DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Chang-Hoo | ko |
dc.contributor.author | Wonsu Kim | ko |
dc.contributor.author | Wonkyun Joo | ko |
dc.contributor.author | Dongmin Jang | ko |
dc.contributor.author | Yi, Mun Yong | ko |
dc.date.accessioned | 2021-03-22T04:50:12Z | - |
dc.date.available | 2021-03-22T04:50:12Z | - |
dc.date.created | 2021-03-18 | - |
dc.date.created | 2021-03-18 | - |
dc.date.created | 2021-03-18 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | ATMOSPHERE, v.12, no.2 | - |
dc.identifier.issn | 2073-4433 | - |
dc.identifier.uri | http://hdl.handle.net/10203/281713 | - |
dc.description.abstract | Nowcasting is an important technique for weather forecasting because sudden weather changes significantly affect human life. The encoding-forecasting model, which is a state-of-the-art architecture in the field of data-driven radar extrapolation, does not particularly focus on the latest data when forecasting natural phenomena. This paper proposes a weighted broadcasting method that emphasizes the latest data of the time step to improve the nowcasting performance. This weighted broadcasting method allows the most recent rainfall patterns to have a greater impact on the forecasting network by extending the architecture of the existing encoding-forecasting model. Experimental results show that the proposed model is 1.74% and 2.20% better than the existing encoding-forecasting model in terms of mean absolute error and critical success index, respectively. In the case of heavy rainfall with an intensity of 30 mm/h or higher, the proposed model was more than 30% superior to the existing encoding-forecasting model. Therefore, applying the weighted broadcasting method, which explicitly places a high emphasis on the latest information, to the encoding-forecasting model is considered as an improvement that is applicable to the state-of-the-art implementation of data-driven radar-based precipitation nowcasting. | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.title | Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step | - |
dc.type | Article | - |
dc.identifier.wosid | 000622134300001 | - |
dc.identifier.scopusid | 2-s2.0-85101686390 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 2 | - |
dc.citation.publicationname | ATMOSPHERE | - |
dc.identifier.doi | 10.3390/atmos12020261 | - |
dc.contributor.localauthor | Yi, Mun Yong | - |
dc.contributor.nonIdAuthor | Wonsu Kim | - |
dc.contributor.nonIdAuthor | Wonkyun Joo | - |
dc.contributor.nonIdAuthor | Dongmin Jang | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | precipitation nowcasting | - |
dc.subject.keywordAuthor | deep neural network | - |
dc.subject.keywordAuthor | radar extrapolation | - |
dc.subject.keywordAuthor | spatiotemporal modeling | - |
dc.subject.keywordAuthor | encoding-forecasting | - |
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