scholarly journals Remote sensing of sea surface wind of Hurricane Michael by GPS reflected signals

2006 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Zhou Zhaoming ◽  
Fu Yang ◽  
Xue Zhengang ◽  
Cui Hongguang
Keyword(s):  
Remote Sensing ◽  
Surface Wind ◽  
Sea Surface ◽  
Sea Surface Wind

scholarly journals SST Anomalies in the Mozambique Channel Using Remote Sensing and Numerical Modeling Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1112
Author(s):  
Guoqing Han ◽  
Changming Dong ◽  
Junde Li ◽  
Jingsong Yang ◽  
Qingyue Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mixed Layer Depth ◽  
Surface Wind ◽  
Radiant Heat ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Surface Mixed Layer ◽  
Mozambique Channel ◽  
Sea Surface Wind ◽  
Sst Anomalies

Based on both satellite remote sensing sea surface temperature (SST) data and numerical model results, SST warming differences in the Mozambique Channel (MC) west of the Madagascar Island (MI) were found with respect to the SST east of the MI along the same latitude. The mean SST west of the MI is up to about 3.0 °C warmer than that east of the MI. The SST differences exist all year round and the maximum value appears in October. The area of the highest SST is located in the northern part of the MC. Potential factors causing the SST anomalies could be sea surface wind, heat flux and oceanic flow advection. The presence of the MI results in weakening wind in the MC and in turn causes weakening of the mixing in the upper oceans, thus the surface mixed layer depth becomes shallower. There is more precipitation on the east of the MI than that inside the MC because of the orographic effects. Different precipitation patterns and types of clouds result in different solar radiant heat fluxes across both sides of the MI. Warm water advected from the equatorial area also contribute to the SST warm anomalies.

Ulva prolifera Monitoring Study in the Yellow Sea from Multi-Temporal Remote Sensing Images

2014 ◽  
Vol 675-677 ◽  
pp. 1201-1206
Author(s):  
Yan Zhou ◽  
Bin Zhou ◽  
Ying Ying Gai
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Wind Field ◽  
Surface Wind ◽  
Ulva Prolifera ◽  
Sea Surface ◽  
Remote Sensing Images ◽  
Sea Surface Wind ◽  
Multi Temporal ◽  
Surface Wind Field

Multi-temporal Moderate resolution Imaging Spectroradiometer (MODIS) remote sensing images were used to monitor the massive blooms of floating green tide algae Ulva prolifera in the midwest of Yellow Sea (YS). In addition, from the marine environment elements of sea surface temperature (SST) and sea surface wind field, the growth and drift characteristic of U. Prolifera were studied based on MODIS thermal infrared channel SST data and Windsat wind field data. In May 2014, U. Prolifera accumulation areas were first found in central YS north of Subei Bank. With summer arrival, seawater temperature of ocean surface gradually increased in YS, It provided a more suitable environment for the growth of U. Prolifera. Due to the prevailing northerly winds in central and western YS, U. Prolifera advected to the coastal waters of Shandong Peninsula, and spread to the northeastward. In late June, it had a massive bloom and reached the maximum coverage in the northern YS. The U. Prolifera drift characteristic was confirmed by the analysis on SST and sea surface wind field, and the result shows that under the environment of suitable sea surface temperature, the driving force of the prevailing sea surface wind field might be the main reason of YS U. Prolifera bloom occurrence.

scholarly journals Development of Airborne Remote Sensing System for Monitoring Marine Meteorology (Sea Surface Wind and Temperature)

The Sea ◽  
2013 ◽  
Vol 18 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Duk-Jin Kim ◽  
Yang-Ki Cho ◽  
Ki-Mook Kang ◽  
Jin-Woo Kim ◽  
Seung-Hee Kim
Keyword(s):  
Remote Sensing ◽  
Surface Wind ◽  
Sea Surface ◽  
Airborne Remote Sensing ◽  
Sensing System ◽  
Sea Surface Wind ◽  
Marine Meteorology

scholarly journals On the Use of Doppler Shift for Sea Surface Wind Retrieval From SAR

2012 ◽  
Vol 50 (7) ◽  
pp. 2901-2909 ◽  
Author(s):  
Alexis A. Mouche ◽  
Fabrice Collard ◽  
Bertrand Chapron ◽  
Knut-Frode Dagestad ◽  
Gilles Guitton ◽  
...  
Keyword(s):  
Doppler Shift ◽  
Surface Wind ◽  
Sea Surface ◽  
Wind Retrieval ◽  
Sea Surface Wind ◽  
Sea Surface Wind Retrieval

scholarly journals FM-CW millimeter wave demonstrator system as a sensor of the sea surface wind vector

2004 ◽  
Vol 1 (6) ◽  
pp. 137-143 ◽  
Author(s):  
Alexey Nekrasov ◽  
Jacco J.M. de Wit ◽  
Peter Hoogeboom
Keyword(s):  
Millimeter Wave ◽  
Surface Wind ◽  
Sea Surface ◽  
Wind Vector ◽  
Sea Surface Wind

Wind Stress Over Waves: Effects of Sea Roughness and Atmospheric Stability

2002 ◽  
Vol 124 (3) ◽  
pp. 169-172 ◽  
Author(s):  
Dag Myrhaug ◽  
Olav H. Slaattelid
Keyword(s):  
Boundary Layer ◽  
Wind Stress ◽  
Local Equilibrium ◽  
Surface Wind ◽  
Atmospheric Stability ◽  
Sea Surface ◽  
Stability Function ◽  
Sea Surface Wind ◽  
Surface Wind Stress ◽  
Sea Surface Roughness

The paper considers the effects of sea roughness and atmospheric stability on the sea surface wind stress over waves, which are in local equilibrium with the wind, by using the logarithmic boundary layer profile including a stability function, as well as adopting some commonly used sea surface roughness formulations. The engineering relevance of the results is also discussed.

scholarly journals Sea surface wind perturbations over the Kashevarov Bank of the Okhotsk Sea: a satellite study

2011 ◽  
Vol 29 (2) ◽  
pp. 393-399
Author(s):  
T. I. Tarkhova ◽  
M. S. Permyakov ◽  
E. Yu. Potalova ◽  
V. I. Semykin
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Wind ◽  
Sea Surface ◽  
Cold Spot ◽  
Okhotsk Sea ◽  
Autumn Period ◽  
Sea Surface Wind ◽  
Spot Center

Abstract. Sea surface wind perturbations over sea surface temperature (SST) cold anomalies over the Kashevarov Bank (KB) of the Okhotsk Sea are analyzed using satellite (AMSR-E and QuikSCAT) data during the summer-autumn period of 2006–2009. It is shown, that frequency of cases of wind speed decreasing over a cold spot in August–September reaches up to 67%. In the cold spot center SST cold anomalies reached 10.5 °C and wind speed lowered down to ~7 m s−1 relative its value on the periphery. The wind difference between a periphery and a centre of the cold spot is proportional to SST difference with the correlations 0.5 for daily satellite passes data, 0.66 for 3-day mean data and 0.9 for monthly ones. For all types of data the coefficient of proportionality consists of ~0.3 m s−1 on 1 °C.

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