Global Drag-Coefficient Estimates From Scatterometer Wind and Wave Steepness

Author

Liu, Guoqiang ; He, Yijun ; Shen, Hui ; Guo, Jie

Author_Institution

Inst. of Oceanol., Chinese Acad. of Sci., Qingdao, China

Volume

49

Issue

5

fYear

2011

fDate

5/1/2011 12:00:00 AM

Firstpage

1499

Lastpage

1503

Abstract

A neural-network model was developed to retrieve the wave steepness (δ), 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 δ and scatterometer wind speed, we calculated and examined the drag coefficient ( CD) over the global ocean. The results show that CD changes significantly when wave steepness is included in the calculation. Combining wave steepness and wind speed increases CD 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.

Keywords

atmospheric boundary layer; atmospheric techniques; drag; geophysical fluid dynamics; neural nets; ocean waves; oceanographic techniques; remote sensing; wind; ERS scatterometer; ERS-1; ERS-2; European Remote Sensing; Southern Ocean; global drag coefficient estimation; global ocean drag coefficient; neural network model; scatterometer wave steepness; scatterometer wind speed; scatterometer wind steepness; sea state representation; sea wave state; tropical eastern Pacific Ocean; wave steepness retrieval; Artificial neural networks; Radar measurements; Remote sensing; Sea surface; Spaceborne radar; Wind speed; Air–sea interaction; neural networks (NNs); remote sensing;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/TGRS.2010.2082554

Filename

5628261