scholarly journals Optimization of Airborne Antenna Geometry for Ocean Surface Scatterometric Measurements

2018 ◽  
Vol 10 (10) ◽  
pp. 1501 ◽  
Author(s):  
Alexey Nekrasov ◽  
Alena Khachaturian ◽  
Evgeny Abramov ◽  
Dmitry Popov ◽  
Oleg Markelov ◽  
...  
Keyword(s):  
Antenna Arrays ◽  
Ocean Surface ◽  
Estimation Accuracy ◽  
Computational Time ◽  
Retrieval Algorithm ◽  
Wind Vector ◽  
Online Data ◽  
Geophysical Model ◽  
Geophysical Model Function ◽  
Total Measurement Time

We consider different antenna configurations, ranging from simple X-configuration to multi-beam star geometries, for airborne scatterometric measurements of the wind vector near the ocean surface. For all geometries, track-stabilized antenna configurations, as well as horizontal transmitter and receiver polarizations, are considered. The wind vector retrieval algorithm is generalized here for an arbitrary star geometry antenna configuration and tested using the Ku-Band geophysical model function. Using Monte Carlo simulations for the fixed total measurement time, we show explicitly that the relative wind speed estimation accuracy barely depends on the chosen antenna geometry, while the maximum wind direction retrieval error reduces moderately with increasing angular resolution, although at the cost of increased retrieval algorithm computational complexity, thus, limiting online analysis options with onboard equipment. Remarkably, the simplest X-configuration, while the simplest in terms of hardware implementation and computational time, appears an outlier, yielding considerably higher maximum retrieval errors when compared to all other configurations. We believe that our results are useful for the optimization of both hardware and software design for modern airborne scatterometric measurement systems based on tunable antenna arrays especially, those requiring online data processing.

scholarly journals Cross-polarization geophysical model function for C-band radar backscattering from the ocean surface and wind speed retrieval

2015 ◽  
Vol 120 (2) ◽  
pp. 893-909 ◽  
Author(s):  
Paul A. Hwang ◽  
Ad Stoffelen ◽  
Gerd-Jan van Zadelhoff ◽  
William Perrie ◽  
Biao Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Ocean Surface ◽  
Cross Polarization ◽  
Model Function ◽  
Geophysical Model ◽  
Geophysical Model Function ◽  
Radar Backscattering

A Geophysical Model Function for S‐Band Reflectometry of Ocean Surface Winds in Tropical Cyclones

2019 ◽  
Vol 46 (16) ◽  
pp. 9843-9850
Author(s):  
Han Zhang ◽  
James L. Garrison ◽  
Rozaine Wijekularatne ◽  
James Warnecke
Keyword(s):  
Tropical Cyclones ◽  
Ocean Surface ◽  
Surface Winds ◽  
Model Function ◽  
Geophysical Model ◽  
Ocean Surface Winds ◽  
Geophysical Model Function

scholarly journals Aquarius geophysical model function and combined active passive algorithm for ocean surface salinity and wind retrieval

2014 ◽  
Vol 119 (8) ◽  
pp. 5360-5379 ◽  
Author(s):  
Simon Yueh ◽  
Wenqing Tang ◽  
Alexander Fore ◽  
Akiko Hayashi ◽  
Yuhe T. Song ◽  
...  
Keyword(s):  
Ocean Surface ◽  
Model Function ◽  
Surface Salinity ◽  
Wind Retrieval ◽  
Geophysical Model ◽  
Geophysical Model Function

scholarly journals Determination of the geophysical model function of the ERS-1 scatterometer by the use of neural networks

1998 ◽  
Vol 103 (C6) ◽  
pp. 12853-12868 ◽  
Author(s):  
Carlos Mejia ◽  
Sylvie Thiria ◽  
Ngan Tran ◽  
Michel Crépon ◽  
Fouad Badran
Keyword(s):  
Neural Networks ◽  
Model Function ◽  
Geophysical Model ◽  
Geophysical Model Function

scholarly journals Validation of NOAA CyGNSS Wind Speed Product with the CCMP Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1832
Author(s):  
Xiaohui Li ◽  
Dongkai Yang ◽  
Jingsong Yang ◽  
Guoqi Han ◽  
Gang Zheng ◽  
...  
Keyword(s):  
Wind Speed ◽  
Comprehensive Evaluation ◽  
Surface Wind ◽  
Temporal Analysis ◽  
Satellite System ◽  
Wind Retrieval ◽  
Sea Surface Wind ◽  
Geophysical Model ◽  
Geophysical Model Function ◽  
Global Navigation Satellite

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.

Sign in / Sign up

Export Citation Format

Share Document