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 MODELLING THE HYDROTHERMODYNAMICS OF THE SURFACE LAYER OF A STRATIFIED TROPICAL SUBTROPICAL OCEAN

2006 ◽  
Vol 5 (1) ◽  
pp. 26 ◽  
Author(s):  
C. A. A. Carbonel ◽  
A. C. Galeão
Keyword(s):  
Finite Element ◽  
Surface Layer ◽  
Equations Of Motion ◽  
Surface Wind ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Net Energy ◽  
Sea Surface Wind ◽  
Surface Wind Stress ◽  
Finite Element Galerkin Method

In the present paper a two-layer model is developed for the non-permanent hydrothermodynamics of a tropical subtropical coastal ocean. The model includes the equations of motion, continuity and heat. The equations apply only to the thin and warm oceanic surface layer. The deep layer is stipulated to be motionless, arbitrarily deep and separated from the upper layer by a density discontinuity. Cold deep water is carried across the interface from the lower into the upper layer; it is warmed up there by the net energy input from the atmosphere into the ocean The non-uniform sea surface wind stress and heat at the surface are the main forcing functions of the model. A numerical finite element method is proposed to approximate the hydrothermodynamic problem. The model uses simple linear spatial and temporal continuous polynomial and a stabilizing Petrov-Galerkin operator to improve the classical finite element Galerkin method. The hydrothermodynamic response is obtained for the eastern pacific boundary in the southern hemisphere. Monthly climatological data are used to determine the wind and heat fluxes forcings of the model. The main features of the observed Sea Surface Temperature (SST) pattern are successfully simulated by the model. In particular the predicted : upwelling along the coastal boundary; warm water intrusion in offshore side and signals of countercurrents are in a quite good agreement with observations.

Ocean Response to Tropical Cyclone Seroja at East Nusa Tenggara Waters

2021 ◽  
Vol 925 (1) ◽  
pp. 012045
Author(s):  
Avrionesti ◽  
Faruq Khadami ◽  
Dayu W Purnaningtyas
Keyword(s):  
Tropical Cyclone ◽  
Chlorophyll A ◽  
Mixed Layer Depth ◽  
Surface Wind ◽  
Sea Surface ◽  
Height Anomaly ◽  
Chl A ◽  
Sea Surface Wind ◽  
Sst Cooling ◽  
Sea Surface Height Anomaly

Abstract Tropical Cyclone (TC) Seroja is a unique tropical cyclone that has significant impacts along its path, such as floods in East Nusa Tenggara and high waves along the southern coast of Indonesia. Research related to ocean responses to tropical cyclones in Indonesia is still limited due to its rarely occurence in Indonesian waters. The responses of the upper ocean to TC Seroja were investigated using multi-satellite remote sensing of sea surface wind (SSW), sea surface temperature (SST), sea surface height anomaly (SSHA), and numerical model of mixed layer depth (MLD) and chlorophyll-a (Chl-a). The SST cooling occurred around the TC Seroja track at 0.5 – 3°C after the storm had passed. During April 3 – 7, 2021, in addition to spatial SST cooling, changes in chlorophyll-a, SSHA, and MLD were also detected. The chlorophyll-a increase to 2.57 mg/m3 and SSHA reached -10 cm. Thus, the MLD was deeper around the eye of the storm during the cyclone and became uniform after the storm passed. These characteristics indicate the upwelling phenomenon induced by the cyclone.

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 Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea

2021 ◽  
Vol 13 (2) ◽  
pp. 259
Author(s):  
Shuping Zhang ◽  
Anna Rutgersson ◽  
Petra Philipson ◽  
Marcus B. Wallin
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Mean Squared Error ◽  
Numerical Models ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Chl A ◽  
Marginal Seas ◽  
The Baltic

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.

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
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