TempNet – Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.2166/hydro.2023.196. This is version 1 of this Preprint.


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Muhammed Sit , Bong-Chul Seo, Ibrahim Demir


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.




Civil and Environmental Engineering, Engineering


Deep learning, radar rainfall, radar echoes, downscaling


Published: 2022-09-22 17:13

Last Updated: 2022-09-23 00:13


CC BY Attribution 4.0 International

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