Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network SCIE SCOPUS
DC Field | Value | Language |
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dc.contributor.author | Park, Sung Hwan | - |
dc.contributor.author | Yoo, Jeseon | - |
dc.contributor.author | Son, Dong Hwi | - |
dc.contributor.author | Kim, Jin Ah | - |
dc.contributor.author | Jung, Hyung-Sup | - |
dc.date.accessioned | 2021-10-27T23:30:03Z | - |
dc.date.available | 2021-10-27T23:30:03Z | - |
dc.date.created | 2021-10-27 | - |
dc.date.issued | 2021-10 | - |
dc.identifier.issn | 2072-4292 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/41674 | - |
dc.description.abstract | Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide more accurate and realistic information. Thus, this study proposed a new method to calibrate ASCAT-based wind speed estimates using artificial neural networks. Herein, a deep neural network (DNN) model was applied. Wind databases collected during a period from 2012 to 2019 by the MetOp-B ASCAT and ten buoy stations in Korean seas were considered for deep learning-based calibration. ASCAT-based wind data and in-situ measurements were collocated in space and time. They were then separated into training and validation sets. A DNN model was designed and trained using multiple input variables such as observation location, sensing date and time, wind speed, and wind direction of the training set. The DNN-based best fit calibration model was evaluated using the validation set. The mean of biases between ASCAT-based and in-situ wind speeds was found to be decreased from 0.41 to 0.05 m/s on average for all buoy locations. The root mean squared error (RMSE) of wind speed was reduced from 1.38 m/s to 0.93 m/s. Moreover, the DNN-based calibration considerably improved the quality of wind speeds of less than 4 m/s, and of high wind speeds of 11–25 m/s. These results suggest that ASCAT-based observations can accurately represent real wind fields if a DNN-based calibration approach is applied. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network | - |
dc.type | Article | - |
dc.citation.title | Remote Sensing | - |
dc.citation.volume | 13 | - |
dc.citation.number | 20 | - |
dc.contributor.alternativeName | 박숭환 | - |
dc.contributor.alternativeName | 유제선 | - |
dc.contributor.alternativeName | 손동휘 | - |
dc.contributor.alternativeName | 김진아 | - |
dc.identifier.bibliographicCitation | Remote Sensing, v.13, no.20 | - |
dc.identifier.doi | 10.3390/rs13204164 | - |
dc.identifier.scopusid | 2-s2.0-85117319162 | - |
dc.identifier.wosid | 000715424000001 | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordPlus | WEST-COAST | - |
dc.subject.keywordPlus | SPEED | - |
dc.subject.keywordPlus | WAVE | - |
dc.subject.keywordPlus | QUIKSCAT | - |
dc.subject.keywordPlus | BUOY | - |
dc.subject.keywordPlus | VALIDATION | - |
dc.subject.keywordPlus | ASCAT | - |
dc.subject.keywordPlus | VECTORS | - |
dc.subject.keywordPlus | CLIMATE | - |
dc.subject.keywordPlus | HEIGHT | - |
dc.subject.keywordAuthor | MetOp-B | - |
dc.subject.keywordAuthor | ASCAT | - |
dc.subject.keywordAuthor | deep neural network | - |
dc.subject.keywordAuthor | Korean seas | - |
dc.subject.keywordAuthor | wind speed | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Geology | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |