Results 301 to 310 of about 439,158 (345)

Breaking of wind waves and the sea surface wind stress

Journal of the Oceanographical Society of Japan, 1970
In the conventional treatment of the coefficient of sea surface wind stress by plotting it against 10-m wind speed, there are inevitable discrepancies among results of various investigators. The reason is considered to lie primarily in the fact that the state of the sea surface or of waves is disregarded, which may have great influence on the sea ...
Yoshiaki Toba, Hideaki Kunishi
openaire   +1 more source

Validation of New Sea Surface Wind Products From Scatterometers Onboard the HY-2B and MetOp-C Satellites

IEEE Transactions on Geoscience and Remote Sensing, 2020
The new Ku-band scatterometer (HSCAT-B) onboard the HY-2B satellite was launched on October 25, 2018, and soon after the C-band scatterometer (Advanced Scatterometer (ASCAT)-C) onboard the MetOp-C satellite was launched on November 6, 2018.
Zhixiong Wang, A. Stoffelen, Juhong Zou, Wenming Lin, A. Verhoef, Yi Zhang, Yijun He, Mingsen Lin   +7 more
semanticscholar   +1 more source

Satellite observations of sea surface temperature and sea surface wind coupling in the Japan Sea

Journal of Geophysical Research: Oceans, 2006
We investigate ocean‐atmosphere coupling in the Japan Sea by using microwave satellite measurements of sea surface temperature (SST) and sea surface wind from 1 June 2002 to 31 December 2004. First, it is observed that instantaneous wind speeds are modified by SST front meanders in widespread areas in the Japan Sea.
Teruhisa Shimada, Hiroshi Kawamura
openaire   +1 more source

Multi-Frequency SAR Retrieval of Sea Surface Wind Field

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023
This study is to present the lesson learned during the activ- ities related to the Italian Space Agency (ASI) funded AP- PLICAVEMARS project which aims at estimating sea surface wind field from L-, C- and X-band Synthetic Aperture Radar (SAR) imagery.
Nunziata F., Migliaccio M., Verlanti A., Buono A., Ferrentino E., Alparone M., Zecchetto S., Zanchetta A., Portabella M., Grieco G.   +9 more
openaire   +2 more sources

Determination of nearshore sea surface wind vector from marine X-band radar images

Ocean Engineering, 2015
Zhongbiao Chen, Yijun He, Biao Zhang
exaly   +2 more sources

On the Analysis of SAR Derived Wind and Sea Surface Currents

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020
Use of the SAR image intensity for estimation of waves and wind over the sea surface is rather well-established. Interpretation of surface currents estimated from SAR data is, as reported in literature, a much more complex issue. In this work we summarize a methodology exploited for the analysis of sea surface currents estimated via the Doppler ...
Virginia Zamparelli, Francesca De Santi, Giacomo De Carolis, Gianfranco Fornaro   +3 more
openaire   +1 more source

GPS Reflections for Sea Surface Wind Speed Measurement

IEEE Geoscience and Remote Sensing Letters, 2008
In this letter, Global Positioning System (GPS) reflections for sea surface wind speed measurement are explored. The reflected signal correlation power is employed to retrieve the sea surface wind speed with a certain degree of accuracy. The GPS coarse-acquisition code autocorrelation sidelobe is studied and considered in the reflected signal model ...
D. K. Yang, Y. Q. Zhang, Y. Lu, Q. S. Zhang   +3 more
openaire   +1 more source

Albedos And Glitter Patterns Of A Wind-Roughened Sea Surface

SPIE Proceedings, 1986
Abstract The downward albedo (irradiance reflectance) r− and the upward albedo r+ of a random air–water surface, formed by capillary waves, are computed as a function of lighting conditions and wind speed by Monte Carlo means for incident unpolarized radiant flux.
Rudolph W. Preisendorfer, Curtis D. Mobley   +1 more
openaire   +1 more source

NOAA operational SAR sea surface wind products

2011 IEEE International Geoscience and Remote Sensing Symposium, 2011
SAR-derived wind measurements are in the process of being implemented for operational production within NOAA's National Environmental Satellite, Data, and Information Service. For C-band ENVISAT and RADARSAT-1/2 data, the CMOD5 algorithm is being used; for ALOS data, a special L-band wind algorithm is employed.
William Pichel, Frank M. Monaldo, Christopher R. Jackson, Xiaofeng Li 0001, John Sapper, Xiaofeng Yang 0002   +5 more
openaire   +1 more source

Hybrid CNN-Transformer Network With a Weighted MSE Loss for Global Sea Surface Wind Speed Retrieval From GNSS-R Data

IEEE Transactions on Geoscience and Remote Sensing
The global navigation satellite system reflectometry (GNSS-R) plays a crucial role in sea surface wind speed measurement, and convolutional neural networks (CNNs) have been a widely used method for wind speed retrieval from GNSS-R data.
Xin Qiao, Q. Yan, Weimin Huang
semanticscholar   +1 more source