Extraction of ocean surface wind field from simulated ERS-1 Scatterometer data

1992 ◽  
Vol 13 (17) ◽  
pp. 3311-3327 ◽  
Author(s):  
B. S. GOHIL
Keyword(s):  
Wind Field ◽  
Surface Wind ◽  
Ocean Surface ◽  
Ocean Surface Wind ◽  
Surface Wind Field

Scaling Feature of the Ocean Surface Wind Field

2021 ◽  
Author(s):  
Yang Gao ◽  
Francois G Schmitt ◽  
Jianyu Hu ◽  
Yongxiang Huang
Keyword(s):  
Seasonal Variations ◽  
Wind Field ◽  
Surface Wind ◽  
Ocean Surface ◽  
Scaling Exponent ◽  
Scaling Exponents ◽  
Multiscale Dynamics ◽  
Ocean Surface Wind ◽  
Scaling Features ◽  
Surface Wind Field

<p>The ocean surface wind plays a crucial role in the air-sea exchanges of momentum, heat, and mass, consequently is vital to the controlling of weather and climate. Due to the extremely large range of scales of the motion of the wind field, e.g., flow structures from millimeters to thousands of kilometers, the multiscale dynamics are known to be relevant. In this work, with the help of a Wiener-Khinchine theorem-based Fourier power spectrum estimator, the scaling features of the wind field provided by several satellites, i.e., QuikSCAT, Metop-A, -B, and -C, Haiyang-2B, and China France Oceanography SATellite (CFOSAT), is examined. Power-law scaling behavior is evident in the ranges of 100 to 3000 km with a scaling exponent β varying from 5/3 to 3. The global distributions and seasonal variations of the scaling exponent β have also been considered. The results show that due to the energetic convective activities in the low-latitude zones, the scaling exponents β in these regions are closer to the value of 5/3. As for the mid-latitudes, the values of β are close to 2 and independent of the variation of longitude. Concerning the seasonal variations, for most regions, the scaling exponents measured in winter are larger than those in summer. Furthermore, the seasonal variations of β in low-latitudes are stronger than those in the mid-latitudes. Our preliminary results indicate that all satellites provide a consistent scaling feature of the ocean surface wind field.</p>

SAR Speckle Dependence on Ocean Surface Wind Field

2019 ◽  
Vol 57 (8) ◽  
pp. 5447-5455 ◽  
Author(s):  
Maurizio Migliaccio ◽  
Lanqing Huang ◽  
Andrea Buono
Keyword(s):  
Wind Field ◽  
Surface Wind ◽  
Ocean Surface ◽  
Ocean Surface Wind ◽  
Surface Wind Field

Sequential Processing of GNSS-R Delay-Doppler Maps to Estimate the Ocean Surface Wind Field

2019 ◽  
Vol 57 (12) ◽  
pp. 10202-10217 ◽  
Author(s):  
Feixiong Huang ◽  
James L. Garrison ◽  
Nereida Rodriguez-Alvarez ◽  
Andrew J. O'Brien ◽  
Kaitie M. Schoenfeldt ◽  
...  
Keyword(s):  
Wind Field ◽  
Surface Wind ◽  
Ocean Surface ◽  
Sequential Processing ◽  
Ocean Surface Wind ◽  
Surface Wind Field

Study on Comparison Analysis and Error Analysis of Polarization Ratio Models

2013 ◽  
Vol 655-657 ◽  
pp. 735-739
Author(s):  
Ke Wang ◽  
Yuan Zhang ◽  
Yan Bo Hui ◽  
Feng Wang ◽  
Hong Yan Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Error Analysis ◽  
Wind Field ◽  
Incidence Angle ◽  
Surface Wind ◽  
Comparison Analysis ◽  
Polarization Ratio ◽  
Sar Images ◽  
Ocean Surface Wind ◽  
Surface Wind Field

Polarization ratio models are key problem for ocean surface wind field retrieval from HH polarization SAR images. This paper studied deeply on various polarization ratio models, and carried on comparison analysis and error analysis about them. We analyzed influence of incidence angle, azimuth, and wind speed for polarization ratio. By using true measurement of polarization ratio, we statistically valuated ability of various polarization ratio models, and proposed the best choice of polarization ratio models. Our research has guide meaning for using polarization ratio models reasonably.

Sign in / Sign up

Export Citation Format

Share Document