CNN-based Temporal Super Resolution of Radar Rainfall Products

Muhammed Sit; Bong-Chul Seo; Ibrahim Demir

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CNN-based Temporal Super Resolution of Radar Rainfall Products

Preprint

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CNN-based Temporal Super Resolution of Radar Rainfall Products

Muhammed Sit, Bong-Chul Seo and Ibrahim Demir

ArXiv.org

09/20/2021

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https://arxiv.org/abs/2109.09289View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

The temporal and spatial resolution of rainfall data is crucial for climate change 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 or satellite) provide different space-time resolutions because of the differences in their sensing capabilities. We developed an approach that augments rainfall data with increased time resolutions to complement relatively lower resolution products. This study proposes a neural network architecture based on Convolutional Neural Networks (CNNs) to improve temporal resolution of radar-based rainfall products and compares the proposed model with an optical flow-based interpolation method.

Details

Title: Subtitle: CNN-based Temporal Super Resolution of Radar Rainfall Products
Creators: Muhammed Sit
Bong-Chul Seo
Ibrahim Demir
Resource Type: Preprint
Publication Details: ArXiv.org
ISSN: 2331-8422
Language: English
Date posted: 09/20/2021
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Injury Prevention Research Center
Record Identifier: 9984202250502771

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