Coordinate-RNN for error correction on numerical weather prediction

Chanjong Yu, Heewoong Ahn, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationInternational Conference on Electronics, Information and Communication, ICEIC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-3
Number of pages3
ISBN (Electronic)9781538647547
DOIs
Publication statusPublished - 2018 Apr 2
Event17th International Conference on Electronics, Information and Communication, ICEIC 2018 - Honolulu, United States
Duration: 2018 Jan 242018 Jan 27

Publication series

NameInternational Conference on Electronics, Information and Communication, ICEIC 2018
Volume2018-January

Other

Other17th International Conference on Electronics, Information and Communication, ICEIC 2018
Country/TerritoryUnited States
CityHonolulu
Period18/1/2418/1/27

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

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