhance the quality of multi-mosaic. The results show that the shielding region with different characters can be identified effectively by selecting adequate Z and C threshold; the selection of the Z and C threshold is the key and moderate Z threshold is beneficial to the further selection of the C threshold; the partial shielding effects can be easily eliminated based on the identification results and the data quality of multi-radar mosaic can be enhanced effectively based on the optimized multi-radar mosaic algorithm.
[1]Zawadzki I. Factors affecting the precision of radar measurements of rain[R]. Preprints, 22d Int. Conf. on Radar Meteorology, Zurich, Switzerland, Amer Meteor Soc, 1984: 251-256.
[2]Germann U, Joss J. Operational measurement of precipitation in mountainous terrain[M]//Peter M ed. Weather Radar: Principles and Advanced Applications, Springer-Verlag, 2003: 52-77.
[3]Dinku T, Anagnostou E N, Borga M. Improving radar-based estimation of rainfall over complex terrain[J]. J Appl Meteor, 2002, 41: 1163-1178.
[4]Krajewski W F, Villarini G, Smith J A. RADAR rainfall uncertainties: Where are we after thirty years of effort[J]. Bull Amer Meteor Soc, 2010, 91: 87-94.
[5]Delrieu G, Creutin J D, Andrieu H. Simulation of radar mountain returns using a digitized terrain model[J]. J Atmos Oceanic Technol, 1995, 12: 1038-1049.
[6]Delrieu G, Caoudal S, Creutin J D. Feasibility of using mountain return for the correction of ground-based x-band weather radar data[J]. J Atmos Oceanic Technol, 1997, 14: 368-385.
[7]Andrieu H, Creutin J D, Delrieu G, et al. Use of a weather radar for the hydrology of a mountainous area. Part I: Radar measurement interpretation[J]. J Hydrol, 1997, 193: 1-25.
[8]Pellarin T, Delrieu G, Saulnier G M, et al. Hydrologic visibility of weather radar systems operating in mountainous regions: case study for the Ardeche Catchment (France)[J]. J Hydrometeorol, 2002, 3: 539-555.
[9]O'Bannon T. Using a terrain-based' hybrid scan to improve WSR-88D precipitation estimates[C]. Preprints, 28<sup>th</sup> Conf on Radar Meteorology, Austin, TX, Amer Meteor Soc, 1997: 506-507.
[10]Fulton, Richard A, Jay P B, et al. The WSR-88D Rainfall Algorithm[J]. Wea Forecasting, 1998, 13: 377-395.
[11]Zhang J, Howard K, Langston C, et al. National mosaic and multi-sensor qpe (nmq) system: description, results, and future plans[J]. Bull Amer Meteor Soc, 2011, 92: 1321-1338.
[12]Gabella M, Perona G. Simulation of the orographic influence on weather radar using a geometric-optics approach[J].J Atmos Oceanic Technol, 1998, 15(6): 1485-1494.
[13]Kucera P A, Krajewski W F, Young C B. Radar beam occultation studies using GIS and DEM technology: An example study of Guam[J]. J Atmos Oceanic Technol, 2004, 21: 995-1006.
[14]Krajewski W F, Ntelekos A A, Goska R. A GIS-based methodology for the assessment of weather radar beam blockages in mountainous regions: two examples from the USNEXRAD network[J]. Computers& Geosciences, 2006, 32(3): 283-302.
[15]Bech J, Gjertsen U, Haase G. Modelling weather radar beam propagation and topographical blockage at northern high latitudes[J]. Quart J Roy Meteorol Soc, 2007, 133: 1191-1204.
[16]张亚萍, 刘钧, 夏文梅, 等. 雷达定量估测区域降水波束阻挡系数的计算[J]. 南京气象学院学报, 2002, 25(5): 640-647.
[17]Zhang Yaping, Chen MingHun. Dynamical weather radar beam blockage correction[C].Digital Manufacturing and Automation (ICDMA), 2010 International Conference, 2010(1): 67-70.
[18]肖艳姣, 刘黎平. 新一代天气雷达网资料的三维格点化及拼图方法研究[J]. 气象学报, 2006, 64(5): 647-656.
[19]王红艳, 刘黎平, 王改利, 等. 多普勒天气雷达三维数字组网系统开发及应用[J]. 应用气象学报, 2009, 20(2): 214-224.
[20]庄薇, 刘黎平, 王改利, 等. 青藏高原复杂地形区雷达估测降水方法研究[J]. 高原气象, 2013, 32(5): 1224-1235
[21]肖艳姣, 刘黎平, 杨洪平. 基于天气雷达网三维拼图的混合反射率因子生成技术[J]. 气象学报, 2008, 66(3): 470-473.
[22]刘黎平, 吴林林, 杨引明. 基于模糊逻辑的分步式超折射地物回波识别方法的建立和效果分析[J]. 气象学报, 2007, 65(2): 251-260.
[23]庄薇, 刘黎平, 余燕群, 等. 雷达地物回波模糊逻辑法识别的改进及效果检验[J]. 气象学报, 2012, 70(3): 576-584.
[24]江源, 刘黎平, 庄薇.多普勒天气雷达地物回波特征及其识别方法改进[J]. 应用气象学报, 2009, 20(2): 203-213.
