Effect of Temporal Correlation in the Presence of Spatial Correlation on Interference Suppression

2010 ◽  
Author(s):  
Rizwan Ghaffar ◽  
Raymond Knopp
Keyword(s):  
Spatial Correlation ◽  
Interference Suppression ◽  
Temporal Correlation

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.

scholarly journals Research of MDCOP mining based on time aggregated graph for large spatio-temproal data sets

2019 ◽  
Vol 16 (3) ◽  
pp. 891-914 ◽  
Author(s):  
Zhanquan Wang ◽  
Taoli Han ◽  
Huiqun Yu
Keyword(s):  
Network Security ◽  
Spatial Correlation ◽  
Denial Of Service ◽  
Temporal Correlation ◽  
Data Sets ◽  
Event Data ◽  
Computationally Efficient ◽  
Long Time ◽  
Feature Information ◽  
Occurrence Patterns

Discovering mixed-drove spatiotemporal co-occurrence patterns (MDCOPs) is important for network security such as distributed denial of service (DDoS) attack. There are usually many features when we are suffering from a DDoS attacks such as the server CPU is heavily occupied for a long time, bandwidth is hoovered and so on. In distributed cooperative intrusion, the feature information from multiple intrusion detection sources should be analyzed simultaneously to find the spatial correlation among the feature information. In addition to spatial correlation, intrusion also has temporal correlation. Some invasions are gradually penetrating, and attacks are the result of cumulative effects over a period of time. So it is necessary to discover mixed-drove spatiotemporal co-occurrence patterns (MDCOPs) in network security. However, it is difficult to mine MDCOPs from large attack event data sets because mining MDCOPs is computationally very expensive. In information security, the set of candidate co-occurrence attack event data sets is exponential in the number of object-types and the spatiotemporal data sets are too large to be managed in memory. To reduce the number of candidate co-occurrence instances, we present a computationally efficient MDCOP Graph Miner algorithm by using Time Aggregated Graph. which can deal with large attack event data sets by means of file index. The correctness, completeness and efficiency of the proposed methods are analyzed.

A Spatial-Temporal Correlation Based Novel Clustering Algorithm for Wireless Sensor Networks

2013 ◽  
Vol 655-657 ◽  
pp. 660-664
Author(s):  
Lei Chun Wang ◽  
Guo Yu Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Spatial Correlation ◽  
Data Aggregation ◽  
Clustering Algorithm ◽  
Cluster Formation ◽  
Temporal Correlation ◽  
Wireless Sensor ◽  
Quality Of Data ◽  
Cluster Heads

Considering the requirements of energy saving and data aggregation, and the characteristics of spatial-correlation and temporal-correlation in wireless sensor networks (WSN), this paper proposes a spatial-temporal correlation based novel clustering algorithm, STCBCA. In cluster formation phase, STCBCA clusters nodes in the network according to the spatial correlation between nodes and cluster heads. In data gathering phase, STCBCA clusters data sampled by nodes via the criterion of data cluster, and then sends them to the cluster heads. Our results show that STCBCA can reduce data traffic, prolong network lifetime and improve quality of data aggregation against the clustering algorithm DCAM.

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 Improved Error Concealment Technique Based on Multi-View Video Coding

2014 ◽  
Vol 599-601 ◽  
pp. 1383-1386
Author(s):  
Hai Bo Liu ◽  
Xiao Sheng Huang
Keyword(s):  
Image Reconstruction ◽  
Video Coding ◽  
Spatial Correlation ◽  
Error Concealment ◽  
Motion Vector ◽  
Temporal Correlation ◽  
Experimental Results ◽  
Matching Algorithm ◽  
Video Images ◽  
Disparity Vector

In this paper, we propose a improved error concealment technique based on multi-view video coding to recover damaged video images. At first,It uses BMA(Boundary Matching Algorithm) method to recover the lost or erroneously received motion vector or disparity vector,then combining inter-view correlation, temporal correlation and spatial correlation to recover the lost blocks. The JM12.0 model of H.264 standard is used to evaluate the algorithm. And the experimental results show that our algorithm achieved a better image reconstruction.

scholarly journals Observations and regional modeling of aerosol optical properties, speciation and size distribution over Northern Africa and western Europe

2016 ◽  
Vol 16 (20) ◽  
pp. 12961-12982 ◽  
Author(s):  
Laurent Menut ◽  
Guillaume Siour ◽  
Sylvain Mailler ◽  
Florian Couvidat ◽  
Bertrand Bessagnet
Keyword(s):  
Size Distribution ◽  
Spatial Correlation ◽  
Western Europe ◽  
Fine Particles ◽  
Mineral Dust ◽  
Temporal Correlation ◽  
Aerosol Size Distribution ◽  
Relative Mass ◽  
Aerosol Composition ◽  
Inorganic Species

