Space–Time Correlation and Its Effects on Methods for Detecting Aquatic Ecological Change

1985 ◽  
Vol 42 (8) ◽  
pp. 1391-1400 ◽  
Author(s):  
Steven P. Millard ◽  
John R. Yearsley ◽  
Dennis P. Lettenmaier
Keyword(s):  
Spatial Correlation ◽  
Analysis Of Variance ◽  
Multivariate Time Series ◽  
Simulated Data ◽  
Temporal Correlation ◽  
Time Correlation ◽  
Ecological Change ◽  
Correlated Errors ◽  
Monitoring Design ◽  
Temporally Correlated

The analysis of variance (ANOVA) is commonly used to analyze observations collected from aquatic monitoring programs designed to detect ecological change. ANOVA assumes that the deviations of the observations from their true means (the errors) are uncorrelated in space and time. Aquatic monitoring data often violate this assumption. The results of Monte Carlo simulations using simulated data generated from both statistically and mechanistically based models show that the presence of either spatially or temporally correlated errors can significantly affect the outcome of ANOVA tests. In practice, spatial correlation is more likely to be a problem than is temporal correlation, given typical monitoring frequencies. The effects of spatial correlation can be minimized through judicious use of control station pairing in the monitoring design. However, when insufficient flexibility exists in the monitoring design, alternate models, such as multivariate time series analysis, or multivariate analysis of variance, must be used in place of ANOVA.

An Alternative Method for Estimating Variance Components in Generalizability Theory

1990 ◽  
Vol 66 (2) ◽  
pp. 379-386 ◽  
Author(s):  
George A. Marcoulides
Keyword(s):  
Maximum Likelihood ◽  
Analysis Of Variance ◽  
Variance Components ◽  
Generalizability Theory ◽  
Simulated Data ◽  
Restricted Maximum Likelihood ◽  
Alternative Method ◽  
Sample Data

This study compares, using simulated data, two methods for estimating variance components in generalizability (G) studies. Traditionally variance components are estimated from an analysis of variance of sample data. The alternative method for estimating variance components is restricted maximum likelihood (REML). The results indicate that REML provides estimates for the components in the various designs that are closer to the true parameters than the estimates from analysis of variance.

Testing for Correlated Errors in the Analysis of Variance

Biometrics ◽  
1988 ◽  
Vol 44 (3) ◽  
pp. 695 ◽  
Author(s):  
M. A. Cameron ◽  
G. K. Eagleson ◽  
M. E. Willcox ◽  
D. G. Laing ◽  
H. Panhuber
Keyword(s):  
Analysis Of Variance ◽  
Correlated Errors

scholarly journals WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry-meteorology interactions

2021 ◽  
Author(s):  
Xu Feng ◽  
Haipeng Lin ◽  
Tzung-May Fu ◽  
Melissa P. Sulprizio ◽  
Jiawei Zhuang ◽  
...  
Keyword(s):  
Open Source ◽  
Spatial Correlation ◽  
Atmospheric Chemistry ◽  
Eastern China ◽  
Wrf Model ◽  
Temporal Correlation ◽  
Cloud Microphysics ◽  
Shortwave Radiation ◽  
Spatial Distributions ◽  
Meteorological Model

Abstract. We present the WRF-GC model v2.0, an online two-way coupling of the Weather Research and Forecasting (WRF) meteorological model (v3.9.1.1) and the GEOS-Chem chemical model (v12.7.2). WRF-GC v2.0 is built on the modular framework of WRF-GC v1.0 and further includes aerosol-radiation interactions (ARI) and aerosol-cloud interactions (ACI) based on bulk aerosol mass and composition, as well as the capability to nest multiple domains for high-resolution simulations. WRF-GC v2.0 is the first implementation of the GEOS-Chem model in an open-source dynamic model with chemical feedbacks to meteorology. We apply prescribed size distributions to the 10 aerosol types simulated by GEOS-Chem to diagnose aerosol optical properties and activated cloud droplet numbers; the results are passed to the WRF model for radiative and cloud microphysics calculations. We use WRF-GC v2.0 to conduct sensitivity simulations with different combinations of ARI and ACI over China during January 2015 and July 2016, with the goal of evaluating the simulated aerosol and cloud properties and the impacts of ARI and ACI on meteorology and air quality. WRF-GC reproduces the day-to-day variability of the aerosol optical depth (AOD) observed by the Aerosol Robotic Network (AERONET) project at four representative Chinese sites in January 2015, with temporal correlation coefficients of 0.56 to 0.85. The magnitudes and spatial distributions of the simulated liquid cloud effective radii, liquid cloud optical depths, surface downward shortwave radiation, and surface temperature over China for July 2016 are in good agreement with aircraft, satellite, and surface observations. WRF-GC simulations including both ARI and ACI reproduce the observed surface concentrations and spatial distributions of PM2.5 in January 2015 (normalized mean bias = −6.6 %, spatial correlation r = 0.74) and afternoon ozone in July 2016 (normalized mean bias = 19 %, spatial correlation r = 0.56) over Eastern China, respectively. Our sensitivity simulations show that including the ARI and ACI improved the model's performance in simulating ozone concentrations over China in July, 2016. WRF-GC v2.0 is open source and freely available from http://wrf.geos-chem.org.

