scholarly journals Comparison and synergy of stratospheric ozone measurements by satellite limb sounders and the ground-based microwave radiometer SOMORA

2007 ◽  
Vol 7 (2) ◽  
pp. 5053-5098 ◽  
Author(s):  
K. Hocke ◽  
N. Kämpfer ◽  
D. Ruffieux ◽  
L. Froidevaux ◽  
A. Parrish ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Cross Validation ◽  
Stratospheric Ozone ◽  
Microwave Radiometer ◽  
Measurement Techniques ◽  
Horizontal Distance ◽  
Ground Station ◽  
Difference Field ◽  
The Cross ◽  
Satellite Ground Station

Abstract. Stratospheric O3 profiles obtained by the satellite limb sounders Aura/MLS, ENVISAT/MIPAS, ENVISAT/GOMOS, SAGE-II, SAGE-III, UARS/HALOE are compared to coincident O3 profiles of the ground-based microwave radiometer SOMORA in Switzerland. Data from the various measurement techniques are within 10% at altitudes below 45 km. At altitudes 45–60 km, the relative O3 differences are within a range of 50% Larger deviations at upper altitudes are attributed to larger relative measurement errors caused by lower O3 concentrations. The spatiotemporal characteristics of the O3 differences (satellite – ground station) are investigated by analyzing about 5000 coincident profile pairs of Aura/MLS (retrieval version 1.5) and SOMORA. The probability density function of the O3 differences is represented by a Gaussian normal distribution (except for profile pairs around the stratopause at noon). The dependence of the O3 differences on the horizontal distance between the sounding volumes of Aura/MLS and SOMORA is derived. While the mean bias (Aura/MLS – SOMORA) is constant with increasing horizontal distance (up to 800 km), the standard deviation of the O3 differences increases from around 8 to 12% in the mid-stratosphere. Geographical maps yield azimuthal dependences and horizontal gradients of the O3 difference field around the SOMORA ground station. Coherent oscillations of O3 are present in the time series of Aura/MLS and SOMORA (e.g., due to traveling planetary waves). Ground- and space-based measurements often complement one another. We introduce the double differencing technique which allows both the cross-validation of two satellites by means of a ground station and the cross-validation of distant ground stations by means of one satellite. Temporal atmospheric noise in the geographical ozone map over Payerne is significantly reduced by combination of the data from SOMORA and Aura/MLS. These analyses illustrate the synergy between ground-based and space-based measurements.

scholarly journals Comparison and synergy of stratospheric ozone measurements by satellite limb sounders and the ground-based microwave radiometer SOMORA

2007 ◽  
Vol 7 (15) ◽  
pp. 4117-4131 ◽  
Author(s):  
K. Hocke ◽  
N. Kämpfer ◽  
D. Ruffieux ◽  
L. Froidevaux ◽  
A. Parrish ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Cross Validation ◽  
Stratospheric Ozone ◽  
Microwave Radiometer ◽  
Measurement Techniques ◽  
Horizontal Distance ◽  
Ground Station ◽  
Difference Field ◽  
The Cross ◽  
Satellite Ground Station

Abstract. Stratospheric O3 profiles obtained by the satellite limb sounders Aura/MLS, ENVISAT/MIPAS, ENVISAT/GOMOS, SAGE-II, SAGE-III, UARS/HALOE are compared to coincident O3 profiles of the ground-based microwave radiometer SOMORA in Switzerland. Data from the various measurement techniques are within 10% at altitudes below 45 km. At altitudes 45–60 km, the relative O3 differences are within a range of 50%. Larger deviations at upper altitudes are attributed to larger relative measurement errors caused by lower O3 concentrations. The spatiotemporal characteristics of the O3 differences (satellite – ground station) are investigated by analyzing about 2300 coincident profile pairs of Aura/MLS (retrieval version 1.5) and SOMORA. The probability density function of the O3 differences is represented by a Gaussian normal distribution. The dependence of the O3 differences on the horizontal distance between the sounding volumes of Aura/MLS and SOMORA is derived. While the mean bias (Aura/MLS – SOMORA) is constant with increasing horizontal distance (up to 800 km), the standard deviation of the O3 differences increases from around 8 to 11% in the mid-stratosphere. Geographical maps yield azimuthal dependences and horizontal gradients of the O3 difference field around the SOMORA ground station. Coherent oscillations of O3 are present in the time series of Aura/MLS and SOMORA (e.g., due to traveling planetary waves). Ground- and space-based measurements often complement one another. We discuss the double differencing technique which allows both the cross-validation of two satellites by means of a ground station and the cross-validation of distant ground stations by means of one satellite. Temporal atmospheric noise in the geographical ozone map over Payerne is significantly reduced by combination of the data from SOMORA and Aura/MLS. These analyses illustrate the synergy of ground-based and space-based measurements.

