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颖的方法来表征冬季降水和相关极化雷达可观测数据的建模，其长期目标是在更强、更危险的冬季事件中显着改进基于雷达的定量降水估计。主要支持技术包括 (i) 多角度雪花相机 (MASC)、(ii) 用于重建 3D 水凝物形状的视觉船体 (VH) 几何方法、(iii) 快速准确的先进高阶计算电磁学 (CEM) 散射方法，以及 (iv) 来自先进 CSU-CHILL 雷达的全偏振数据。这项研究的主要目标和采用的方法是： - 使用 MASC 对冰颗粒进行微观物理和真实的 3D 几何表征 - 结合下落速度和颗粒几何形状来估计密度 - “尺寸”幂律、降雪率 - 计算“颗粒” “逐粒子”散射矩阵和极化雷达可观测值 - 雷达可测量值模拟中散射模型各种参数的灵敏度研究 - CSU-CHILL 和 CSU-CHILL 的分析和交叉验证冬季降水事件的 MASC/VH/CEM 数据 - 针对先前分类的颗粒类型基于雷达的雪率关系的推导和验证 上述研究目标包含并保证了智力价值。总体而言，通过协同使用新型研究仪器 (MASC) 与准确、高效、通用且稳健的 CEM 散射方法以及最先进的偏振雷达（具有出色的偏振纯度），可以大幅提高雷达观测值建模的准确性和冬季降水的表征。这是第一次将真实（测量的）雪花图像与高精度和高效的现实散射计算结合使用，以获得雷达可测量参数，并将由高精度偏振雷达进行验证。这将是第一组高质量的散射矩阵多年数据和构成冬季降水的基于 MASC 的分类颗粒类型的全套雷达观测数据。主要基于高阶矩法 (MoM) 的大气颗粒散射全波 CEM 建模方法将能够克服 T 矩阵和 DDA 方法的所有缺点。基于三张 MASC 照片的 VH 方法进行的雪花 3D 形状重建比任何其他可用的雪花形状重建示例都要准确得多。从长远来看，这项研究将显着改进基于雷达的地表附近液体等效雪率的估计在更强、更危险的冬季事件中，首先对降水类型进行分类，然后进行量化。使用 MASC 和 OTT-Pluvio 雪计联合进行的冬季降水研究将影响高级云解析模型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-88D 网络改进的冬季降水量化。这项研究还旨在建立和推广全波 CEM 建模方法和高阶 MoM，作为未来大气颗粒散射分析研究的支持资源和技术。应用可以扩展到辐射云/雪探测和毫米波雷达。有潜力改变冬季降水的表征方式；这项研究本质上是变革性的。教育和外展活动包括培训两名博士。学生，一门关于降水颗粒散射的新课程，为科罗拉多前沿范围内的研究生、教师和科学家举办的一系列关于该项目主题的研讨会/讲座的高级研讨会，以及关于雪花研究的 K-12 外展研讨会高中生。