On the use of cross-polarized SAR and GPS-sonde measurements for wind speed retrieval in tropical cyclones

2020 ◽  
Author(s):  
Evgeny Poplavsky ◽  
Nikita Rusakov ◽  
Olga Ermakova ◽  
Yuliya Troitskaya ◽  
Daniil Sergeev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Wind Speed ◽  
Atmospheric Boundary Layer ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Cross Polarization ◽  
Sar Images ◽  
Sensing Data ◽  
High Level

<p>The current investigation is concerned with the study of the dependence of the scattered cross-polarized microwave signal from the Sentinel-1 satellite on the parameters of the marine atmospheric boundary layer based on data obtained from falling NOAA GPS-sondes under tropical cyclone conditions.<br>Field measurements and remote sensing data for hurricanes in the Atlantic and Pacific oceans were analyzed for the period 2016 - 2018. Based on the analysis of data measured by GPS-sondes, averaged wind speed profiles were obtained, while the parameters of the atmospheric boundary layer (drag coefficient and wind friction velocity) were retrieved using the self-similarity property of velocity profiles from measurements in the “wake” part.<br>Sentinel-1 SAR images were used as remote sensing data. Images with cross polarization have a high level of thermal noise (NESZ), which leads to errors when retrieving the NRCS. In this regard, preliminary image processing was performed in the SNAP application.<br>Using the obtained parameters of the atmospheric boundary layer, the data of GRS-sonde measurements and Sentinel-1 SAR images on cross polarization were collocated and the dependences of the NRCS on the parameters of the atmospheric boundary layer were obtained.</p><div>This work was supported by the RFBR projects No. 19-05-00249, 19-05-00366, 18-35-20068 (remote sensing data analysis) and RSF No. 19-17-00209 (GPS-sondes data assimilation and processing).</div>

Atmospheric boundary layer parameters retrieval from Stepped Frequency Microwave Radiometer measurements in tropical cyclones

2020 ◽  
Author(s):  
Nikita Rusakov ◽  
Evgeny Poplavsky ◽  
Olga Ermakova ◽  
Yuliya Troitskaya ◽  
Daniil Sergeev ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Microwave Radiometer ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Radar Signal ◽  
Time And Space ◽  
Sensing Data ◽  
Radar Images

<p>Active microwave sensing using satellite instruments has great advantages, since in this range the absorption by clouds and atmospheric gases is noticeably reduced, it allows for round-the-clock and all-weather monitoring of the ocean. One of the main problems is concerned with obtaining the dependency between the RCS of radar signal scattered by the wavy water surface and the parameters of the atmospheric boundary layer in hurricane conditions. To obtain this dependence, we used field measurements of wind speed in a hurricane from falling NOAA GPS-sondes and SAR images from the Sentinel-1 satellite. However, there is the problem of correct collocation of remote sensing data with field measurements of the atmospheric boundary layer parameters, since they are separated in time and space. In this regard, the amount of data suitable for analysis is very limited, which forces us to look for new data sources for processing. A six-channel SFMR radiometer is also installed on board of NOAA research aircraft that measures the emissivity of the ocean surface beneath the aircraft. Thus, it becomes possible to relate the radiometric measurements of SFMR with the parameters of the atmospheric boundary layer in a tropical cyclone obtained from wind velocity profiles, since they are carried out as close as possible in time and space. Using this relation, the SFMR data and the hurricane radar images were analyzed together and an alternative method was found for constructing the dependence of the RCS on the parameters of the boundary layer.</p><p>This work was supported by the RFBR projects No. 19-05-00249, 19-05-00366, 18-35-20068 (remote sensing data analysis) and RSF No. 19-17-00209 (GPS-sondes data assimilation and processing).</p><p> </p>

Internal gravity-shear waves in the atmospheric boundary layer from acoustic remote sensing data

2015 ◽  
Vol 51 (2) ◽  
pp. 193-202 ◽  
Author(s):  
V. S. Lyulyukin ◽  
M. A. Kallistratova ◽  
R. D. Kouznetsov ◽  
D. D. Kuznetsov ◽  
I. P. Chunchuzov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Shear Waves ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Acoustic Remote Sensing

scholarly journals Calibration of the depth invariant algorithm to monitor the tidal action of Rabigh City at the Red Sea Coast, Saudi Arabia

