Correlation analysis between Korean spring drought and large-scale teleconnection patterns for drought forecasting

2016 ◽  
Vol 21 (1) ◽  
pp. 458-466 ◽  
Author(s):  
Jong-Suk Kim ◽  
Gil-Su Seo ◽  
Ho-Won Jang ◽  
Joo-Heon Lee
Keyword(s):  
Correlation Analysis ◽  
Large Scale ◽  
Drought Forecasting ◽  
Teleconnection Patterns

scholarly journals Remote Sensing of Local Warming Trend in Alberta, Canada during 2001–2020, and Its Relationship with Large-Scale Atmospheric Circulations

2021 ◽  
Vol 13 (17) ◽  
pp. 3441
Author(s):  
Quazi K. Hassan ◽  
Ifeanyi R. Ejiagha ◽  
M. Razu Ahmed ◽  
Anil Gupta ◽  
Elena Rangelova ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Correlation Analysis ◽  
Large Scale ◽  
Kendall Test ◽  
Warming Trend ◽  
Small Magnitude ◽  
Teleconnection Patterns ◽  
Local Warming ◽  
Slope Estimator ◽  
Time Series Images

Here, the objective was to study the local warming trend and its driving factors in the natural subregions of Alberta using a remote-sensing approach. We applied the Mann–Kendall test and Sen’s slope estimator on the day and nighttime MODIS LST time-series images to map and quantify the extent and magnitude of monthly and annual warming trends in the 21 natural subregions of Alberta. We also performed a correlation analysis of LST anomalies (both day and nighttime) of the subregions with the anomalies of the teleconnection patterns, i.e., Pacific North American (PNA), Pacific decadal oscillation (PDO), Arctic oscillation (AO), and sea surface temperature (SST, Niño 3.4 region) indices, to identify the relationship. May was the month that showed the most significant warming trends for both day and night during 2001–2020 in most of the subregions in the Rocky Mountains and Boreal Forest. Subregions of Grassland and Parkland in southern and southeastern parts of Alberta showed trends of cooling during daytime in July and August and a small magnitude of warming in June and August at night. We also found a significant cooling trend in November for both day and night. We identified from the correlation analysis that the PNA pattern had the most influence in the subregions during February to April and October to December for 2001–2020; however, none of the atmospheric oscillations showed any significant relationship with the significant warming/cooling months.

MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS FOR LARGE-SCALE WAREHOUSE-OUT BEHAVIORS

Fractals ◽  
2015 ◽  
Vol 23 (04) ◽  
pp. 1550044 ◽  
Author(s):  
CAN-ZHONG YAO ◽  
JI-NAN LIN ◽  
XU-ZHOU ZHENG
Keyword(s):  
Correlation Analysis ◽  
Large Scale ◽  
Cross Correlation ◽  
Correlation Property ◽  
Long Term Memory ◽  
Correlation Relationship ◽  
Term Memory ◽  
Wire Rod ◽  
Cross Correlation Analysis

Based on cross-correlation algorithm, we analyze the correlation property of warehouse-out quantity of different warehouses, respectively, and different products of each warehouse. Our study identifies that significant cross-correlation relationship for warehouse-out quantity exists among different warehouses and different products of a warehouse. Further, we take multifractal detrended cross-correlation analysis for warehouse-out quantity among different warehouses and different products of a warehouse. The results show that for the warehouse-out behaviors of total amount, different warehouses and different products of a warehouse significantly follow multifractal property. Specifically for each warehouse, the coupling relationships of rebar and wire rod reveal long-term memory characteristics, no matter for large fluctuation or small one. The cross-correlation effect on long-range memory property among warehouses probably has less to do with product types,and the long-term memory of YZ warehouse is greater than others especially in total amount and wire rod product. Finally, we shuffle and surrogate data to explore the source of multifractal cross-correlation property in logistics system. Taking the total amount of warehouse-out quantity as example, we confirm that the fat-tail distribution of warehouse-out quantity sequences is the main factor for multifractal cross-correlation. Through comparing the performance of the multifractal detrended cross-correlation analysis (MF-DCCA), centered multifractal detrending moving average cross-correlation analysis (MF-X-DMA) algorithms, the forward and backward MF-X-DMA algorithms, we find that the forward and backward MF-X-DMA algorithms exhibit a better performance than the other ones.

