  The temporal and spatial resolution of rainfall data is crucial for
environmental modeling studies in which its variability in space and time is
considered as a primary factor. Rainfall products from different remote sensing
instruments (e.g., radar, satellite) have different space-time resolutions
because of the differences in their sensing capabilities and post-processing
methods. In this study, we developed a deep learning approach that augments
rainfall data with increased time resolutions to complement relatively lower
resolution products. We propose a neural network architecture based on
Convolutional Neural Networks (CNNs) to improve the temporal resolution of
radar-based rainfall products and compare the proposed model with an optical
flow-based interpolation method and CNN-baseline model. The methodology
presented in this study could be used for enhancing rainfall maps with better
temporal resolution and imputation of missing frames in sequences of 2D
rainfall maps to support hydrological and flood forecasting studies.