2020 ◽  
Vol 12 (1) ◽  
pp. 1666-1678
Author(s):  
Mohammed H. Aljahdali ◽  
Mohamed Elhag
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Wind Speed ◽  
Red Sea ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Optical Data ◽  
Mean Values ◽  
Sensing Data ◽  
The Mean

AbstractRabigh is a thriving coastal city located at the eastern bank of the Red Sea, Saudi Arabia. The city has suffered from shoreline destruction because of the invasive tidal action powered principally by the wind speed and direction over shallow waters. This study was carried out to calibrate the water column depth in the vicinity of Rabigh. Optical and microwave remote sensing data from the European Space Agency were collected over 2 years (2017–2018) along with the analog daily monitoring of tidal data collected from the marine station of Rabigh. Depth invariant index (DII) was implemented utilizing the optical data, while the Wind Field Estimation algorithm was implemented utilizing the microwave data. The findings of the current research emphasis on the oscillation behavior of the depth invariant mean values and the mean astronomical tides resulted in R2 of 0.75 and 0.79, respectively. Robust linear regression was established between the astronomical tide and the mean values of the normalized DII (R2 = 0.81). The findings also indicated that January had the strongest wind speed solidly correlated with the depth invariant values (R2 = 0.92). Therefore, decision-makers can depend on remote sensing data as an efficient tool to monitor natural phenomena and also to regulate human activities in fragile ecosystems.

Exchange coefficients derived from GPS-sonde and SFMR measurements in hurricane conditions

2020 ◽  
Author(s):  
Olga Ermakova ◽  
Nikita Rusakov ◽  
Evgeny Poplavsky ◽  
Yuliya Troitskaya ◽  
Daniil Sergeev ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Atmospheric Boundary Layer ◽  
Moisture Transfer ◽  
Weather Forecasting ◽  
Aerodynamic Drag ◽  
Remote Sensing Data ◽  
Thermodynamic Quantities ◽  
Self Similarity ◽  
Heat And Moisture

<p>Insufficient knowledge of the atmosphere layer momentum, heat and moisture transfer between the wavy water surface and marine atmospheric boundary layer under hurricane conditions lead to the uncertainties while using weather forecasting models and models of climate. In particular, there is a significant lack of data for heat and moisture exchange coefficients. In this regard, it is necessary to analyze and process the vertical profiles of wind speed and thermodynamic quantities. The present study is concerned with the analysis and processing of measurements from the NOAA falling GPS-sondes for hurricanes of categories 4 and 5 of 2003-2017, which represent an array of data on wind speed, temperature, altitude, coordinates, etc.</p><p>The proposed approach for describing a turbulent boundary layer formed in hurricane conditions is based on the use of the self-similarity properties of the velocity and enthalpy profiles in the atmospheric boundary layer, which includes a layer of constant flows, transferring into its “wake” part with height. Based on the proposed approach, the aerodynamic drag coefficients Cd and the enthalpy exchange coefficient Ck for a selected group of hurricanes were restored. As the value of Ck/Cd represents a determining factor in the formation of a hurricane, the dependence of this ratio on the wind speed was constructed.</p><p>This work was supported by the RFBR projects No 19-05-00249, 19-05-00366, 18-35-20068 (remote sensing data analysis) and RSF No 19-17-00209 (GPS-sonde data assimilation and processing).</p>

scholarly journals BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

2016 ◽  
Vol III-7 ◽  
pp. 83-88 ◽  
Author(s):  
M. Papadomanolaki ◽  
M. Vakalopoulou ◽  
S. Zagoruyko ◽  
K. Karantzalos
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Multispectral Data ◽  
Sensing Data ◽  
Multispectral Remote Sensing ◽  
High Level ◽  
Learning Frameworks

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the <i>AlexNet</i>, <i>AlexNet-small</i> and <i>VGG</i> models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates <i>i.e.</i>, above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.

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