Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network
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 t...
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| Format: | Article |
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/20/4164 |
| _version_ | 1827678585034375168 |
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| author | Sung-Hwan Park Jeseon Yoo Donghwi Son Jinah Kim Hyung-Sup Jung |
| author_facet | Sung-Hwan Park Jeseon Yoo Donghwi Son Jinah Kim Hyung-Sup Jung |
| author_sort | Sung-Hwan Park |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:13:10Z |
| format | Article |
| id | doaj.art-46ee6428727248fb89805d54135e2ba2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:13:10Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-46ee6428727248fb89805d54135e2ba22023-11-22T19:55:09ZengMDPI AGRemote Sensing2072-42922021-10-011320416410.3390/rs13204164Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural NetworkSung-Hwan Park0Jeseon Yoo1Donghwi Son2Jinah Kim3Hyung-Sup Jung4Marine Disaster Research Center, Korea Institute of Ocean Science & Technology (KIOST), 385, Haeyang-ro, Yeongdo-gu, Busan 49111, KoreaMarine Disaster Research Center, Korea Institute of Ocean Science & Technology (KIOST), 385, Haeyang-ro, Yeongdo-gu, Busan 49111, KoreaMarine Disaster Research Center, Korea Institute of Ocean Science & Technology (KIOST), 385, Haeyang-ro, Yeongdo-gu, Busan 49111, KoreaMarine Disaster Research Center, Korea Institute of Ocean Science & Technology (KIOST), 385, Haeyang-ro, Yeongdo-gu, Busan 49111, KoreaDepartment of Geoinformatics, University of Seoul, 163, Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, KoreaSatellite-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.https://www.mdpi.com/2072-4292/13/20/4164MetOp-BASCATdeep neural networkKorean seaswind speed |
| spellingShingle | Sung-Hwan Park Jeseon Yoo Donghwi Son Jinah Kim Hyung-Sup Jung Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network Remote Sensing MetOp-B ASCAT deep neural network Korean seas wind speed |
| title | Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network |
| title_full | Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network |
| title_fullStr | Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network |
| title_full_unstemmed | Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network |
| title_short | Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network |
| title_sort | improved calibration of wind estimates from advanced scatterometer metop b in korean seas using deep neural network |
| topic | MetOp-B ASCAT deep neural network Korean seas wind speed |
| url | https://www.mdpi.com/2072-4292/13/20/4164 |
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