Accurate Characterization of Winter Precipitation Using Multi-Angle Snowflake Camera, Visual Hull, Advanced Scattering Methods, and Polarimetric Radar

使用多角度雪花相机、视觉船体、先进散射方法和偏振雷达准确表征冬季降水

• 批准号: 1344862
• 负责人: Branislav Notaros
• 金额: $ 58.8万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-12-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1344862&HistoricalAwards=false
• 关键词: Accurate; Characterization; Winter; Precipitation; Using

This award will establish a novel approach to characterization of winter precipitation and modeling of associated polarimetric radar observables, with a longer-term goal to significantly improve the radar-based quantitative precipitation estimation in stronger, more hazardous, winter events. The principal enabling technologies are (i) multi-angle snowflake camera (MASC), (ii) visual hull (VH) geometrical method for reconstruction of 3D hydrometeor shapes, (iii) fast and accurate advanced higher order computational electromagnetics (CEM) scattering methods, and (iv) fully polarimetric data from the advanced CSU-CHILL radar. The main objectives of this research and methods to be employed are:- Microphysical and realistic 3D-geometrical characterization of ice particles using MASC- Combining fall speed and particle geometry to estimate density-"size" power laws, snow rates- Calculations of "particle-by-particle" scattering matrices and polarimetric radar observables- Sensitivity studies of various parameters of scattering models in simulations of radar measurables- Analysis and cross-validation of CSU-CHILL and MASC/VH/CEM data for winter precipitation events- Derivation and validation of radar-based snow rate relations for previously classified particle types Intellectual merit is contained in and warranted by the research objectives described above. Overall, it is in the synergistic use of new research instrumentation (MASC) coupled with accurate, efficient, versatile, and robust CEM scattering methods as well as state-of-the-art polarimetric radar (with exceptional polarization purity) to substantially increase the accuracy of modeling of radar observables and characterization of winter precipitation. This is the first time real (measured) snowflake images will be used with highly accurate and efficient realistic scattering calculations, to obtain radar measurable parameters, which will be validated by a highly precise polarimetric radar. This will be the first set of high-quality multi-year data for scattering matrices and the full set of radar observables for MASC-based classified particle types constituting winter precipitation. The full-wave CEM modeling approach to atmospheric particle scattering based primarily on the higher order method of moments (MoM) will be able to overcome all shortcomings of both the T-matrix and the DDA methods. Snowflake 3D shape reconstruction by the VH method based on three MASC photographs is much more accurate than any other available snowflake shape reconstruction examples.This research will significantly improve, in a longer term, the radar-based estimation of liquid equivalent snow rates near the surface in stronger, more hazardous, winter events by first classification of precipitation type followed by quantification. Winter precipitation studies using the combined MASC and OTT-Pluvio snow gauge will impact microphysical parameterizations used in advanced cloud resolving models. "Look-up tables" with comprehensive scattering properties of ice hydrometeors, obtained by MASC/VH/CEM-methods, at multiple radar/radiometric sensor frequencies from 3-150 GHz, should be of interest and use for many researchers in the field. Radar-based snow rate relations will be directly applicable to improved quantification of winter precipitation by the WSR-88D network. This research is also aimed at establishing and promoting the full-wave CEM modeling approach and the higher order MoM as an enabling resource and technology for future research in atmospheric particle scattering analysis. Applications may be extended to radiometric cloud/snow detection and mm-wave radars. Having potential to change the way characterization of winter precipitation is done; this research is transformative in its nature. Educational and outreach activities include training of two Ph.D. students, a new course on scattering by precipitation particles, advanced workshops with a series of seminars/lectures on the topics of the project for graduate students, faculty, and scientists within the Colorado Front Range, and K-12 outreach workshops on Snowflake Research for high school students.

该奖项将建立一种新的方法来表征冬季降水和相关极化雷达观测值的建模，其长期目标是显着改善基于雷达的定量降水估计，在更强，更危险的冬季事件中。主要的使能技术是（一）多角度雪花照相机（MASC），（二）用于重建三维水凝物形状的可视船体（VH）几何方法，（三）快速和准确的高级高阶计算电磁散射方法，以及（四）来自高级CSU-CHILL雷达的全极化数据。这项研究的主要目标和采用的方法是：- 使用MASC的冰粒的微物理和现实3D几何表征-组合下落速度和粒子几何形状以估计密度-“尺寸”幂律，雪率.“逐粒子”散射矩阵和极化雷达观测值的计算.雷达观测值模拟中散射模型各种参数的灵敏度研究.冬季降水事件的CSU-CHILL和MASC/VH/CEM数据的分析和交叉验证.雷达的推导和验证基于先前分类的颗粒类型的雪率关系上述研究目标包含并保证了知识价值。总的来说，它是在新的研究仪器（MASC）的协同使用加上准确，高效，多功能，强大的CEM散射方法以及国家的最先进的极化雷达（具有特殊的极化纯度），以大大提高雷达观测和冬季降水特征建模的准确性。这是第一次将真实的（测量的）雪花图像与高精度和高效的真实散射计算一起使用，以获得雷达可测量参数，这些参数将由高精度极化雷达进行验证。这将是第一套高质量的散射矩阵多年数据，以及构成冬季降水的基于MASC的分类颗粒类型的全套雷达可观测数据。基于高阶矩量法（MoM）的大气粒子散射全波CEM建模方法能够克服T矩阵和DDA方法的所有缺点。雪花三维形状重建的VH方法的基础上，三个MASC的照片是更准确的比任何其他可用的雪花形状重建examines.This研究将显着改善，在较长的时间内，基于雷达的估计液体等效雪率附近的表面更强，更危险的，冬季事件的降水类型的第一个分类，然后量化。使用MASC和OTT-Pluvio雪量计联合进行的冬季降水研究将影响先进云解析模式中使用的微物理参数化。通过MASC/VH/CEM方法在3-150 GHz的多个雷达/辐射传感器频率下获得的具有冰水凝物综合散射特性的“查找表”应该引起该领域许多研究人员的兴趣和使用。基于雷达的降雪率关系将直接适用于WSR-88 D网络对冬季降水量的改进量化。本研究还旨在建立和推广全波CEM建模方法和高阶矩量法，作为未来大气粒子散射分析研究的有利资源和技术。其应用可扩展到辐射云/雪探测和毫米波雷达。有可能改变冬季降水的表征方式;这项研究在本质上是变革性的。教育和外联活动包括培训两名博士。学生，一个新的课程散射的降水粒子，先进的讲习班与一系列研讨会/讲座的主题项目的研究生，教师和科学家在科罗拉多前线范围内，和K-12推广讲习班雪花研究高中学生。