In this paper, we present a deep learning technique for data-driven predictions of wave propagation in fluid medium. The relies on an attention-based convolutional recurrent autoencoder network (AB-CRAN). To construct low-dimensional representation data, employ denoising-based autoencoder. AB-CRAN architecture with long short-term memory cells forms our neural model the time marching features. ...

Abstract. Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles the lower atmosphere commonly linked to through empirical power laws, but these relationships tightly coupled particle microphysical assumptions that do not generalize well different regional climates. Here, we deve...