Global Drag-Coefficient Estimates From Scatterometer Wind and Wave Steepness
A neural-network model was developed to retrieve the wave steepness (delta), which was used to represent the sea state (particularly wave state), from the European Remote Sensing (ERS) scatterometer onboard ERS-1/2. Using the retrieved delta and scatterometer wind speed, we calculated and examined t...
| Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://ir.qdio.ac.cn/handle/337002/11578 https://doi.org/10.1109/TGRS.2010.2082554 |
| Summary: | A neural-network model was developed to retrieve the wave steepness (delta), which was used to represent the sea state (particularly wave state), from the European Remote Sensing (ERS) scatterometer onboard ERS-1/2. Using the retrieved delta and scatterometer wind speed, we calculated and examined the drag coefficient (C-D) over the global ocean. The results show that C-D changes significantly when wave steepness is included in the calculation. Combining wave steepness and wind speed increases C-D by nearly 14% on average. That change is spatially variable, ranging from -18.76% for the tropical Eastern Pacific Ocean to 104% for the Southern Ocean. A neural-network model was developed to retrieve the wave steepness (delta), which was used to represent the sea state (particularly wave state), from the European Remote Sensing (ERS) scatterometer onboard ERS-1/2. Using the retrieved delta and scatterometer wind speed, we calculated and examined the drag coefficient (C(D)) over the global ocean. The results show that C(D) changes significantly when wave steepness is included in the calculation. Combining wave steepness and wind speed increases C(D) by nearly 14% on average. That change is spatially variable, ranging from -18.76% for the tropical Eastern Pacific Ocean to 104% for the Southern Ocean. |
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