PRISTINE - Polarimetric Radar simulations with realistic Ice and Snow properties and mulTI-frequeNcy consistency Evaluation

PRISTINE - 具有真实冰雪特性和多频率一致性评估的偏振雷达模拟

基本信息

项目摘要

Project PRISTINE aims at developing a polarimetric radar forward operator that is based on realistic snow particle properties. A reliable forward operator is an essential tool for the overall PROM program since it is a core component of data assimilation algorithms and model evaluation studies. The existing forward operators based on the T-matrix approximation for homogeneous, spheroid-shaped cloud and precipitation particles have shown substantial deviations with respect to the observed polarimetric signatures of frozen hydrometeors. Those deviations have been linked to the simplifications introduced by the T-matrix approximation. PRISTINE will build upon the efficient polarimetric radar simulator framework EMVORADO, which covers many other aspects of radar simulation and is coupled to the weather models COSMO and ICON. The project will perform novel single-scattering computations using the Discrete Dipole Approximation (DDA) method. The accurate DDA calculations require additional assumptions regarding the detailed hydrometeor structure. PRISTINE will address this issue by means of an innovative modeling technique. The snow particle shapes will be generated using a combination of detailed Lagrangian cloud modeling and an explicit snow particle simulator. By doing so, PRISTINE aims at finding the snow shapes that are representative of the ensemble mean properties of snow. Moreover, the uncertainties, in terms of snow particle microphysical and polarimetric scattering properties, that arise from the stochastic nature of snow growth processes and the irregular shape of snow particles will be evaluated. The single-scattering properties will be used to replace the scattering modules for cloud ice and snow in EMVORADO that are currently based on the T-Matrix approximation. The performance of the updated scattering simulations will be evaluated against multi-frequency, Doppler-resolved, polarimetric radar observations. The successively updated versions of EMVORADO and of the DDA scattering data will be made available to the PROM project partners and the larger scientific community.
PRISTINE项目旨在开发一种基于真实雪粒子特性的偏振雷达前向操作员。可靠的正演算子是整个PROM程序的重要工具,因为它是数据同化算法和模式评估研究的核心组成部分。现有的前向运营商的基础上的T-矩阵近似均匀,球状的云和降水粒子已经显示出显着的偏差,相对于所观察到的偏振签名冻结水凝物。这些偏差与T矩阵近似所引入的简化有关。 PRISTINE将建立在有效的极化雷达模拟器框架EMVORADO的基础上,该框架涵盖雷达模拟的许多其他方面,并与COSMO和ICON天气模型相结合。该项目将使用离散偶极子近似(DDA)方法进行新颖的单次散射计算。 精确的DDA计算需要关于详细的水凝物结构的额外假设。 PRISTINE将通过创新的建模技术来解决这个问题。 雪粒子形状将使用详细的拉格朗日云建模和显式雪粒子模拟器的组合来生成。通过这样做,PRISTINE旨在找到代表雪的总体平均属性的雪的形状。 此外,不确定性,在雪粒子的微物理和偏振散射特性,所产生的随机性质的雪生长过程和雪粒子的不规则形状将进行评估。单次散射特性将被用来取代目前基于T矩阵近似的EMVORADO中云冰和雪的散射模块。更新的散射模拟的性能将根据多频,多普勒分辨,极化雷达观测进行评估。连续更新的EMVORADO和DDA散射数据将提供给PROM项目合作伙伴和广大科学界。

项目成果

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Dr. Ulrich Blahak其他文献

Dr. Ulrich Blahak的其他文献

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{{ truncateString('Dr. Ulrich Blahak', 18)}}的其他基金

Sensitivities of deep convective systems on temperature stratification, surface parameters and orography
深对流系统对温度分层、表面参数和地形的敏感性
  • 批准号:
    5426915
  • 财政年份:
    2004
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes
Synergie eines effizienten polarimetrischen Radarvorwärtsoperators und eines hochentwickelten Klassifizierungsalgorithmus zur verbesserten Repräsentierung von Hydrometeoren im ICON-Modell (Operation Hydrometeors Part II)
高效的极化雷达前向算子和复杂的分类算法的协同作用,可改善 ICON 模型中水凝物的表示(水凝物操作第二部分)
  • 批准号:
    408027387
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes

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Collaborative Research: Using Polarimetric Radar Observations, Cloud Modeling, and In Situ Aircraft Measurements for Large Hail Detection and Warning of Impending Hail
合作研究:利用偏振雷达观测、云建模和现场飞机测量来检测大冰雹并预警即将发生的冰雹
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Collaborative Research: Using Polarimetric Radar Observations, Cloud Modeling, and In Situ Aircraft Measurements for Large Hail Detection and Warning of Impending Hail
合作研究:利用偏振雷达观测、云建模和现场飞机测量来检测大冰雹并预警即将发生的冰雹
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    2344260
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Framework construction and engineering development of polarimetric-interferometric synthetic aperture radar based on phasor-quaternion neural networks
基于相量四元数神经网络的偏振干涉合成孔径雷达框架构建及工程开发
  • 批准号:
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  • 财政年份:
    2023
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Rapid-scan Polarimetric Radar Data Collection and Analysis of the Wind Field in Severe Convective Storms and Tornadoes
强对流风暴和龙卷风风场的快速扫描偏振雷达数据采集和分析
  • 批准号:
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CAREER: Artificial Intelligence for Polarimetric Radar Remote Sensing of Precipitation
职业:用于降水偏振雷达遥感的人工智能
  • 批准号:
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  • 财政年份:
    2023
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Collaborative Research: EAGER--Initial Evaluation of Polarimetric Phased Array Radar for the Study of Storm Electrification and Lightning
合作研究:EAGER——用于风暴带电和闪电研究的偏振相控阵雷达的初步评估
  • 批准号:
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    --
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Collaborative Research: EAGER--Initial Evaluation of Polarimetric Phased Array Radar for the Study of Storm Electrification and Lightning
合作研究:EAGER——用于风暴带电和闪电研究的偏振相控阵雷达的初步评估
  • 批准号:
    2310337
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Collaborative Research: Comparison between In-situ and Polarimetric Radar Hail Observations in Convective Storms
合作研究:对流风暴中原位和偏振雷达冰雹观测的比较
  • 批准号:
    2221719
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Implications and Applications of Emerging Polarimetric Synthetic Aperture Radar for the Retrieval of Freshwater Ice Properties
新兴偏振合成孔径雷达对淡水冰性质反演的意义和应用
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    Discovery Grants Program - Individual
Implications and Applications of Emerging Polarimetric Synthetic Aperture Radar for the Retrieval of Freshwater Ice Properties
新兴偏振合成孔径雷达对淡水冰性质反演的意义和应用
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    RGPNS-2021-02742
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  • 项目类别:
    Discovery Grants Program - Northern Research Supplement
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