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 ◽  
...  

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).

2020 ◽  
Author(s):  
Tran Vu La ◽  
Christophe Messager ◽  
Rémi Sahl ◽  
Marc Honnorat

&lt;p&gt;Convective Systems (CS) are dangerous weather events since they are associated with intense precipitation (up to 50 mm/hr) and strong surface winds (exceeding 20 m/s), for instance over the sea surface. Furthermore, they happen suddenly and evolve quickly, and thereby their effects on the sea surface are difficult to track and predict. Thanks to the geostationary meteorological satellites of METEOSAT (Europe), GOES (USA), and Himawari (Japan), the CS detection and tracking can be performed in most of the world with a 5-15-minute observation time sampling and about 2.8-km spatial resolution (up to about 1-km for the new&amp;#8211;generation satellites). Indeed, the instruments onboard these satellites perform the CS detection based on the identification of deep convective clouds. The deeper the convective clouds, the lower the brightness temperature is. The highest (coldest) clouds have the lowest brightness temperature (200 K&amp;#8211;205 K).&lt;/p&gt;&lt;p&gt;While the CS detection has been significantly improved for recent years thanks to the infrared images, the investigation of strong winds (or wind gusts) produced by the CS downdrafts hitting the sea surface did not progress a lot. It is mainly due to the lack of in-situ data and (especially) high-resolution remote sensing images. Some studies proposed the use of ASCAT scatterometers for the detection of surface wind patterns associated with the CS. However, the ASCAT only identified the mesoscale patterns (100&amp;#8211;300 km) and failed to detect the convective-scale gust fronts (5&amp;#8211;20 km), due to their large spatial resolution (12.5&amp;#8211;25 km wind grid). To be able to observe both small- and large-scale surface wind patterns, Synthetic Aperture Radar (SAR) images are used in this study thanks to their high spatial resolution, wide swath, and availability in most weather conditions. Indeed, the obtained results in (La et al., 2018, 2020) illustrate that Sentinel-1 (C-band SAR) may detect surface wind patterns in shapes of a mesoscale squall line and sub-mesoscale convection cells. The associated wind intensity with the patterns exceeds 10&amp;#8211;25 m/s.&lt;/p&gt;&lt;p&gt;To strengthen the assumption that the detected wind patterns on SAR images are produced by the CS downdrafts hitting the sea surface, we use the corresponding METEOSAT images for the detection of deep convective clouds (200 K&amp;#8211;205 K brightness temperature). The comparisons between Sentinel-1 and METEOSAT images illustrate that surface wind patterns and deep convective clouds have a matching in spatial location (and sometimes in shape). In particular, the coldest spots of deep convective clouds correspond to the one with high wind intensity (15&amp;#8211;25 m/s) of the patterns. This result thus permits to highlight a strong relationship between the detected wind patterns on the sea surface and the CS aloft.&lt;/p&gt;


2020 ◽  
Vol 12 (22) ◽  
pp. 3760
Author(s):  
Jinwei Bu ◽  
Kegen Yu ◽  
Yongchao Zhu ◽  
Nijia Qian ◽  
Jun Chang

This paper focuses on sea surface wind speed estimation based on cyclone global navigation satellite system reflectometry (GNSS-R) data. In order to extract useful information from delay-Doppler map (DDM) data, three delay waveforms are presented for wind speed estimation. The delay waveform without Doppler shift is defined as central delay waveform (CDW), and the integral of the delay waveforms with different Doppler shift values is defined as integral delay waveform (IDW), while the difference between normalized IDW (NIDW) and normalized CDW (NCDW) is defined as differential delay waveform (DDW). We first propose a data filtering method based on threshold setting for data quality control. This method can select good-quality DDM data by adjusting the root mean square (RMS) threshold of cleaned DDW. Then, the normalized bistatic radar scattering cross section (NBRCS) and the leading edge slope (LES) of IDW are calculated using clean DDM data. Wind speed estimation models based on NBRCS and LES observations are then developed, respectively, and on this basis, a combination wind speed estimation model based on determination coefficient is further proposed. The CYGNSS data and ECMWF reanalysis data collected from 12 May 2020 to 12 August 2020 are used, excluding data collected on land, to evaluate the proposed models. The evaluation results show that the wind speed estimation accuracy of the piecewise function model based on NBRCS is 2.3 m/s in terms of root mean square error (RMSE), while that of the double-parameter and triple-parameter models is 2.6 and 2.7 m/s, respectively. The wind speed estimation accuracy of the double-parameter and triple-parameter models based on LES is 3.3 and 2.5 m/s. The results also demonstrate that the RMSE of the combination method is 2.1 m/s, and the coefficient of determination is 0.906, achieving a considerable performance gain compared with the individual NBRCS- and LES-based methods.


2021 ◽  
Author(s):  
Yuqi Wang ◽  
Renguang Wu

AbstractSurface latent heat flux (LHF) is an important component in the heat exchange between the ocean and atmosphere over the tropical western North Pacific (WNP). The present study investigates the factors of seasonal mean LHF variations in boreal summer over the tropical WNP. Seasonal mean LHF is separated into two parts that are associated with low-frequency (> 90-day) and high-frequency (≤ 90-day) atmospheric variability, respectively. It is shown that low-frequency LHF variations are attributed to low-frequency surface wind and sea-air humidity difference, whereas high-frequency LHF variations are associated with both low-frequency surface wind speed and high-frequency wind intensity. A series of conceptual cases are constructed using different combinations of low- and high-frequency winds to inspect the respective effects of low-frequency wind and high-frequency wind amplitude to seasonal mean LHF variations. It is illustrated that high-frequency wind fluctuations contribute to seasonal high-frequency LHF only when their intensity exceeds the low-frequency wind speed under which there is seasonal accumulation of high-frequency LHF. When high-frequency wind intensity is smaller than the low-frequency wind speed, seasonal mean high-frequency LHF is negligible. Total seasonal mean LHF anomalies depend on relative contributions of low- and high-frequency atmospheric variations and have weak interannual variance over the tropical WNP due to cancellation of low- and high-frequency LHF anomalies.


2020 ◽  
Vol 12 (2) ◽  
pp. 155-164
Author(s):  
He Fang ◽  
William Perrie ◽  
Gaofeng Fan ◽  
Tao Xie ◽  
Jingsong Yang

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