scholarly journals Evaluating the Impact of Systematic Error on the Estimation Performance of a Length‐Based Spawning Potential Ratio

2021 ◽  
Vol 13 (6) ◽  
pp. 673-686
Author(s):  
Libin Dai ◽  
Fei Wang ◽  
Chunxia Gao ◽  
Cameron Hodgdon ◽  
Luoliang Xu ◽  
...  
Keyword(s):  
Systematic Error ◽  
Estimation Performance ◽  
Spawning Potential Ratio ◽  
The Impact

Investigation of mb and MS formulas for the Western United States and their impact on the MS/mb discriminant

1987 ◽  
Vol 77 (3) ◽  
pp. 987-995
Author(s):  
Marvin D. Denny ◽  
Steven R. Taylor ◽  
Eileen S. Vergino
Keyword(s):  
United States ◽  
Systematic Error ◽  
Analysis Of Variance ◽  
Magnitude Estimate ◽  
Peak Amplitude ◽  
Western United States ◽  
Station Correction ◽  
Simple Analysis ◽  
The Impact ◽  
Distance Correction

Abstract The impact of regional mb and MS formulas on regional MS/mb discrimination is investigated using a large number of Western United States earthquakes and explosions. Comparison of NEIS mb values with regional mb values shows a systematic error of 1.2. Additionally, a simple analysis of variance shows that the variance of the magnitude estimate is reduced when log(A) replaces log(A/T). These changes, along with a refinement of the distance correction, yield a new regional mb for the Western United States given by mb = log(A) + 2.4 log(Δ) − 3.95 + cj, where A = 0 to peak amplitude in nanometers, Δ is the distance in kilometers, and ci is a station correction. Usage of this formula improves the performance of regional MS/mb discrimination by a factor of 2 to 6.

scholarly journals A robust data cleaning procedure for eddy covariance flux measurements

2020 ◽  
Vol 17 (6) ◽  
pp. 1367-1391
Author(s):  
Domenico Vitale ◽  
Gerardo Fratini ◽  
Massimo Bilancia ◽  
Giacomo Nicolini ◽  
Simone Sabbatini ◽  
...  
Keyword(s):  
Systematic Error ◽  
Eddy Covariance ◽  
Outlier Detection ◽  
Stochastic Dynamics ◽  
Data Cleaning ◽  
Observation System ◽  
Correct Identification ◽  
Cleaning Procedure ◽  
Flux Data ◽  
The Impact

Abstract. The sources of systematic error responsible for introducing significant biases in the eddy covariance (EC) flux computation are manifold, and their correct identification is made difficult by the lack of reference values, by the complex stochastic dynamics, and by the high level of noise characterizing raw data. This work contributes to overcoming such challenges by introducing an innovative strategy for EC data cleaning. The proposed strategy includes a set of tests aimed at detecting the presence of specific sources of systematic error, as well as an outlier detection procedure aimed at identifying aberrant flux values. Results from tests and outlier detection are integrated in such a way as to leave a large degree of flexibility in the choice of tests and of test threshold values, ensuring scalability of the whole process. The selection of best performing tests was carried out by means of Monte Carlo experiments, whereas the impact on real data was evaluated on data distributed by the Integrated Carbon Observation System (ICOS) research infrastructure. Results evidenced that the proposed procedure leads to an effective cleaning of EC flux data, avoiding the use of subjective criteria in the decision rule that specifies whether to retain or reject flux data of dubious quality. We expect that the proposed data cleaning procedure can serve as a basis towards a unified quality control strategy for EC datasets, in particular in centralized data processing pipelines where the use of robust and automated routines ensuring results reproducibility constitutes an essential prerequisite.

scholarly journals Error Self-Calibration Algorithm for Acoustic Vector Sensor Array

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Peng Wang ◽  
Yujun Kong ◽  
Mingxing Zhang
Keyword(s):  
Sensor Array ◽  
Doa Estimation ◽  
Acoustic Vector Sensor ◽  
Estimation Performance ◽  
Self Calibration ◽  
Phase Errors ◽  
Vector Sensor ◽  
Calibration Algorithm ◽  
The Impact ◽  
Vector Sensor Array

In this paper, the errors of acoustic vector sensor array are classified, the impact factor of each error for the array signal model is derived, and the influence of each type of error on the direction-of-arrival (DOA) estimation performance of the array is compared by Monte Carlo experiments. Converting the directional error and location error to amplitude and phase errors, the optimization model and error self-calibration algorithm for acoustic vector sensor array are proposed. The simulation experiments and field experiment data processing of MEMS vector sensor array show that the proposed self-calibration algorithm has good parameter estimation performance and certain engineering practicability.

Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model

2008 ◽  
Vol 366 (1875) ◽  
pp. 2559-2577 ◽  
Author(s):  
J Berner ◽  
F.J Doblas-Reyes ◽  
T.N Palmer ◽  
G Shutts ◽  
A Weisheimer
Keyword(s):  
Cellular Automaton ◽  
Systematic Error ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Seasonal Prediction ◽  
Global Climate Models ◽  
Seasonal Forecasts ◽  
The Impact

The impact of a nonlinear dynamic cellular automaton (CA) model, as a representation of the partially stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium Range Weather Forecasts coupled ocean–atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the CA at the near-grid scale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the CA's representation of inherent model uncertainty.

New Channel Estimation Schemes for MIMO-OFDM Systems

2013 ◽  
Vol 709 ◽  
pp. 374-378
Author(s):  
Guo Jun Chen ◽  
You Zhou ◽  
Han Ying Hu
Keyword(s):  
Channel Estimation ◽  
Estimation Algorithm ◽  
Ofdm Systems ◽  
Estimation Performance ◽  
Mimo Ofdm ◽  
The Impact

Based on MIMO-OFDM systems,an adaptive modified factor EM channel estimation algorithm was proposed to improve the performance of channel estimation.Comparad to MEM,the adaptive modified factor was introduced in the algorithm,which reflected the impact of noise on signal more accurately and improved the estimation performance.The result of simulation showed that the algorithm proposed had better estimation performance.

scholarly journals Global component analysis of errors in five satellite-only global precipitation estimates

2020 ◽  
Author(s):  
Hanqing Chen ◽  
Bin Yong ◽  
Leyang Wang ◽  
Liliang Ren ◽  
Yang Hong
Keyword(s):  
Systematic Error ◽  
Random Error ◽  
Mean Squared Error ◽  
Systematic Errors ◽  
Topographic Factor ◽  
Error Adjustment ◽  
Error Components ◽  
Four Seasons ◽  
Retrieval Algorithms ◽  
The Impact

Abstract. Revealing the error components for satellite-only precipitation products (SPPs) can help algorithm developers and end-users substantially understand their error features and meanwhile is fundamental to customize retrieval algorithms and error adjustment models. Two error decomposition schemes were employed to explore the error components for five SPPs (i.e., MERG-Late, IMERG-Early, GSMaP-MVK, GSMaP-NRT, and PERSIANN-CCS) over different seasons, rainfall intensities, and topography classes. Firstly, this study depicted global maps of the total bias (total mean squared error) and its three (two) independent components for these five SPPs over four seasons for the first time. We found that the evaluation results between similar regions could not be extended to one another. Hit and/or false biases are major components of the total bias in most regions of the global land areas. In addition, the proportions of the systematic error are less than 20 % of total errors in most areas. One should note that each SPP has larger systematic errors in several regions (i.e., Russia, China, and Conterminous United States) for all four seasons, these larger systematic errors from retrieval algorithms are primarily due to the missed precipitation. Furthermore, IMERG suite and GSMaP-NRT display less systematic error in the rain rates with intensity less than 40 mm/day, while the systematic errors of GSMaP-MVK and PERSIANN-CCS increase with increasing rainfall intensity. Given that mean elevation cannot reflect the complex degree of terrain, we introduced the standard deviation of elevation (SDE) to replace mean elevation to better describe topographic complexity. Compared with other SPPs, GSMaP suite shows a stronger topographic dependency in the four bias scores. A novel metric namely normalized error component (NEC) was proposed to fairly evaluate the impact of the solely topographic factor on systematic (random) error. It is found that these products show different topographic dependency patterns in systematic (random) error. Meanwhile, the pattern of the impact of the solely topographic factor on systematic (random) error is similar to the relationship between systematic (random) error and topography because the average precipitations of all topography categories are very close. Finally, the potential directions of the improvement in satellite precipitation retrieval algorithms and error adjustment models were identified in this study.