scholarly journals Estimating sediment thickness by using horizontal distance to outcrop as secondary information

2017 ◽  
Author(s):  
Nils-Otto Kitterød
Keyword(s):  
Cross Validation ◽  
Estimation Error ◽  
Small Scale ◽  
Horizontal Distance ◽  
Estimation Variance ◽  
Sediment Thickness ◽  
Preferential Sampling ◽  
Cross Covariance ◽  
The Cross ◽  
Secondary Information

Abstract. Sediment thickness (D) was estimated utilizing a publically available well database from Norway, GRANADA. General challenges associated with such databases typically involve clustering and bias of the data material due to preferential sampling. However, if information about horizontal distance to the nearest outcrop (L) is included, does the spatial estimation of D improve? This idea was tested comparing two cross-validation results: ordinary kriging (OK) where L was disregarded; and co-kriging (CK) where cross-covariance between D and L was included. The analysis resulted in only minor differences between OK and CK in terms of absolute estimation error, however CK produced more precise results than OK. All observations were declustered and transformed to standard normal probability density functions before estimation and back transformed for the cross-validation analysis. The semivariogram analysis gave correlation lengths for D and L of approx. 10 km and 6 km. These correlations reduce the estimation variance in the cross-validation analysis because more than 50 % of the data material had two or more observations within a radius of 5 km. The small-scale variance of D, however, was about 50 % of the total variance, which gave an accuracy of less than 60 % for most of the cross-validation cases. Despite of the noisy character in the data material, the analysis demonstrates that L can be used as a secondary information to reduce the estimation variance of D.

scholarly journals Estimating unconsolidated sediment cover thickness by using the horizontal distance to a bedrock outcrop as secondary information

2017 ◽  
Vol 21 (8) ◽  
pp. 4195-4211
Author(s):  
Nils-Otto Kitterød
Keyword(s):  
Cross Validation ◽  
Small Scale ◽  
Horizontal Distance ◽  
Estimation Variance ◽  
Unconsolidated Sediment ◽  
Sediment Cover ◽  
The Cross ◽  
Secondary Information ◽  
Cover Thickness ◽  
Bedrock Outcrop

Abstract. Unconsolidated sediment cover thickness (D) above bedrock was estimated by using a publicly available well database from Norway, GRANADA. General challenges associated with such databases typically involve clustering and bias. However, if information about the horizontal distance to the nearest bedrock outcrop (L) is included, does the spatial estimation of D improve? This idea was tested by comparing two cross-validation results: ordinary kriging (OK) where L was disregarded; and co-kriging (CK) where cross-covariance between D and L was included. The analysis showed only minor differences between OK and CK with respect to differences between estimation and true values. However, the CK results gave in general less estimation variance compared to the OK results. All observations were declustered and transformed to standard normal probability density functions before estimation and back-transformed for the cross-validation analysis. The semivariogram analysis gave correlation lengths for D and L of approx. 10 and 6 km. These correlations reduce the estimation variance in the cross-validation analysis because more than 50 % of the data material had two or more observations within a radius of 5 km. The small-scale variance of D, however, was about 50 % of the total variance, which gave an accuracy of less than 60 % for most of the cross-validation cases. Despite the noisy character of the observations, the analysis demonstrated that L can be used as secondary information to reduce the estimation variance of D.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals Amateur satellite ground station: Troubleshooting and lesson learned

