基于深度学习的积层混合云对流泡降水粒子特征研究

A study on particle characteristics of generating cells in stratiform-cumulus mixed cloud based on convolution neural network

为实现对降水粒子的高精准分类,整理3 a机载探测降水粒子图像,构建山东省降水粒子图像数据集(Shandong Province Precipitation Particle Image Dataset,SD-PPID)。结合多维度混和的模型放缩方法,提出一种基于EfficientNet卷积神经网络的降水粒子识别模型(A Precipitation particle Recogni⁃tion model based on EfficientNet convolutional neural Network,PREN)。通过多模型、多指标评价对比,验证了PREN模型具有较好的性能和分类识别能力,模型的识别准确率、精准率和召回率均为98%。使用PREN模型分析对流泡降水粒子特征,选取2次典型积层混合云降水过程的3个时段,结合机载Ka波段云雷达(Airborne Ka-Band Precipitation Cloud Radar,KPR)和DMT粒子测量系统(Droplet Mea⁃surement Technologies)分析对流泡内部与外部、不同强度和不同高度的降水粒子形状占比,并研究其降水机制。结果表明,PREN可有效识别对流泡降水粒子的特征。对流泡内,主要是球状、针状、不规则状和柱状降水粒子,而对流泡外降水粒子主要为球状和针状。不同强度的对流泡云微物理参数各不相同。降水成熟阶段对流泡内霰粒子和针状粒子占比高于消散阶段,降水成熟阶段降水粒子平均弦长415μm,而消散阶段粒子平均弦长367μm。对流泡上部降水粒子以球状和六边形板状为主,主要通过凝华过程增长。在0℃层,不规则状粒子和柱状粒子的比例增加,融化过程与动力条件有利于碰并增长形成不规则状粒子,柱状粒子主要来自于高层掉落。

Arranging 3 years’worth of airborne precipitation particle images to construct a precipitation particle image dataset in Shan⁃dong Province.Building a precipitation particle recognition model based on EfficientNet convolutional neural network,named PREN(Precipitation particle Recognition model based on EfficientNet convolutional neural Network).The accuracy rate is 98%,and the multimodel and multi-index evaluation and comparison experiments verify that PREN demonstrates excellent robustness and generalization ability.Taking typical stratiform-cumulus mixed cloud precipitation as two examples(total 3 time periods),PREN is applied to the par⁃ticle characteristics analysis of generating cells.Combined with airborne Ka-band cloud radar and DMT particle measurement system,an analysis conducted on the shape proportion of precipitation particles inside and outside the generating cells and indifferent intensity generating cells,revealing the precipitation mechanism.The results show that the shapes of precipitation particles in the generating cells are mainly spherical,needle-like,irregular and columnar.Precipitation particles outside the generating cells are mostly spherical and needle-like.The cloud microphysical parameters in the generating cells with different intensities vary.The proportion of graupel and needle particles in the precipitation maturity stage is higher than that in the dissipation stage.The average chord length of precipi⁃tation particles in the maturity stage is 415μm.While the average chord length of particles in dissipation stage is 367μm.The par⁃ticles on the top of generating cells are mainly spherical and hexagonal,primarily growing through the process of deposition.The ratio of irregular particles and columnar particles in the 0℃are increasing,and the melting process and dynamic conditions favor aggrega⁃tion and growth,forming irregular particles,while columns mainly originate from the upper levels of the atmosphere.