scholarly journals ENVIRONMENTAL INFLUENCES ON THE STABILITY OF A PERMANENTLY INSTALLED LASER SCANNER

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 745-752
Author(s):  
M. Kuschnerus ◽  
D. Schröder ◽  
R. Lindenbergh
Keyword(s):  
Systematic Error ◽  
Point Cloud ◽  
Laser Scanner ◽  
Weather Station ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Mean ◽  
The Stability ◽  
The Impact ◽  
Mean Square Errors

Abstract. The advancement of permanently measuring laser scanners has opened up a wide range of new applications, but also led to the need for more advanced approaches on error quantification and correction. Time-dependent and systematic error influences may only become visible in data of quasi-permanent measurements. During a scan experiment in February/March 2020 point clouds were acquired every thirty minutes with a Riegl VZ-2000 laser scanner, and various other sensors (inclination sensors, weather station and GNSS sensors) were used to survey the environment of the laser scanner and the study site. Using this measurement configuration, our aim is to identify apparent displacements in multi-temporal scans due to systematic error influences and to investigate data quality for assessment of geomorphic changes in coastal regions. We analyse scan data collected around two storm events around 09/02/2020 (Ciara) and around 22/02/2020 (Yulia) and derive the impact of heavy storms on the point cloud data through comparison with the collected auxiliary data. To investigate the systematic residuals on data acquired by permanent laser scanning, we extracted several stable flat surfaces from the point cloud data. From a plane fitted through the respective surfaces of each scan, we estimated the mean displacement of each plane with the respective root mean square errors. Inclination sensors, internal and external, recorded pitch and roll values during each scan. We derived a mean inclination per scan (in pitch and roll) and the standard deviation from the mean as a measure of the stability of the laser scanner during each scan. Evaluation of the data recorded by a weather station together with knowledge of the movement behaviour, allows to derive possible causes of displacements and/or noise and correction models. The results are compared to independent measurements from GNSS sensors for validation. For wind speeds of 10 m/s and higher, movements of the scanner considerably increase the noise level in the point cloud data.

scholarly journals Improving performance of direction-of-arrival estimation using sparse array in passive radar

2020 ◽  
Vol 14 ◽  
pp. 174830262094337
Author(s):  
Geng Chen ◽  
Bo Tian ◽  
Jian Gong ◽  
Cunqian Feng
Keyword(s):  
Direction Of Arrival ◽  
Direction Of Arrival Estimation ◽  
Passive Radar ◽  
Direct Wave ◽  
Signal Noise ◽  
Sparse Array ◽  
Estimation Performance ◽  
Noise Ratio ◽  
Coprime Array ◽  
The Impact

A method based on sparse array is applied to direction-of-arrival estimation of passive radar in this paper to increase the number of resolvable sources and improve the direction-of-arrival estimation performance for coprime array. The virtual symmetric non-uniform linear array of coprime array based on passive radar signal model is introduced. Considering the impact of direct wave, extensive cancellation algorithm is used to cancel the direct wave, with the conventional MUSIC with spatial smoothing algorithm and virtual aperture filling applied on the sparse array of passive radar; the resolution of target is low in the low signal-noise-ratio. To effectively improve the estimation of the target under the low signal-noise-ratio, a noise subspace reconstruction method is proposed. The proposed direction-of-arrival estimation method can improve the direction-of-arrival estimation performance of passive radar. The simulations are provided to demonstrate the effectiveness of the proposed method.

The Impact of Severe Ionospheric Conditions on the Accuracy of Kinematic Position Estimation: Performance Analysis of Various Ionosphere Modeling Techniques

Navigation ◽  
2006 ◽  
Vol 53 (3) ◽  
pp. 203-217 ◽  
Author(s):  
DOROTA A. GREJNER-BRZEZINSKA ◽  
PAWEL WIELGOSZ ◽  
ISRAEL KASHANI ◽  
DRU A. SMITH ◽  
DOUGLAS S. ROBERTSON ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Position Estimation ◽  
Estimation Performance ◽  
Ionosphere Modeling ◽  
Modeling Techniques ◽  
Kinematic Position ◽  
The Impact
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