TempNet - temporal super-resolution of radar rainfall products with residual CNNs

Muhammed Ali Sit; Bongchul Seo; Ibrahim Demir

doi:10.2166/hydro.2023.196

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TempNet - temporal super-resolution of radar rainfall products with residual CNNs

Journal article

Open access

Peer reviewed

TempNet - temporal super-resolution of radar rainfall products with residual CNNs

Muhammed Ali Sit, Bongchul Seo and Ibrahim Demir

Journal of hydroinformatics, Vol.25(2), pp.552-566

03/03/2023

DOI: 10.2166/hydro.2023.196

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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), namely TempNet, 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. While TempNet achieves a mean absolute error of 0.332 mm/h, comparison methods achieve 0.35 and 0.341, respectively. 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.

Computer Science

Engineering

Physical Sciences

Technology

Computer Science, Interdisciplinary Applications

Engineering, Civil

Environmental Sciences

Environmental Sciences & Ecology

Life Sciences & Biomedicine

Science & Technology

Water Resources

UIOWA OA Agreement

Details

Title: Subtitle: TempNet - temporal super-resolution of radar rainfall products with residual CNNs
Creators: Muhammed Ali Sit - University of Iowa
Bongchul Seo - University of Iowa
Ibrahim Demir - University of Iowa
Resource Type: Journal article
Publication Details: Journal of hydroinformatics, Vol.25(2), pp.552-566
DOI: 10.2166/hydro.2023.196
ISSN: 1464-7141
eISSN: 1465-1734
Publisher: IWA Publishing
Number of pages: 15
Language: English
Date published: 03/03/2023
Academic Unit: Electrical and Computer Engineering; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Injury Prevention Research Center
Record Identifier: 9984385050202771

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