Convolutional Neural Networks for Hydrometeor Classification using Dual Polarization Doppler Radars Traditional fuzzy logic hydrometeor classification algorithm is a common way to classify precipitation type from dual polarization doppler radar. We propose a deep learningbased method to estimate hydrometeors efficiently using observed radar variables such as horizontal reflectivity (Z H ), differential reflectivity (Z DR ), correlation coefficient (xcfx81 HV ) and specific differential phase (K DP ) from National Weather Service NEXRAD collected at Vance AFB facility at the first elevation angle from January 1st, 2015 to July 31th, 2019. We stack matrices of values from these four polarimetric variables as one 3D array. Samples are preprocessed and divided into training, validation and test set with four target hydrometeor categories (Ice Crystals (IC), Dry Snow (DS), Light andor Moderate Rain (RA) and Big Drops (rain) (BD)). We developed and optimized five Convolutional Neural Networks (CNNs) architectures and achieved an accuracy of 87.23% and 93.736% respectively using modified ResNet with two different input data selection strategies for hydrometeor classification. Training data selection strategies were important to ensure use of available samples in training for robust performance evaluated by applying the models to novel time period beyond what was use to train the model. Seasonal variation in atmospheric conditions lead to seasonal patterns of liquid vs solid forms of precipitation, that poses challenge for classifier and offer insights into domain specific approaches required for problem of hydrometeor identification. Computationally efficient and scalable approach for classification of hydrometeors offer opportunties to effectively use the large volumes of rich time series of radar observations that are becoming increasingly available.

Resolving Elevation Ambiguity in 1D Radar Array Measurements using Deep Learning Motivated by requirements for future automotive radar, we study the problem of resolving target elevation from measurements by a onedimensional horizontal radar antenna array. This is a challenging and illposed problem, since such measurements contain only indirect and highly ambiguous elevation cues. As a consequence, traditional modelbased approaches fail. We instead propose to use a machinelearningbased approach that learns to exploit the subtle elevation cues and prior knowledge of the scene from the data. We design an encoderdecoder structured deep convolutional neural network that takes a radar return intensity image in the rangeazimuth plane as input and produces a depth image in the elevationazimuth plane as output. We train the network with over 200 000 radar frames collected in highway environments. Through experimental evaluations, we demonstrate the feasibility of resolving the highly ambiguous elevation information in such environments.