Wind speed estimation using spatial resolution enhanced AMSR-E microwave radiometer data

2015 ◽  
Author(s):  
F. Lenti ◽  
F. Nunziata ◽  
M. Migliaccio ◽  
L. T. Pedersen
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Speed Estimation

Use of Sentinel-1 C-Band SAR Images for Convective System Surface Wind Pattern Detection

2020 ◽  
Vol 59 (8) ◽  
pp. 1321-1332
Author(s):  
Tran Vu La ◽  
Christophe Messager ◽  
Marc Honnorat ◽  
Rémi Sahl ◽  
Ali Khenchaf ◽  
...  
Keyword(s):  
Wind Speed ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Surface Wind ◽  
Sea Surface ◽  
Speed Estimation ◽  
Convective Clouds ◽  
Wind Intensity ◽  
Wind Patterns

AbstractConvective systems (CS) through their downdrafts hitting the sea surface may produce wind patterns (or cold pools) with wind intensity exceeding 10–25 m s−1. The latter for a long time have been significant for weather forecast and meteorological studies, especially in the tropical regions like the Gulf of Guinea since it is hard to detect the CS-associated wind patterns. Based on Sentinel-1 images [C-band Synthetic Aperture Radar (SAR)] with high spatial resolution and large swath, the current study proposed the detection of surface wind patterns through wind speed estimation by C-band model 5.N (CMOD5.N; for vertically polarized images) and two models proposed by Sapp and Komarov (for horizontally polarized images). Relative to the X-band SAR, the effects of precipitation on C-band radar backscattering are negligible, and thereby it has little impact on wind speed estimation from Sentinel-1 images. The detected surface wind patterns include a squall line and a bow echo at the mesoscale (>100 km) and many submesoscale (<100 km) convection cells. They are accompanied by various degrees of precipitation (from light to heavy rain). This study also used Meteosat infrared images for monitoring and detection of deep convective clouds (with low brightness temperature) corresponding to surface wind patterns. The agreement in location and sometimes in shape between them strengthened the assumption that the CS downdrafts may induce the sea surface patterns with high wind intensity (10–25 m s−1). In particular, because of the Sentinel-1 high spatial resolution, the pattern spots with high winds (20–25 m s−1) are detected on the illustrated images, which was not reported in the literature. They are located close to the coldest convective clouds (about 200-K brightness temperature).

Ocean surface wind speed retrieval from C-band quad-polarized SAR measurements at optimal spatial resolution

2020 ◽  
Vol 12 (2) ◽  
pp. 155-164
Author(s):  
He Fang ◽  
William Perrie ◽  
Gaofeng Fan ◽  
Tao Xie ◽  
Jingsong Yang
Keyword(s):  
Wind Speed ◽  
Spatial Resolution ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Ocean Surface Wind

Neuroadaptive Variable Speed Control of Wind Turbine With Wind Speed Estimation

2016 ◽  
Vol 63 (12) ◽  
pp. 7754-7764 ◽  
Author(s):  
Dan-Yong Li ◽  
Wen-Chuan Cai ◽  
Peng Li ◽  
Zi-Jun Jia ◽  
Hou-Jin Chen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Speed Control ◽  
Speed Estimation ◽  
Variable Speed ◽  
Variable Speed Control

Macromodel development for Wind Speed Estimation Using RBF-SVM

2021 ◽  
Author(s):  
B Shivalal Patro ◽  
Pruthiraj Swain ◽  
B Vandana
Keyword(s):  
Wind Speed ◽  
Speed Estimation

scholarly journals A Novel Wind Speed Estimation Based on the Integration of an Artificial Neural Network and a Particle Filter Using BeiDou GEO Reflectometry

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3350 ◽  
Author(s):  
Kittipong Kasantikul ◽  
Dongkai Yang ◽  
Qiang Wang ◽  
Aung Lwin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Particle Filter ◽  
Maritime Transportation ◽  
Global Navigation Satellite Systems ◽  
Speed Estimation ◽  
Estimation Model ◽  
Main Concept ◽  
Artificial Neural

Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with I and Q components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.

scholarly journals Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer

2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Georg Heygster ◽  
Victor Pellet ◽  
...  
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Microwave Radiometer ◽  
Optimal Estimation ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Special Focus ◽  
Polar Regions ◽  
Sea Ice Concentration ◽  
Ice Concentration

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.

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