Method of calculating the cross-wind speed at the entrance aperture of an adaptive system based on Shack–Hartmann wavefront sensor measurements

2015 ◽  
Vol 51 (6) ◽  
pp. 587-592 ◽  
Author(s):  
L. V. Antoshkin ◽  
N. V. Goleneva ◽  
V. V. Lavrinov ◽  
L. N. Lavrinova
Keyword(s):  
Wind Speed ◽  
Adaptive System ◽  
Wavefront Sensor ◽  
Entrance Aperture ◽  
The Cross

scholarly journals Quantifying methane point sources from fine-scale (GHGSat) satellite observations of atmospheric methane plumes

2018 ◽  
Author(s):  
Daniel J. Varon ◽  
Daniel J. Jacob ◽  
Jason McKeever ◽  
Dylan Jervis ◽  
Berke O. A. Durak ◽  
...  
Keyword(s):  
Wind Speed ◽  
Methane Emissions ◽  
Point Sources ◽  
Small Scale ◽  
Atmospheric Methane ◽  
Flux Method ◽  
Pixel Resolution ◽  
Cross Sectional ◽  
Source Rate ◽  
The Cross

Abstract. Anthropogenic methane emissions originate from a large number of relatively small point sources. The planned GHGSat satellite fleet aims to quantify emissions from individual point sources by measuring methane column plumes over selected ~ 10 × 10 km2 domains with ≤ 50 × 50 m2 pixel resolution and 1–5 % measurement precision. Here we develop algorithms for retrieving point source rates from such measurements. We simulate a large ensemble of instantaneous methane column plumes at 50 × 50 m2 pixel resolution for a range of atmospheric conditions using the Weather Research and Forecasting model (WRF) in large eddy simulation (LES) mode and adding instrument noise. We show that standard methods to infer source rates by Gaussian plume inversion or source pixel mass balance are prone to large errors because the turbulence cannot be properly parameterized on the small scale of instantaneous methane plumes. The integrated mass enhancement (IME) method, which relates total plume mass to source rate, and the cross-sectional flux method, which infers source rate from fluxes across plume transects, are better adapted to the problem. We show that the IME method with local measurements of the 10-m wind speed can infer source rates with error of 0.07–0.17 t h−1 + 5–12 % depending on instrument precision (1–5 %). The cross-sectional flux method has slightly larger errors (0.07–0.26 t h−1 + 8–12 %) but a simpler physical basis. For comparison, point sources larger than 0.5 t h−1 contribute more than 75 % of methane emissions reported to the U.S. Greenhouse Gas Reporting Program. Additional error applies if local wind speed measurements are not available, and may dominate the overall error at low wind speeds. Low winds are beneficial for source detection but not for source quantification.

scholarly journals Preliminary Analysis of Wind Retrieval Based on GF-3 Wave Mode Images

2018 ◽  
Author(s):  
Lei Wang ◽  
Bing Han ◽  
Xinzhe Yuan ◽  
Bin Lei ◽  
Chibiao Ding ◽  
...  
Keyword(s):  
Wind Speed ◽  
Incidence Angle ◽  
Surface Wind ◽  
Wave Mode ◽  
Azimuth Angle ◽  
Calibration Constant ◽  
Wind Fields ◽  
Wind Retrieval ◽  
Geophysical Model ◽  
The Cross

In this paper, we analyze the measurements of the normalized radar cross-section(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, we verify the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. The Amazon rainforest experiment results show that the accuracy of obtained calibration constant meets the requirements. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it has an approximate cosine modulation with the wind direction when the wind speed is greater than 8m/s. The cross-polarized system noise floor is low enough to ignore it in wind retrieval. Furthermore, we also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ_VV^0 retrieved from new PR models as well as σ_HH^0 is in good agreement with σ_VV^0 extracted from SAR images directly. And it is also shown that considering the azimuth angle can improve polarization conversion accuracy.

Closed loop adaptive system with Hartmann wavefront sensor for CO2 laser radiation correction

2014 ◽  
Author(s):  
Alexis Kudryashov ◽  
Alex Alexandrov ◽  
Vadim Samarkin ◽  
Alexey Rukosuev ◽  
Pierre Galarneau
Keyword(s):  
Laser Radiation ◽  
Closed Loop ◽  
Adaptive System ◽  
Wavefront Sensor ◽  
Radiation Correction

Real-time wind speed measurement using wavefront sensor data

2010 ◽  
Author(s):  
M. B. Roopashree ◽  
Vyas Akondi ◽  
Raghavendra Prasad Budihala
Keyword(s):  
Wind Speed ◽  
Real Time ◽  
Sensor Data ◽  
Wavefront Sensor ◽  
Speed Measurement ◽  
Wind Speed Measurement

scholarly journals Tropical Cyclone Wind Speed Retrieval from Dual-Polarization Sentinel-1 EW Mode Products

2020 ◽  
Vol 37 (9) ◽  
pp. 1713-1724
Author(s):  
Yuan Gao ◽  
Changlong Guan ◽  
Jian Sun ◽  
Lian Xie
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Noise Removal ◽  
Cross Polarization ◽  
High Wind ◽  
Dual Polarization ◽  
High Wind Speed ◽  
Wind Speeds ◽  
Hurricane Wind ◽  
The Cross