2021 ◽  
Vol 1768 (1) ◽  
pp. 012013
Author(s):  
F Z Ali ◽  
S N M Rahim ◽  
M H Jusoh
Keyword(s):  
Ground Station ◽  
Satellite Ground Station

scholarly journals Gravity-Based Characterization of Three-Axis Accelerometers in Terms of Intrinsic Accelerometer Parameters

2017 ◽  
Vol 122 ◽  
Author(s):  
Jon Geist ◽  
Muhammad Yaqub Afridi ◽  
Craig D. McGray ◽  
Michael Gaitan
Keyword(s):  
Gravitational Field ◽  
Coordinate System ◽  
Measurement Errors ◽  
Angle Measurement ◽  
Sensitivity Matrix ◽  
Cross Sensitivity ◽  
Measurement Capability ◽  
Intrinsic Parameters ◽  
The Cross ◽  
Alignment Errors

Cross-sensitivity matrices are used to translate the response of three-axis accelerometers into components of acceleration along the axes of a specified coordinate system. For inertial three-axis accelerometers, this coordinate system is often defined by the axes of a gimbal-based instrument that exposes the device to different acceleration inputs as the gimbal is rotated in the local gravitational field. Therefore, the cross-sensitivity matrix for a given three-axis accelerometer is not unique. Instead, it depends upon the orientation of the device when mounted on the gimbal. We define nine intrinsic parameters of three-axis accelerometers and describe how to measure them directly and how to calculate them from independently determined cross-sensitivity matrices. We propose that comparisons of the intrinsic parameters of three axis accelerometers that were calculated from independently determined cross-sensitivity matrices can be useful for comparisons of the cross-sensitivity-matrix measurement capability of different institutions because the intrinsic parameters will separate the accelerator-gimbal alignment differences among the participating institutions from the purely gimbal-related differences, such as gimbal-axis orthogonality errors, z-axis gravitational-field alignment errors, and angle-setting or angle-measurement errors.

scholarly journals Diffracting-grain identification from electron backscatter diffraction maps during residual stress measurements: a comparison between the sin2ψ and cosα methods

2019 ◽  
Vol 52 (4) ◽  
pp. 828-843 ◽  
Author(s):  
Dorian Delbergue ◽  
Damien Texier ◽  
Martin Lévesque ◽  
Philippe Bocher
Keyword(s):  
Residual Stress ◽  
Grain Size ◽  
Tilt Angle ◽  
Measurement Errors ◽  
Electron Backscatter Diffraction ◽  
Measurement Techniques ◽  
Single Exposure ◽  
X Ray ◽  
Electron Backscatter ◽  
Backscatter Diffraction

X-ray diffraction (XRD) is a widely used technique to evaluate residual stresses in crystalline materials. Several XRD measurement methods are available. (i) The sin2ψ method, a multiple-exposure technique, uses linear detectors to capture intercepts of the Debye–Scherrer rings, losing the major portion of the diffracting signal. (ii) The cosα method, thanks to the development of compact 2D detectors allowing the entire Debye–Scherrer ring to be captured in a single exposure, is an alternative method for residual stress measurement. The present article compares the two calculation methods in a new manner, by looking at the possible measurement errors related to each method. To this end, sets of grains in diffraction condition were first identified from electron backscatter diffraction (EBSD) mapping of Inconel 718 samples for each XRD calculation method and its associated detector, as each method provides different sets owing to the detector geometry or to the method specificities (such as tilt-angle number or Debye–Scherrer ring division). The X-ray elastic constant (XEC) ½S 2, calculated from EBSD maps for the {311} lattice planes, was determined and compared for the different sets of diffracting grains. It was observed that the 2D detector captures 1.5 times more grains in a single exposure (one tilt angle) than the linear detectors for nine tilt angles. Different XEC mean values were found for the sets of grains from the two XRD techniques/detectors. Grain-size effects were simulated, as well as detector oscillations to overcome them. A bimodal grain-size distribution effect and `artificial' textures introduced by XRD measurement techniques are also discussed.

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