A new hurricane wind retrieval algorithm for SAR images

2006 ◽  
Vol 33 (21) ◽  
Author(s):  
Hui Shen ◽  
Will Perrie ◽  
Yijun He
Keyword(s):  
Retrieval Algorithm ◽  
Sar Images ◽  
Wind Retrieval ◽  
Hurricane Wind

A ‘Track-Wise’ Wind Retrieval Algorithm for the CYGNSS Mission

2019 ◽  
Author(s):  
Faozi Said ◽  
Zorana Jelenak ◽  
Jeonghwang Park ◽  
Seubson Soisuvarn ◽  
Paul S. Chang
Keyword(s):  
Retrieval Algorithm ◽  
Wind Retrieval

scholarly journals Improvement of wind retrieval algorithm for Rayleigh Doppler lidar

2012 ◽  
Vol 61 (3) ◽  
pp. 030702
Author(s):  
Shen Fa-Hua ◽  
Shu Zhi-Feng ◽  
Sun Dong-Song ◽  
Wang Zhong-Chun ◽  
Xue Xiang-Hui ◽  
...  
Keyword(s):  
Retrieval Algorithm ◽  
Doppler Lidar ◽  
Wind Retrieval

scholarly journals Profiling wind and greenhouse gases by infrared-laser occultation: results from end-to-end simulations in windy air

2015 ◽  
Vol 8 (7) ◽  
pp. 2813-2825 ◽  
Author(s):  
A. Plach ◽  
V. Proschek ◽  
G. Kirchengast
Keyword(s):  
Wind Speed ◽  
Greenhouse Gases ◽  
Doppler Shift ◽  
Lower Stratosphere ◽  
Infrared Laser ◽  
Retrieval Algorithm ◽  
Upper Troposphere ◽  
Wind Retrieval ◽  
End To End ◽  
Thermodynamic Variables

Abstract. The new mission concept of microwave and infrared-laser occultation between low-Earth-orbit satellites (LMIO) is designed to provide accurate and long-term stable profiles of atmospheric thermodynamic variables, greenhouse gases (GHGs), and line-of-sight (l.o.s.) wind speed with focus on the upper troposphere and lower stratosphere (UTLS). While the unique quality of GHG retrievals enabled by LMIO over the UTLS has been recently demonstrated based on end-to-end simulations, the promise of l.o.s. wind retrieval, and of joint GHG and wind retrieval, has not yet been analyzed in any realistic simulation setting. Here we use a newly developed l.o.s. wind retrieval algorithm, which we embedded in an end-to-end simulation framework that also includes the retrieval of thermodynamic variables and GHGs, and analyze the performance of both stand-alone wind retrieval and joint wind and GHG retrieval. The wind algorithm utilizes LMIO laser signals placed on the inflection points at the wings of the highly symmetric C18OO absorption line near 4767 cm−1 and exploits transmission differences from a wind-induced Doppler shift. Based on realistic example cases for a diversity of atmospheric conditions, ranging from tropical to high-latitude winter, we find that the retrieved l.o.s. wind profiles are of high quality over the lower stratosphere under all conditions, i.e., unbiased and accurate to within about 2 m s−1 over about 15 to 35 km. The wind accuracy degrades into the upper troposphere due to the decreasing signal-to-noise ratio of the wind-induced differential transmission signals. The GHG retrieval in windy air is not vulnerable to wind speed uncertainties up to about 10 m s−1 but is found to benefit in the case of higher speeds from the integrated wind retrieval that enables correction of wind-induced Doppler shift of GHG signals. Overall both the l.o.s. wind and GHG retrieval results are strongly encouraging towards further development and implementation of a LMIO mission.

Wave and wind retrieval from sar images of the ocean

2001 ◽  
Vol 56 (11-12) ◽  
pp. 682-699
Author(s):  
B. Chapron ◽  
H. Johnsen ◽  
R. Garello
Keyword(s):  
Sar Images ◽  
Wind Retrieval

scholarly journals Comparison of C-Band Quad-Polarization Synthetic Aperture Radar Wind Retrieval Models

2018 ◽  
Vol 10 (9) ◽  
pp. 1448 ◽  
Author(s):  
He Fang ◽  
Tao Xie ◽  
William Perrie ◽  
Guosheng Zhang ◽  
Jingsong Yang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Synthetic Aperture Radar ◽  
Surface Wind ◽  
Synthetic Aperture ◽  
Northwest Pacific ◽  
Sar Images ◽  
Retrieval Models ◽  
Wind Retrieval ◽  
Wind Speeds ◽  
Aperture Radar

This work discusses the accuracy of C-2PO (C-band cross-polarized ocean backscatter) and CMOD4 (C-band model) geophysical model functions (GMF) for sea surface wind speed retrieval from satellite-born Synthetic Aperture Radar (SAR) images over in the Northwest Pacific off the coast of China. In situ observations are used for comparison of the retrieved wind speed using two established wind retrieval models: C-2PO model and CMOD4 GMF. Using 439 samples from 92 RADARSAT-2 fine quad-polarization SAR images and corresponding reference winds, we created two subset wind speed databases: the training and testing subsets. From the training data subset, we retrieve ocean surface wind speeds (OSWSs) from different models at each polarization and compare with reference wind speeds. The RMSEs of SAR-retrieved wind speeds are: 2.5 m/s: 2.11 m/s (VH-polarized), 2.13 m/s (HV-polarized), 1.86 m/s (VV-polarized) and 2.26 m/s (HH-polarized) and the correlation coefficients are 0.86 (VH-polarized), 0.85(HV-polarized), 0.87(VV-polarized) and 0.83 (HH-polarized), which are statistically significant at the 99.9% significance level. Moreover, we found that OSWSs retrieved using C-2PO model at VH-polarized are most suitable for moderate-to-high winds while CMOD4 GMF at VV-polarized tend to be best for low-to-moderate winds. A hybrid wind retrieval model is put forward composed of the two models, C-2PO and CMOD4 and sets of SAR test data are used in order to establish an appropriate wind speed threshold, to differentiate the wind speed range appropriate for one model from that of the other. The results show that the OSWSs retrieved using our hybrid method has RMSE of 1.66 m/s and the correlation coefficient are 0.9, thereby significantly outperforming both the C-2PO and CMOD4 models.

scholarly journals A Generalized Simulation Capability for Rotating Beam Scatterometers

2018 ◽  
Author(s):  
Zhen Li ◽  
Ad Stoffelen ◽  
Anton Verhoef
Keyword(s):  
Incidence Angle ◽  
Retrieval Algorithm ◽  
Pencil Beam ◽  
Wind Vector ◽  
Response Functions ◽  
Rotating Beam ◽  
Retrieval Performance ◽  
Wind Retrieval ◽  
Spatial Response

Abstract. Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT, WindRad on FY-3E and SeaWinds on QuikScat. Besides the comparison of the so-called 1st rank-solution retrieval skill of the input wind field, other Figure of Merits (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the Wind Vector Cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir, but also to investigate resolution and accuracy through incorporating and analysing the spatial response functions of the simulated Level-1B data for each WVC.

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