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.

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.

scholarly journals Evaluation of a Support Vector Machine–Based Single-Doppler Wind Retrieval Algorithm

2017 ◽  
Vol 34 (8) ◽  
pp. 1749-1761 ◽  
Author(s):  
Nan Li ◽  
Ming Wei ◽  
Yongjiang Yu ◽  
Wengang Zhang
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Condition Number ◽  
Condition Numbers ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Small Scale ◽  
Wind Retrieval ◽  
Original Algorithm ◽  
Retrieval Accuracy

AbstractWind retrieval algorithms are required for Doppler weather radars. In this article, a new wind retrieval algorithm of single-Doppler radar with a support vector machine (SVM) is analyzed and compared with the original algorithm with the least squares technique. Through an analysis of coefficient matrices of equations corresponding to the optimization problems for the two algorithms, the new algorithm, which contains a proper penalization parameter, is found to effectively reduce the condition numbers of the matrices and thus has the ability to acquire accurate results, and the smaller the analysis volume is, the smaller the condition number of the matrix. This characteristic makes the new algorithm suitable to retrieve mesoscale and small-scale and high-resolution wind fields. Afterward, the two algorithms are applied to retrieval experiments to implement a comparison and a discussion. The results show that the penalization parameter cannot be too small, otherwise it may cause a large condition number; it cannot be too large either, otherwise it may change the properties of equations, leading to retrieved wind direction along the radial direction. Compared with the original algorithm, the new algorithm has definite superiority with the appropriate penalization parameters for small analysis volumes. When the suggested small analysis volume dimensions and penalization parameter values are adopted, the retrieval accuracy can be improved by 10 times more than the traditional method. As a result, the new algorithm has the capability to analyze the dynamical structures of severe weather, which needs high-resolution retrieval, and the potential for quantitative applications such as the assimilation in numerical models, but the retrieval accuracy needs to be further improved in the future.

scholarly journals The MIGHTI Wind Retrieval Algorithm: Description and Verification

2017 ◽  
Vol 212 (1-2) ◽  
pp. 585-600 ◽  
Author(s):  
Brian J. Harding ◽  
Jonathan J. Makela ◽  
Christoph R. Englert ◽  
Kenneth D. Marr ◽  
John M. Harlander ◽  
...  
Keyword(s):  
Retrieval Algorithm ◽  
Wind Retrieval

scholarly journals Validating precision estimates in horizontal wind measurements from a Doppler lidar

2017 ◽  
Vol 10 (3) ◽  
pp. 1229-1240 ◽  
Author(s):  
Rob K. Newsom ◽  
W. Alan Brewer ◽  
James M. Wilczak ◽  
Daniel E. Wolfe ◽  
Steven P. Oncley ◽  
...  
Keyword(s):  
Wind Speed ◽  
Radial Velocity ◽  
Sonic Anemometer ◽  
Direct Calculation ◽  
Measurement Precision ◽  
Horizontal Wind ◽  
Retrieval Algorithm ◽  
Doppler Lidar ◽  
Wind Fields ◽  
Wind Retrieval

Abstract. Results from a recent field campaign are used to assess the accuracy of wind speed and direction precision estimates produced by a Doppler lidar wind retrieval algorithm. The algorithm, which is based on the traditional velocity-azimuth-display (VAD) technique, estimates the wind speed and direction measurement precision using standard error propagation techniques, assuming the input data (i.e., radial velocities) to be contaminated by random, zero-mean, errors. For this study, the lidar was configured to execute an 8-beam plan-position-indicator (PPI) scan once every 12 min during the 6-week deployment period. Several wind retrieval trials were conducted using different schemes for estimating the precision in the radial velocity measurements. The resulting wind speed and direction precision estimates were compared to differences in wind speed and direction between the VAD algorithm and sonic anemometer measurements taken on a nearby 300 m tower.All trials produced qualitatively similar wind fields with negligible bias but substantially different wind speed and direction precision fields. The most accurate wind speed and direction precisions were obtained when the radial velocity precision was determined by direct calculation of radial velocity standard deviation along each pointing direction and range gate of the PPI scan. By contrast, when the instrumental measurement precision is assumed to be the only contribution to the radial velocity precision, the retrievals resulted in wind speed and direction precisions that were biased far too low and were poor indicators of data quality.

scholarly journals Intelligent Wind Retrieval from Chinese Gaofen-3 SAR Imagery in Quad Polarization

2019 ◽  
Vol 36 (11) ◽  
pp. 2121-2138 ◽  
Author(s):  
Weizeng Shao ◽  
Shuai Zhu ◽  
Xiaopeng Zhang ◽  
Shuiping Gou ◽  
Changzhe Jiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Retrieval Algorithm ◽  
Neural Network Method ◽  
Wind Retrieval ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural ◽  
Sar Imagery

AbstractThis study proposes the use of the artificial neural network for wind retrieval with Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) data. More than 10 000 images acquired in wave mode and quad-polarization strip map were collected over global seas throughout the 2-yr mission. The GF-3 operated in a quad-polarization channel—vertical–vertical (VV), vertical–horizontal (VH), horizontal–horizontal (HH), and horizontal–vertical (HV). These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts at a 0.125° grid. The newly released wind retrieval algorithm for copolarization (VV and HH) SAR included CMOD7 and C-SARMOD2. We developed an algorithm based on an artificial neural network method using the SAR-measured normalized radar cross section at quad-polarization channels, herein named QPWIND_GF. Simulations using the QPWIND_GF showed that the correlation coefficient of wind speed was 0.94. We then validated the retrieval wind speeds against the measurements at a 0.25° grid from the Advanced Scatterometer. A comparison showed that the root-mean-square error (RMSE) of wind speed was 0.74 m s−1, which was better than the wind speed obtained using state-of-the-art methods—including, for example, CMOD7 (RMSE 0.88 m s−1) and C-SARMOD2 (RMSE 1.98 m s−1). The finding indicated that the accuracy of wind retrieval from GF-3 SAR images was significantly improved. Our work demonstrates the advanced feasibility of an artificial neural network method for SAR marine applications.

scholarly journals Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validation

2021 ◽  
Author(s):  
Oliver Lux ◽  
Christian Lemmerz ◽  
Fabian Weiler ◽  
Uwe Marksteiner ◽  
Benjamin Witschas ◽  
...  
Keyword(s):  
Direct Detection ◽  
Near Field ◽  
Measurement Range ◽  
Wind Data ◽  
Retrieval Algorithm ◽  
Random Errors ◽  
Wind Retrieval ◽  
Rayleigh Channel ◽  
Wind Lidar ◽  
Doppler Wind Lidar

Abstract. The realization of the European Space Agency’s Aeolus mission was supported by the long-standing development and field deployment of the ALADIN Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of the Atmospheric LAser Doppler INstrument (ALADIN), the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer’s high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent Doppler wind lidar that was deployed in parallel on-board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 m∙s−1 to about 1.5 m∙s−1, demonstrating the capability of such a direct detection DWL. Moreover, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument’s near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m∙s−1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the L2B Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) for ascending and descending orbits as well as random errors of about 3 m∙s−1 (HLOS) for the Mie and close to 6 m∙s−1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality.

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