Statistical Characteristics and Correlation Analysis for Control Index of Soil-Aggregate Mixture

2013 ◽  
Vol 718-720 ◽  
pp. 1872-1877 ◽  
Author(s):  
Xu Xi Chang ◽  
Xie Jian Ming ◽  
Jiang Ling Fa ◽  
Chen Shan Xiong
Keyword(s):  
Grain Size ◽  
Correlation Analysis ◽  
Normal Distribution ◽  
Large Scale ◽  
Soil Aggregate ◽  
Site Preparation ◽  
Statistical Characteristics ◽  
Coarse Grain ◽  
Systematic Research ◽  
Large Variability

Currently, the soil-aggregate mixture has been widely used in some large-scale site preparation projects, compaction characteristics has been pay more attention by many engineers and researchers. However, systematic research is insufficient on how to choose the filler. Moreover, some industry regulations are different on the requirements about filler. This paper relies on a certain big site preparation projects, discussing statistical characteristics and correlation on the maximal grain size, contents of the coarse grain, gradation and other parameters of soil-aggregate mixture. The results show that the maximal and the median grain size have small discreteness and normal distribution, indicating site filler is easy to reach the requirement; The coefficient of curvature, coefficient of nonuniformity and the coarse grain content have large discreteness, and dont obey normal distribution, indicating the filler has large variability. The median grain size is highly relevant to the coarse grain content; the maximal grain size isnt relevant to the coefficient of nonuniformity, the coefficient of curvature and the coarse grain content. According to the results of correlation analysis, we suggest that the importance order follow by coarse grain content, the maximum grain size and gradation for the control parameters of filler. This research may be significant to other similar projects.

scholarly journals Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data

2019 ◽  
Vol 67 (2) ◽  
pp. 306-319 ◽  
Author(s):  
Charilaos I. Kanatsoulis ◽  
Xiao Fu ◽  
Nicholas D. Sidiropoulos ◽  
Mingyi Hong
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals On the Relationship between Teleconnections and Taiwan's Streamflow: Evidence of Climate Regime Shift and Implications for Seasonal Forecasting

2016 ◽  
Author(s):  
Chia-Jeng Chen ◽  
Tsung-Yu Lee
Keyword(s):  
Large Scale ◽  
Critical Role ◽  
Phase Relationship ◽  
Temporal Variations ◽  
Pacific Basin ◽  
Climate Regime ◽  
Quasi Biennial Oscillation ◽  
Teleconnection Patterns ◽  
Integrated Response ◽  
The Relationship

Abstract. Interannual variations of catchment streamflow represent an integrated response to anomalies in regional moisture transport and atmospheric circulations, ultimately linked to large-scale climate oscillations. This study investigates the relationship between Taiwan's long-term summertime (July to September, JAS) streamflow and manifold teleconnection patterns. Lagged correlation analysis is conducted to calculate how JAS streamflow data derived at 28 upstream and 13 downstream gauges in Taiwan correlate with 14 teleconnection indices in the concurrent or preceding seasons. Out of the many indices, the West-Pacific and Pacific-Japan (PJ) patterns, both of which play a critical role in determining cyclonic activity in the western North Pacific basin, exhibit the highest concurrent correlations (most significant r = 0.48) with the JAS flows in Taiwan. At a one-month lead time, on the other hand, the Quasi-Biennial Oscillation significantly correlate with the JAS flows (most significant r = −0.66), indicating some forecasting utility. By further examining the correlation results using a 20-year moving window, peculiar temporal variations and possible climate regime shifts (CRS) can be revealed. To identify suspicious, abrupt changes in the correlation, a CRS test is employed. The late 1970s and 1990s are identified as two significant change points, and during the intermediate period, a marked in-phase relationship (r ~ 0.9) between Taiwan's streamflow and the PJ index is observed. It is verified that the two shifts are in concordance with the alteration of large-scale circulations in the Pacific basin. Discussion about the changes in pattern correlation and composite maps before and after the change point is carried out, and our results suggest that empirical forecasting techniques should take into account the effect of CRS on predictor screening.

Semi-supervised Heterogeneous Defect Prediction with Open-source Projects on GitHub

2021 ◽  
Vol 31 (06) ◽  
pp. 889-916
Author(s):  
Ying Sun ◽  
Xiao-Yuan Jing ◽  
Fei Wu ◽  
Xiwei Dong ◽  
Yanfei Sun ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Open Source ◽  
Large Scale ◽  
Real Life ◽  
Defect Prediction ◽  
Public Project ◽  
Research Attention ◽  
Target Company ◽  
Public Datasets ◽  
Heterogeneous Defect Prediction

The heterogeneous defect prediction (HDP) technique can predict defects in a target company using heterogeneous metric data from external company, which has received substantial research attention. However, existing HDP methods assume that source data is labeled but labeling data is expensive. Semi-supervised defect prediction technique can perform defect prediction with few labeled data. In this paper, we investigate a new problem — semi-supervised HDP (SHDP). To solve this problem, we propose a new approach named cost-sensitive kernel semi-supervised correlation analysis (CKSCA) as a solution of SHDP problem. It introduces unified metric representation and canonical correlation analysis to make the data distributions of different company projects more similar. CKSCA also designs a cost-sensitive kernel semi-supervised discriminant analysis mechanism to utilize the limited labeled data and sufficient real-life unlabeled data from different companies. