Temporal Super-Resolution, Ground Adjustment, and Advection Correction of Radar Rainfall Using 3-D-Convolutional Neural Networks-Reference-Cited by-同舟云学术

Temporal Super-Resolution, Ground Adjustment, and Advection Correction of Radar Rainfall Using 3-D-Convolutional Neural Networks

Published:2024 Issue: Volume:62 Page:1-10
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Polz Julius¹^ORCID,Glawion Luca¹^ORCID,Gebisso Hiob²,Altenstrasser Lukas²^ORCID,Graf Maximilian³,Kunstmann Harald¹^ORCID,Vogl Stefanie²,Chwala Christian¹^ORCID

Affiliation:

1. Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany

2. Department of Informatics and Mathematics, Munich University of Applied Sciences, Munich, Germany

3. Chair of Regional Climate and Hydrology, Institute of Geography, University of Augsburg, Augsburg, Germany

Funder

German Research Foundation within German Research Foundation (DFG) Research Unit RealPEP

Helmholtz Association (HGF)-Innopool

Federal Ministry of Education and Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10453974.pdf?arnumber=10453974

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3. Probabilistic radar-gauge merging by multivariate spatiotemporal techniques;Pulkkinen;J. Hydrol.,2016

4. Real-time radar rainfall estimation. Part I: Algorithm formulation;Anagnostou;J. Atmos. Ocean. Technol.,1999

5. Correcting temporal sampling error in radar-rainfall: Effect of advection parameters and rain storm characteristics on the correction accuracy;Seo;J. Hydrol.,2015