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Computer Science > Computer Vision and Pattern Recognition

arXiv:2109.09289 (cs)
[Submitted on 20 Sep 2021 (v1), last revised 22 Sep 2022 (this version, v2)]

Title:TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

Authors:Muhammed Sit, Bong-Chul Seo, Ibrahim Demir
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2109.09289 [cs.CV]
  (or arXiv:2109.09289v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2109.09289

Submission history

From: Muhammed Sit [view email]
[v1] Mon, 20 Sep 2021 03:58:52 UTC (958 KB)
[v2] Thu, 22 Sep 2022 04:14:44 UTC (700 KB)
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