TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs
This work addresses the need for higher temporal resolution in rainfall data to support hydrological and flood forecasting studies, representing an incremental improvement in domain-specific methods.
The study tackled the problem of low temporal resolution in radar rainfall data by developing a deep learning approach to increase time resolution, achieving improved results compared to optical flow interpolation and a CNN baseline.
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.