SAR Observation Error Estimation Based on Maximum Relative Projection Matching by Y. Zhang, B. P. Wang, Y. Fang, Z. X. Song

“…The existing sparse imaging observation error estimation methods are to usually estimate the error of each observation position by substituting the error parameters into the iterative reconstruction process, which has a huge calculation cost. …”
Get full text

A compensation method for PMSM sensorless control with parameter identification considering SMO observation error by Ruizhi Guan, Jinglin Liu, Mengqi Li, Minglang Xiao, Xinran Shi

Get full text

Efficient estimation method of population mean with non-response and observational error under ORRT models by Sunil Kumar, Chanda Rani, Housila P. Singh, J. P. S. Joorel

“…A new class of estimators are introduced to simultaneously account for social desirability bias, non-response and observational error. These estimators are evaluated against existing methods to assess their properties and effectiveness. …”
Get full text

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery by Andrii Zaiats, T. Trevor Caughlin, Jennyffer Cruz, David S. Pilliod, Megan E. Cattau, Rongsong Liu, Richard Rachman, Maisha Maliha, Donna Delparte, John D. J. Clare

“…Raw counts of plant abundance in high‐resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting (p) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. …”
Get full text

Assimilating GOES‐16 ABI All‐Sky Brightness Temperature Into the HAFS Dual‐Resolution Self‐Consistent EnVar DA System: Methods for Observation Error Estimation and Impact on Hurricane Laura (2020) by Xu Lu, Xuguang Wang

“…Focusing on the pre‐rapid intensification period of Hurricane Laura, the results demonstrated that assimilating ABI observations without proper observation error treatment can be neutral or even detrimental. …”
Get full text

Using audio lures to improve golden‐winged warbler (Vermivora chrysoptera) detection during point‐count surveys by Darin J. McNeil Jr., Clint R. V. Otto, Gary J. Roloff

Subjects: Get full text

Quantifying observer variance in expansive monitoring program indicator data with heterogeneous-variance mixed-effects models by Leah T. Dreesmann, Timothy R. Johnson, Jason W. Karl

Subjects: “…Observer error…”
Get full text

A Null Space Sensitivity Analysis for Hydrological Data Assimilation with Ensemble Methods by Nick Martin, Jeremy White, Paul Southard

Subjects: Get full text

Warring tautologies: moral dissent from a cognitivist perspective by Matthias Kiesselbach

“… It is commonly thought that the prevalence of moral dissent poses a problem for the moral cognitivist, forcing her to diagnose either a lot of misunderstanding, or a lot of unexplained observational error. Since mere misunderstanding can be ruled out in most cases of moral dissent, and since the diagnosis of widespread unexplained error is interpretively unstable, prevalent dissent has pushed many philosophers towards non-cognitivism. …”
Get full text

Robust Logistic Modelling for Datasets with Unusual Points by Kumru Urgancı Tekın, Burcu Mestav, Neslihan İyit

“…Unusual Points (UPs) occur for different reasons, such as an observational error or the presence of a phenomenon with unknown cause. …”
Get full text

An Intelligent Trajectory Prediction Algorithm for Hypersonic Glide Targets Based on Maneuver Mode Identification by Mingjie Li, Chijun Zhou, Lei Shao, Humin Lei

“…And it is also proved valid with some observational error.…”
Get full text

Using Chemical Transport Model and Climatology Data as Backgrounds for Aerosol Optical Depth Spatial–Temporal Optimal Interpolation by Natallia Miatselskaya, Andrey Bril, Anatoly Chaikovsky

“…Spatial–temporal OI (STOI) utilizes both spatial and temporal observational error covariance and background error covariance. …”
Get full text

Research on a Quality Control Method for L Band Second-Level Radiosonde toward Assimilation Applications by Dan WANG, Jincheng WANG, Weihong TIAN