scholarly journals Evaluation of the LOTOS-EUROS NO<sub>2</sub> simulations using ground-based measurements and S5P/TROPOMI observations over Greece

2020 ◽  
Author(s):  
Ioanna Skoulidou ◽  
Maria-Elissavet Koukouli ◽  
Astrid Manders ◽  
Arjo Segers ◽  
Dimitris Karagkiozidis ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Extreme Values ◽  
Transport Model ◽  
Monitoring Network ◽  
Temporal Correlation ◽  
Chemical Transport ◽  
Pollution Monitoring ◽  
Chemical Transport Model ◽  
Air Pollution Monitoring ◽  
Tropospheric No2

Abstract. The evaluation of chemical transport models, CTMs, is essential for the assessment of their performance regarding the physical and chemical parameterizations used. While regional CTMs have been widely used and evaluated over Europe, their validation over Greece is limited. In this study, we investigate the performance of the LOTOS-EUROS v2.2.001 regional chemical transport model in simulating nitrogen dioxide, NO2, over Greece from June to December 2018. In-situ NO2 measurements obtained from the National Air Pollution Monitoring Network are compared with surface simulations over the two major cities of Greece, Athens and Thessaloniki. The model reproduces well the spatial variability of the measured NO2 with a spatial correlation coefficient of 0.85 for the period between June and December 2018. About half of the 14 air quality monitoring stations show a good temporal correlation to the simulations, higher than 0.6, during daytime (12–15 p.m. local time), while the corresponding biases are negative. Most stations show stronger negative biases during winter than in summer. Furthermore, the simulated tropospheric NO2 columns are evaluated against ground-based MAX-DOAS NO2 measurements and space-borne Sentinel 5-Precursor TROPOMI tropospheric NO2 observations in July and December 2018. LOTOS-EUROS captures better the NO2 temporal variability in December (0.61 and 0.81) than in July (0.50 and 0.21) when compared to the corresponding measurements of the MAX-DOAS instruments in Thessaloniki and the rural azimuth viewing direction in Athens respectively. The urban azimuth viewing direction in Athens region however shows a better correlation in July than in December (0.41 and 0.19, respectively). LOTOS-EUROS NO2 columns over Athens and Thessaloniki agree well with the TROPOMI observations showing higher spatial correlation in July (0.95 and 0.82, respectively) than in December (0.82 and 0.66, respectively) while the relative temporal correlations are higher during winter. Overall, the comparison of the simulations with the TROPOMI observations shows a model underestimation in summer and an overestimation in winter both in Athens and Thessaloniki. Updated emissions for the simulations and model improvements when extreme values of boundary layer height are encountered are further suggested.

scholarly journals The Influencing Effects of Industrial Eco-Efficiency on Carbon Emissions in the Yangtze River Delta

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8169
Author(s):  
Zaijun Li ◽  
Xiang Zheng ◽  
Dongqi Sun
Keyword(s):  
Spatial Correlation ◽  
Carbon Emissions ◽  
Yangtze River ◽  
Temporal Correlation ◽  
Yangtze River Delta ◽  
River Delta ◽  
Direct And Indirect Effects ◽  
The Yangtze River ◽  
Spatio Temporal ◽  
The Yangtze River Delta

A low-carbon economy is the most important requirement to realize high-quality integrated development of the Yangtze River Delta. Utilizing the following models: a super-efficiency slacks-based measure model, a spatio-temporal correlation model, a bivariate LISA model, a spatial econometric model, and a geographically weighted random forest model, this study measured urban industrial eco-efficiency (IEE) and then analyzed its influencing effects on carbon emission in the Yangtze River Delta from 2000 to 2017. The influencing factors included spatio-temporal correlation intensity, spatio-temporal association type, direct and indirect impacts, and local importance impacts. Findings showed that: (1) The temporal correlation intensity between IEE and scale efficiency (SE) and carbon emissions exhibited an inverted V-shaped variation trend, while the temporal correlation intensity between pure technical efficiency (PTE) and carbon emissions exhibited a W-shaped fluctuation trend. The negative spatial correlation between IEE and carbon emissions was mainly distributed in the developed cities of the delta, while the positive correlation was mainly distributed in central Anhui Province and Yancheng and Taizhou cities. The spatial correlation between PTE and carbon emissions exhibited a spatial pattern of being higher in the central part of the delta and lower in the northern and southern parts. The negative spatial correlation between SE and carbon emissions was mainly clustered in Zhejiang Province and scattered in Jiangsu and Anhui provinces, with the cities with positive correlations being concentrated around two locations: the junction of Anhui and Jiangsu provinces, and within central Jiangsu Province. (2) The direct and indirect effects of IEE on carbon emissions were significantly negative, indicating that IEE contributed to reducing carbon emissions. The direct impact of PTE on carbon emissions was also significantly negative, while its indirect effect was insignificant. Both the direct and indirect effects of SE on carbon emissions were significantly negative. (3) It was found that the positive effect of IEE was more likely to alleviate the increase in carbon emissions in northern Anhui City. Further, PTE was more conducive to reducing the increase in carbon emissions in northwestern Anhui City, southern Zhejiang City, and in other cities including Changzhou and Wuxi. Finally, it was found that SE played a relatively important role in reducing the increase in carbon emissions only in four cities: Changzhou, Suqian, Lu’an, and Wenzhou.