Abstract. The aerosol speciation and size distribution is modeled during the summer 2013 and over a large area encompassing Africa, Mediterranean and western Europe. The modeled aerosol is compared to available measurements such as the AERONET aerosol optical depth (AOD) and aerosol size distribution (ASD) and the EMEP network for surface concentrations of particulate matter PM2.5, PM10 and inorganic species (nitrate, sulfate and ammonium). The main goal of this study is to quantify the model ability to realistically model the speciation and size distribution of the aerosol. Results first showed that the long-range transport pathways are well reproduced and mainly constituted by mineral dust: spatial correlation is  ≈  0.9 for AOD and Ångström exponent, when temporal correlations show that the day-to-day variability is more difficult to reproduce. Over Europe, PM2.5 and PM10 have a mean temporal correlation of  ≈  0.4 but the lowest spatial correlation ( ≈  0.25 and 0.62, respectively), showing that the fine particles are not well localized or transported. Being short-lived species, the uncertainties on meteorology and emissions induce these lowest scores. However, time series of PM2.5 with the speciation show a good agreement between model and measurements and are useful for discriminating the aerosol composition. Using a classification from the south (Africa) to the north (northern Europe), it is shown that mineral dust relative mass contribution decreases from 50 to 10 % when nitrate increases from 0 to 20 % and all other species, sulfate, sea salt, ammonium, elemental carbon, primary organic matter, are constant. The secondary organic aerosol contribution is between 10 and 20 % with a maximum at the latitude of the Mediterranean Sea (Spanish stations). For inorganic species, it is shown that nitrate, sulfate and ammonium have a mean temporal correlation of 0.25, 0.37 and 0.17, respectively. The spatial correlation is better (0.25, 0.5 and 0.87), showing that the mean values may be biased but the spatial localization of sulfate and ammonium is well reproduced. The size distribution is compared to the AERONET product and it is shown that the model fairly reproduces the main values for the fine and coarse mode. In particular, for the fine mode, the model overestimates the aerosol mass in Africa and underestimates it in Europe.

scholarly journals Observations and regional modeling of aerosol speciation and size distribution over Africa and Europe

2016 ◽  
Author(s):  
Laurent Menut ◽  
Guillaume Siour ◽  
Sylvain Mailler ◽  
Florian Couvidat ◽  
Bertrand Bessagnet
Keyword(s):  
Size Distribution ◽  
Spatial Correlation ◽  
Western Europe ◽  
Mineral Dust ◽  
Temporal Correlation ◽  
Large Area ◽  
Aerosol Composition ◽  
Transport Pathways ◽  
Mean Values ◽  
Inorganic Species

Abstract. The aerosol speciation and size distribution is modelled during the summer 2013 and over a large area encompassing Africa, Mediterranean and western Europe. The modelled aerosol is compared to available measurements such as the AERONET Aerosol Optical depth (AOD) and Inversion Size Distribution (ASD) and the EMEP network for surface concentrations of PM2.5, PM10 and inorganic species (nitrate, sulfate and ammonium). The main goal of this study is to quantify the model ability to realistically model the speciation and size distribution of the aerosol. Results first showed that the long-range transport pathways is well reproduced and mainly constituted by mineral dust: spatial correlation is &amp;approx; 0.9 for AOD and Angstrom, when temporal correlation show that the day to day variability is more difficult to reproduce. Over Europe, the PM2.5 and PM10 have a mean temporal correlation of &amp;approx; 0.4, but a lowest spatial correlation (&amp;approx; 0.25 and 0.62, respectively), showing that the fine particules are not well localized or transported. Being short-lived species, the uncertainties on meteorology and emissions conduct to these lowest scores. However, time series of PM2.5 with the speciation show a good agreement between model and measurements, and are useful to discriminate the aerosol composition. Using a classification from the south (Africa) to the North (northern Europe), it is shown that mineral dust relative contributions decreases from 50 % to 10 %, when nitrate increases from 0 % to 20 %, all other species species, sulfate, sea salt, ammonium, elemental carbon, primary organic matter, are constant. The secondary organic aerosol contribution is between 10 % and 20 % with a maximum at the latitude of the Mediterranean sea (Spanish stations). For inorganic species, it is shown that nitrate, sulfate and ammonium have a mean temporal correlation of 0.25, 0.37 and 0.17, respectively. The spatial correlation is better (0.25, 0.5 and 0.87) showing that the mean values may be biased but the spatial localization of sulfate and ammonium is well reproduced. The size distribution is compared to the AERONET product and it is shown that the model is able to reproduce the main values for the fine and coarse mode. More in detail, for the fine mode, the model overestimates the aerosol mass in Africa and underestimates in Europe.

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