Combining radar POlarimetry, weather forecast MOdel outputs and DOppler radar observations for Riming analysis~(POMODORI)

结合雷达偏振测量、天气预报 MODEl 输出和多普勒雷达观测进行 Riming 分析~(POMODORI)

• 批准号: 408012686
• 负责人: Dr. Michael Frech
• 金额: --
• 依托单位: Bundesamt für Meteorologie u Klimatologie MeteoSwiss
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/408012686?language=en
• 关键词: Combining; radar; POlarimetry; weather; forecast

Riming can efficiently transform cloud supercooled liquid water into ice and therefore has a strong impact on the formation and evolution of precipitation. Yet, riming is difficult to identify, or even quantify, in remote sensing observations and to faithfully represent in atmospheric models. Commonly, only intense riming in the form of graupel is considered in observational and modeling studies. To gain a better quantitative understanding of the riming process, PROMPOMODORI will combine scanning polarimetric measurements from the German C-band weather radar network, operational short-term weather predictions from DWD’s ICON-D2 forecast model, and vertically pointing Doppler measurements from high-resolution cloud radars operated at JOYCE in Jülich and at the Meteorological Institute of the University of Munich, and, which is unique, also from the Cband radars of the German weather radar network. Vertically pointing Doppler radar measurements will be used to retrieve the degree of riming throughout a narrow atmospheric column above the radar by exploiting the effect of riming on particle terminal fall velocity. These riming retrievals will then be correlated with the spatial fields of CBand polarimetric moments and ICON-D2 (thermo)dynamic variables by employing a machine learning approach using JOYCE cloud radar data. By essentially transferring the (local) riming retrievals from Doppler radar measurements to the (spatial) fields of polarimetric and thermodynamic variables, PROM-POMODORI will investigate the spatial variability of riming in precipitating clouds for typical precipitation conditions over parts of southern Germany. PROM-POMODORI will investigate how the variability derived from the riming retrievals relates to the ICON-D2 grid resolution and whether significant spatial variability of riming is observed on a sub-grid scale. PROM-POMODORI will also explore whether the quantitative identification of riming has the potential to be used as an indicator of aircraft icing, by comparing the retrieval results with the DWD ADWICE system, which provides maps of regions with icing potentialcritical for air traffic.

日凌能有效地将云中过冷液态水转化为冰，对降水的形成和演变有重要影响。然而，雾凇是很难识别，甚至量化，在遥感观测和忠实地代表在大气模型。通常，在观测和模拟研究中，只考虑以形式出现的强烈日晕。为了更好地定量了解雾凇过程，PROMPOMODORI将结合德国C波段天气雷达网络的联合收割机扫描偏振测量、DWD的ICON-D2预报模型的业务短期天气预报，以及位于Jülich的JOYCE和慕尼黑大学气象研究所的高分辨率云雷达的垂直指向多普勒测量，这是独一无二的，也来自德国气象雷达网的C波段雷达。垂直指向的多普勒雷达测量将用于检索的程度，通过利用粒子终端下降速度的影响，在雷达上方的一个狭窄的大气柱的雾凇。然后，通过使用JOYCE云雷达数据的机器学习方法，将这些riming检索与CB和极化矩的空间场以及ICON-D2（热）动态变量相关。PROM-POMODORI主要通过将多普勒雷达测量的（局部）雾凇反演转移到偏振和热力学变量的（空间）场，研究德国南部部分地区典型降水条件下降水云中雾凇的空间变异性。PROM-POMODORI将调查从雾凇反演得出的变化如何与ICON-D2网格分辨率相关，以及在子网格尺度上是否观察到雾凇的显著空间变化。PROM-POMODORI还将通过将检索结果与DWD ADWICE系统进行比较，探索是否有可能将雾凇的定量识别用作飞机结冰的指标，DWD ADWICE系统提供了具有对空中交通至关重要的结冰可能性的区域地图。