Bootstrapping shear-wave splitting errors

1994 ◽  
Vol 84 (6) ◽  
pp. 1971-1977 ◽  
Author(s):  
Eric Sandvol ◽  
Thomas Hearn
Keyword(s):  
Shear Wave ◽  
Error Estimation ◽  
Bootstrap Method ◽  
Estimation Method ◽  
Simulated Data ◽  
Shear Wave Splitting ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Correlated Errors ◽  
Wave Splitting

Abstract We have developed a bootstrap method to estimate errors associated with inverting SKS waveforms for shear-wave splitting parameters. Although presented for shear-wave splitting inversions, this method is suitable for any waveform inversion procedure. The bootstrap error estimation method consists of multiple inversions of simulated data that imitate the original data with differing noise sequences. The results of the bootstrap inversions are used to directly calculate variances and covariances for all model parameters. We employ a bootstrap error estimation technique to nonlinear inversion for shear-wave splitting parameters. Since seismic data have correlated errors, the bootstrap method was modified for stationary bandlimited time series. This modified bootstrap method was applied to shear-wave splitting measurements from over 60 pairs of horizontal seismograms. The method is stable under a large range of noise conditions. By using this bootstrap method, we can distinguish among data with no apparent splitting, data with splitting, and noisy data.

scholarly journals A Space-Time Correlation Model for MRC Receivers in Rayleigh Fading Channels

Technologies ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 41
Author(s):  
Ramiro Sámano-Robles
Keyword(s):  
Frequency Domain ◽  
Fading Channels ◽  
Rayleigh Fading ◽  
Temporal Correlation ◽  
Time Correlation ◽  
Spatial Diversity ◽  
Space Time ◽  
Rayleigh Fading Channels ◽  
Space Correlation ◽  
Temporal Diversity

This paper presents a statistical model for maximum ratio combining (MRC) receivers in Rayleigh fading channels enabled with a temporal combining process. This means that the receiver effectively combines spatial and temporal branch components. Therefore, the signals that will be processed by the MRC receiver are collected not only across different antennas (space), but also at different instants of time. This suggests the use of a retransmission, repetition or space-time coding algorithm that forces the receiver to store signals in memory at different instants of time. Eventually, these stored signals are combined after a predefined or dynamically optimized number of time-slots or retransmissions. The model includes temporal correlation features in addition to the space correlation between the signals of the different components or branches of the MRC receiver. The derivation uses a frequency domain approach (using the characteristic function of the random variables) to obtain closed-form expressions of the statistics of the post-processing signal-to-noise ratio (SNR) under the assumption of equivalent correlation in time and equivalent correlation in space. The described methodology paves the way for the reformulation of other statistical functions as a frequency-domain polynomial root analysis problem. This is opposed to the infinite series approach that is used in the conventional methodology using directly the probability density function (PDF). The results suggest that temporal diversity is a good complement to receivers with limited spatial diversity capabilities. It is also shown that this additional operation could be maximized when the temporal diversity is adaptive (i.e., activated by thresholds of SNR), thus leading to a better resource utilization.

scholarly journals A Time Series Model Comparison for Monitoring and Forecasting Water Quality Variables

Hydrology ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 37 ◽  
Author(s):  
Magda Monteiro ◽  
Marco Costa
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Regression Model ◽  
Model Comparison ◽  
State Space Model ◽  
Time Correlation ◽  
Quality Parameters ◽  
Calibration Model ◽  
Correlated Errors ◽  
Water Quality Variables

The monitoring and prediction of water quality parameters are important tasks in the management of water resources. In this work, the performances of time series statistical models were evaluated to predict and forecast the dissolved oxygen (DO) concentration in several monitoring sites located along the main river Vouga, in Portugal, during the period from January 2002 to May 2015. The models being compared are a regression model with correlated errors and a state-space model, which can be seen as a calibration model. Both models allow the incorporation of water quality variables, such as time correlation or seasonality. Results show that, for the DO variable, the calibration model outperforms the regression model for sample modeling, that is, for a short-term forecast, while the regression model with correlated errors has a better performance for the forecasting h-steps ahead framework. So, the calibration model is more useful for water monitoring using an online or real-time procedure, while the regression model with correlated errors can be applied in order to forecast over a longer period of time.

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