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

沸腾能有效地将云中过冷的液态水转化为冰，从而对降水的形成和演化产生强烈的影响。然而，在遥感观测中很难识别，甚至很难量化，也很难在大气模型中忠实地表示出来。通常，在观测和模型研究中，只考虑以小球形式的强烈边缘运动。为了更好地定量了解结圈过程，PROMPOMODORI将结合来自德国C波段天气雷达网的扫描偏振测量、来自DWD ICON-D2预报模式的业务性短期天气预报，以及来自在于利希乔伊斯和慕尼黑大学气象研究所运行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，也来自德国天气雷达网的C波段雷达。垂直指向的多普勒雷达测量将被用来通过利用雾化对粒子终端下落速度的影响来恢复雷达上方狭窄大气柱中的雾化程度。然后，通过使用Joyce云雷达数据的机器学习方法，将这些Riming反演与CB场、极化矩和ICON-D2(温度)动态变量的空间场相关联。PROM-Pomodori将主要通过将多普勒雷达测量的(本地)边缘提取转移到极化和热力学变量的(空间)场中，研究德国南部部分地区典型降水条件下降水云中边缘形成的空间变异性。PROM-Pomodori将调查从Riming检索得到的可变性如何与ICON-D2网格分辨率相关，以及是否在次网格尺度上观察到Riming的显著空间可变性。PROM-Pomodori还将通过将检索结果与DWD ADWICE系统进行比较，探索Riming的定量识别是否有可能被用作飞机结冰的指标，DWD ADWICE系统提供了对空中交通至关重要的结冰潜力地区的地图。

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.