AbstractRecent studies indicate that the cross-polarization synthetic aperture radar (SAR) images have the ability of retrieving high wind speed on ocean surface without wind direction input. This study presents a new approach for tropical cyclone (TC) wind speed retrieval utilizing thermal-noise-removed Sentinel-1 dual-polarization (VV + VH) Extra-Wide Swath (EW) Mode products. Based on 20 images of 9 TCs observed in the 2016 and 2018 and SAR-collocated European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) data and the National Oceanic and Atmospheric Administration (NOAA) Hurricane Research Division’s (HRD) Real-time Hurricane Wind Analysis System (H*Wind) data, a subswath-based geophysical model function (GMF) Sentinel-1 EW Mode Wind Speed Retrieval Model after Noise Removal (S1EW.NR) is developed and validated statistically. TC wind speed is retrieved by using the proposed GMF and the C-band model 5.N (CMOD5.N). The results show that the wind speeds retrieved by the S1EW.NR model are in good agreement with wind references up to 31 m s−1. The correlation coefficient, bias, and standard deviation between the retrieval results and reference wind speeds are 0.74, −0.11, and 3.54 m s−1, respectively. Comparison of the wind speeds retrieved from both channels suggests that the cross-polarized signal is more suitable for high–wind speed retrieval, indicating the promising capability of cross-polarization SAR for TC monitoring.

scholarly journals Preliminary Analysis of Wind Retrieval Based on GF-3 Wave Mode Images

2018 ◽  
Author(s):  
Lei Wang ◽  
Bing Han ◽  
xinzhe yuan ◽  
Bin Lei ◽  
Chibiao Ding ◽  
...  
Keyword(s):  
Wind Speed ◽  
Incidence Angle ◽  
Surface Wind ◽  
Wave Mode ◽  
Azimuth Angle ◽  
Calibration Constant ◽  
Wind Fields ◽  
Wind Retrieval ◽  
Geophysical Model ◽  
The Cross

In this paper, we analyze the measurements of the normalized radar cross-section(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, we verify the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. The Amazon rainforest experiment results show that the accuracy of obtained calibration constant meets the requirements. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it has an approximate cosine modulation with the wind direction when the wind speed is greater than 8m/s. The cross-polarized system noise floor is low enough to ignore it in wind retrieval. Furthermore, we also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ_VV^0 retrieved from new PR models as well as σ_HH^0 is in good agreement with σ_VV^0 extracted from SAR images directly. And it is also shown that considering the azimuth angle can improve polarization conversion accuracy.

scholarly journals Preliminary Analysis of Wind Retrieval Based on GF-3 Wave Mode Images

2018 ◽  
Author(s):  
Lei Wang ◽  
Bin Han ◽  
Xinzhe Yuan ◽  
Bin Lei ◽  
Chibiao Ding ◽  
...  
Keyword(s):  
Wind Speed ◽  
Incidence Angle ◽  
Surface Wind ◽  
Wave Mode ◽  
Azimuth Angle ◽  
Calibration Constant ◽  
Wind Fields ◽  
Wind Retrieval ◽  
Geophysical Model ◽  
The Cross

In this paper, we analyze the measurements of the normalized radar cross-section(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, we verify the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. The Amazon rainforest experiment results show that the accuracy of obtained calibration constant meets the requirements. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it has an approximate cosine modulation with the wind direction when the wind speed is greater than 8m/s. The cross-polarized system noise floor is low enough to ignore it in wind retrieval. Furthermore, we also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ_VV^0 retrieved from new PR models as well as σ_HH^0 is in good agreement with σ_VV^0 extracted from SAR images directly. And it is also shown that considering the azimuth angle can improve polarization conversion accuracy.

Shack-Hartmann wavefront sensor for phase distortions, wind speed and Fried parameter estimations

2019 ◽  
Author(s):  
Lidia A. Bolbasova ◽  
Alexey Gritsuta ◽  
Evgeniy Kopylov ◽  
Vitaliy Lavrinov ◽  
Vladimir Lukin ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wavefront Sensor ◽  
Parameter Estimations ◽  
Phase Distortions

Wind Speed and Direction on Water Application Uniformity of a Mechanical Lateral-Move Irrigation System

2017 ◽  
Vol 33 (4) ◽  
pp. 543-549 ◽  
Author(s):  
Bernardo Gomes Nörenberg ◽  
Lessandro Coll Faria ◽  
Osvaldo Rettore Neto ◽  
Samuel Beskow ◽  
Alberto Colombo ◽  
...  
Keyword(s):  
Wind Speed ◽  
Cross Validation ◽  
Irrigation System ◽  
Field Tests ◽  
Sprinkler Irrigation ◽  
Support Tool ◽  
Validation Method ◽  
Wind Speeds ◽  
Statistical Measures ◽  
The Cross

Abstract. In order to develop models for representation of Christiansen’s Uniformity (CU) and Distribution Uniformity (DU) as a function of wind speed, 32 in-field tests evaluating a mechanical lateral-move irrigation system, used in rice production, were carried out in southern Rio Grande do Sul, Brazil. These tests were used to generate two third-order polynomial models for estimation of CU and DU, which were then validated based on a cross-validation approach. The generated models had their accuracy quantified by means of the following statistical measures: determination coefficient (R2), reliability and performance index (c), root mean square error (RMSE), and Nash-Sutcliffe coefficient (CNS). Wind direction had no significant influence on CU and DU. The CU values estimated from the cross-validation method were compared to those observed, resulting in R2 = 0.44, c = 0.53, RMSE = 1.82%, and CNS = 0.43. Likewise, DU values estimated from the cross-validation method were compared to the observed values, culminating in R2, c, RMSE, and CNS equal to 0.41%, 0.51%, 2.81% and 0.40%, respectively. The models developed in this study can be useful as a support tool for decision making when applying mechanical lateral-move irrigation systems, allowing estimation of CU and DU values with satisfactory precision for wind speeds less than 5.5 m s-1. Keywords: In-field tests, Rice, Sprinkler irrigation.

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