“…Since 2003， the World Meteorological Organization （WMO） proposed and coordinated a switch from traditional radiosonde format （TAC code） to second-resolution binary （BUFR） report， which leads the assimilation of second radiosonde data in numerical prediction model becoming the future trend.Data quality control is a key and basic work before assimilation application.In addition， the observational error characteristics of Lband second-level radiosonde are significantly different from that of conventional radiosonde.Therefore， a two-step quality control method for assimilation application is developed in this paper.The first step integrates conventional radiosonde quality control procedures and inserts several additional steps according to the data characteristics of second-level radiosonde， which aims at eliminating human-observation errors as much as possible.The second step introduces a dynamic blacklist checking module into the assimilation system， at the same time remaining the old OMB （observation minus background） check method， so as to control the representative deviation between the observation and the model background.By comparing and analyzing the statistical characteristics of the observation samples before and after the quality control procedure， and combining them with the actual assimilation analysis results of the NWP model， the rationality of the two-step quality control procedure has been comprehensively verified.It reveals that the data anomaly is greatly reduced after the two-step quality control procedure， at the same time the OMB is closer to a Gaussian distribution.Moreover， the validity of data assimilation is enhanced leading to a better assimilation analysis effect.The work lays a foundation for the future operational application of L-band second-level radiosonde data in GRAPES （Global/Regional Assimilation and Prediction System） model.…”
Get full text

Robust prediction of chaotic systems with random errors using dynamical system deep learning by Zixiang Wu, Jianping Li, Hao Li, Mingyu Wang, Ning Wang, Guangcan Liu

“…Notably, as the magnitude of errors decreases, the advantage of the DSDL over traditional machine learning methods becomes more pronounced, highlighting the DSDL’s capacity to effectively extract the temporal evolution characteristics of chaotic systems from time series and to identify the true system state within observational error bands, significantly mitigating the impact of random errors. …”
Get full text

Assimilation of Fengyun-4A Atmospheric Motion Vectors and Its Impact on China Meteorological Administration—Beijing System Forecasts by Yanhui Xie, Shuting Zhang, Xin Sun, Min Chen, Jiancheng Shi, Yu Xia, Ruixia Liu

“…The statistical characterization of FY-4A AMVs was firstly analyzed, and an optimal observation error in each vertical level was obtained. …”
Get full text

Enhancing Robustness of Variational Data Assimilation in Chaotic Systems: An α-4DVar Framework with Rényi Entropy and α-Generalized Gaussian Distributions by Yuchen Luo, Xiaoqun Cao, Kecheng Peng, Mengge Zhou, Yanan Guo

“…Traditional 4-dimensional variational data assimilation methods have limitations due to the Gaussian distribution assumption of observation errors, and the gradient of the objective functional is vulnerable to observation noise and outliers. …”
Get full text

An accessible method for implementing hierarchical models with spatio-temporal abundance data. by Beth E Ross, Mevin B Hooten, David N Koons

“…Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. …”
Get full text

Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders by M. G. A. Hendrickx, M. G. A. Hendrickx, J. Vanderborght, J. Vanderborght, P. Janssens, P. Janssens, P. Janssens, S. Bombeke, E. Matthyssen, A. Waverijn, J. Diels, J. Diels

“…Sensor deviations from the “true” MZ soil moisture were defined as observational errors and lump both measurement errors and representational errors. …”
Get full text

Regional Exponential Observation and error by NIHAD SHAREEF KHALAF

“…We introduce the notion of exponential observation error. We show that, the number and location of sensor may be some interest in the existence of regional exponential observation state. …”
Get full text

Enhancing catch-based stock assessment in data-limited fisheries with proxy CPUE indicators in the Yellow Sea by Kun Wang, Qi Li, Chongliang Zhang, Binduo Xu, Yiping Ren

“…Results indicate that proxy-CPUE substantially improves the robustness of stock status estimates, especially by mitigating the impact of high catch observation errors—reducing estimate variations by 50% compared to catch-only methods. …”
Get full text