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Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction

The weather radar will receive a lot of non-meteorological echo information during the body scan process, such as: object echoes, co-wave interference echoes, airplanes, flocks of birds, etc. These non-meteorological echoes will cause pollution to normal meteorological echoes, thereby affecting the accuracy of the output product. The traditional weather radar beam blocking correction method is to interpolate and fill according to the adjacent radial data, but this method cannot make full use of the deep laws of radar data and is relatively limited. In this paper, the weather radar beam blocking correction problem is regarded as an image completion problem, a fully convolutional neural network with dense connection is designed, and the multiclass cross entropy loss function is used to optimize the model. The model proposed in this paper has achieved good performance in the evaluation indicators in the field of meteorology and image completion.