Coordinate-RNN for error correction on numerical weather prediction

Chanjong Yu; Heewoong Ahn; Junhee Seok

doi:10.23919/ELINFOCOM.2018.8330699

Coordinate-RNN for error correction on numerical weather prediction

Chanjong Yu, Heewoong Ahn, Junhee Seok

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

In this work, we present a coordinate-based Recurrent Neural Networks (RNN) for error correction on the Numerical Weather Prediction (NWP) model. We show that the output errors on NWP have spatial and temporal properties, which is collinear with meteorological data. The correction model reflects these characteristics by encompassing the latitude and longitude coordinates as direct inputs to RNN. Examined with the NWP data in Korea, the proposed RNN-based correction reduces the humidity prediction errors by 4.8% and 4.2% compared to the predictions without correction and with simple linear correction, respectively. The overall result highlights the promise of our approach.

Original language	English
Title of host publication	International Conference on Electronics, Information and Communication, ICEIC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-3
Number of pages	3
ISBN (Electronic)	9781538647547
DOIs	https://doi.org/10.23919/ELINFOCOM.2018.8330699
Publication status	Published - 2018 Apr 2
Event	17th International Conference on Electronics, Information and Communication, ICEIC 2018 - Honolulu, United States Duration: 2018 Jan 24 → 2018 Jan 27

Publication series

Name	International Conference on Electronics, Information and Communication, ICEIC 2018
Volume	2018-January

Other

Other	17th International Conference on Electronics, Information and Communication, ICEIC 2018
Country/Territory	United States
City	Honolulu
Period	18/1/24 → 18/1/27

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea grant (NRF-2016R1D1A1B03931077, NRF-2017R1C1B2002850) as well as a grant from Korea Meteorological Administration.

Publisher Copyright:
© 2018 Institute of Electronics and Information Engineers.

Keywords

Meteorological data
coordinates
error correction
numerical weather prediction model
recurrent neural network

ASJC Scopus subject areas

Information Systems
Computer Networks and Communications
Computer Science Applications
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.23919/ELINFOCOM.2018.8330699

Cite this

Yu, C., Ahn, H., & Seok, J. (2018). Coordinate-RNN for error correction on numerical weather prediction. In International Conference on Electronics, Information and Communication, ICEIC 2018 (pp. 1-3). (International Conference on Electronics, Information and Communication, ICEIC 2018; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ELINFOCOM.2018.8330699

Coordinate-RNN for error correction on numerical weather prediction. / Yu, Chanjong; Ahn, Heewoong; Seok, Junhee.
International Conference on Electronics, Information and Communication, ICEIC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-3 (International Conference on Electronics, Information and Communication, ICEIC 2018; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yu, C, Ahn, H & Seok, J 2018, Coordinate-RNN for error correction on numerical weather prediction. in International Conference on Electronics, Information and Communication, ICEIC 2018. International Conference on Electronics, Information and Communication, ICEIC 2018, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-3, 17th International Conference on Electronics, Information and Communication, ICEIC 2018, Honolulu, United States, 18/1/24. https://doi.org/10.23919/ELINFOCOM.2018.8330699

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abstract = "In this work, we present a coordinate-based Recurrent Neural Networks (RNN) for error correction on the Numerical Weather Prediction (NWP) model. We show that the output errors on NWP have spatial and temporal properties, which is collinear with meteorological data. The correction model reflects these characteristics by encompassing the latitude and longitude coordinates as direct inputs to RNN. Examined with the NWP data in Korea, the proposed RNN-based correction reduces the humidity prediction errors by 4.8% and 4.2% compared to the predictions without correction and with simple linear correction, respectively. The overall result highlights the promise of our approach.",

keywords = "Meteorological data, coordinates, error correction, numerical weather prediction model, recurrent neural network",

author = "Chanjong Yu and Heewoong Ahn and Junhee Seok",

note = "Funding Information: This work was supported by the National Research Foundation of Korea grant (NRF-2016R1D1A1B03931077, NRF-2017R1C1B2002850) as well as a grant from Korea Meteorological Administration. Publisher Copyright: {\textcopyright} 2018 Institute of Electronics and Information Engineers.; 17th International Conference on Electronics, Information and Communication, ICEIC 2018 ; Conference date: 24-01-2018 Through 27-01-2018",

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N2 - In this work, we present a coordinate-based Recurrent Neural Networks (RNN) for error correction on the Numerical Weather Prediction (NWP) model. We show that the output errors on NWP have spatial and temporal properties, which is collinear with meteorological data. The correction model reflects these characteristics by encompassing the latitude and longitude coordinates as direct inputs to RNN. Examined with the NWP data in Korea, the proposed RNN-based correction reduces the humidity prediction errors by 4.8% and 4.2% compared to the predictions without correction and with simple linear correction, respectively. The overall result highlights the promise of our approach.

AB - In this work, we present a coordinate-based Recurrent Neural Networks (RNN) for error correction on the Numerical Weather Prediction (NWP) model. We show that the output errors on NWP have spatial and temporal properties, which is collinear with meteorological data. The correction model reflects these characteristics by encompassing the latitude and longitude coordinates as direct inputs to RNN. Examined with the NWP data in Korea, the proposed RNN-based correction reduces the humidity prediction errors by 4.8% and 4.2% compared to the predictions without correction and with simple linear correction, respectively. The overall result highlights the promise of our approach.

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BT - International Conference on Electronics, Information and Communication, ICEIC 2018

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Coordinate-RNN for error correction on numerical weather prediction

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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