geophysical model function
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2021 ◽  
Vol 13 (24) ◽  
pp. 5165
Author(s):  
Alexey Nekrasov ◽  
Alena Khachaturian

Extension of the existing airborne radars’ applicability is a perspective approach to the remote sensing of the environment. Here we investigate the capability of the rotating-beam radar installed over the fuselage for the sea surface wind measurement based on the comparison of the backscatter with the respective geophysical model function (GMF). We also consider the robustness of the proposed approach to the partial shading of the underlying water surface by the aircraft nose, tail, and wings. The wind retrieval algorithms have been developed and evaluated using Monte-Carlo simulations. We find our results promising both for the development of new remote sensing systems as well as the functional enhancement of existing airborne radars.



2021 ◽  
Vol 13 (23) ◽  
pp. 4783
Author(s):  
Zhixiong Wang ◽  
Juhong Zou ◽  
Youguang Zhang ◽  
Ad Stoffelen ◽  
Wenming Lin ◽  
...  

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.



2021 ◽  
Vol 13 (9) ◽  
pp. 1832
Author(s):  
Xiaohui Li ◽  
Dongkai Yang ◽  
Jingsong Yang ◽  
Guoqi Han ◽  
Gang Zheng ◽  
...  

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CyGNSS) mission was launched in December 2016, which can remotely sense sea surface wind with a relatively high spatio-temporal resolution for tracking tropical cyclones. In recent years, with the gradual development of the geophysical model function (GMF) for CyGNSS wind retrieval, different versions of CyGNSS Level 2 products have been released and their performance has gradually improved. This paper presents a comprehensive evaluation of CyGNSS wind product v1.1 produced by the National Oceanic and Atmospheric Administration (NOAA). The Cross-Calibrated Multi-Platform (CCMP) analysis wind (v02.0 and v02.1 near real time) products produced by Remote Sensing Systems (RSS) were used as the reference. Data pairs between the NOAA CyGNSS and RSS CCMP products were processed and evaluated by the bias and standard deviation SD. The CyGNSS dataset covers the period between May 2017 and December 2020. The statistical comparisons show that the bias and SD of CyGNSS relative to CCMP-nonzero collocations when the flag of CCMP winds is nonzero are –0.05 m/s and 1.19 m/s, respectively. The probability density function (PDF) of the CyGNSS winds coincides with that of CCMP-nonzero. Furthermore, the average monthly bias and SD show that CyGNSS wind is consistent and reliable generally. We found that negative deviation mainly appears at high latitudes in both hemispheres. Positive deviation appears in the China Sea, the Arabian Sea, and the west of Africa and South America. Spatial–temporal analysis demonstrates the geographical anomalies in the bias and SD of the CyGNSS winds, confirming that the wind speed bias shows a temporal dependency. The verification and comparison show that the remotely sensed wind speed measurements from NOAA CyGNSS wind product v1.1 are in good agreement with CCMP winds.



2020 ◽  
Vol 17 (8) ◽  
pp. 1333-1337 ◽  
Author(s):  
Milad Asgarimehr ◽  
Irina Zhelavskaya ◽  
Giuseppe Foti ◽  
Sebastian Reich ◽  
Jens Wickert


2020 ◽  
Author(s):  
Romain Husson ◽  
Alexis Mouche ◽  
Nicolas Longepe ◽  
Henrick Berger ◽  
Olivier Archer ◽  
...  

<p><span>More than </span><span>2</span><span>00 Sentinel-1 acquisitions over Tropical Cyclones (TC) eyes have been accumulated since 2016 thanks to the SHOC scheme (Satellite Hurricane Observation Campaign) operated in collaboration with ESA ground segment. These high-resolution observations have shown the great potential offered by S1 constellation in dual-polarization to monitor TC along their lifetime and to provide numerous observable parameters such as maximum sustained wind speed (up to 80 m/s) and TC structure (e.g. wind radii, eye geometry and position). Co-locations with the Stepped Frequency Microwave Radiometer (SFMR) confirm that, even for extreme cases, S1-derived ocean wind speeds are found in agreement and able to provide consistent measurements in the eyewall. Similarly, co-locations between SMOS and S1-wind degraded at a similar medium resolution are in good agreement. Also, Hurricane experts listed in their recommendations at the 40</span><sup><span>th</span></sup><span> WMO Hurricane Committee for USA/Caribbean region that “Special acquisitions plans during Irma, Jose and Maria having demonstrated the high value of kilometric-scale information provided by Sentinel-1 SAR data, HC40 recommends that these data are made available to help monitor critical aspects of the TC structure”.</span></p><p><span>Based on this demonstration, a new ESA-funded project called CYMS (CYclone Monitoring service with S-1) starts in February 2020, with the objective of scaling up the SHOC initiative for its potential integration as part of a Copernicus Service. One objective is the operational delivery of tailored S1-derived TC observations to tropical cyclone forecasters of all tropical cyclone Regional Specialized Meteorological Centres (RSMCs) and Tropical Cyclone Warning Centres (TCWCs). Besides, S1 TC observations will contribute to a new database for science applications. </span></p><p><span>In order to continuously keep improving the S1-derived TC observations, current limitations in the wind field retrieval are recalled and perspectives to overcome them are proposed. First, the presence of rain signatures over SAR images requires a fine pre-processing filtering of these non-wind related features in order not to interpret them as wind speed. Second, the current inversion using the co- and cross-polarized NRCS channels via a noise-dependent mixing can show some limitations for wind speed around 30m/s. Alternative schemes are proposed to mitigate this issue. Third, S1 wind directions are mostly influenced by the co-located atmospheric model which can show some significant shifts with respect to the actual situation. Pre-processing methods based on the exploitation of wind rolls signatures, ubiquitous under intense TC, are presented to improve the wind direction retrieval. Finally, improvements of the current cross-polarized Geophysical Model Function (GMF), MS1A, are proposed taking advantage of a more complete dataset of S1 TC observations since its first estimation in 2017. </span></p><p><span>Overall, the current and future developments for S1 wind field retrieval aim at integrating all valuable observations as inputs. Additional candidate parameters of interest are the Doppler Centroïd anomaly, which is related to the radial wind, and the Co-Cross Polarization Coherence (CCPC), which is related to both the wind speed and direction.</span></p>



Author(s):  
Yanmin Zhang ◽  
Yunhua Wang ◽  
Jie Zhang ◽  
Yingzhe Liu


2019 ◽  
Vol 16 (10) ◽  
pp. 1521-1525 ◽  
Author(s):  
Biao Zhang ◽  
Alexis Mouche ◽  
Yiru Lu ◽  
William Perrie ◽  
Guosheng Zhang ◽  
...  


2019 ◽  
Vol 46 (16) ◽  
pp. 9843-9850
Author(s):  
Han Zhang ◽  
James L. Garrison ◽  
Rozaine Wijekularatne ◽  
James Warnecke




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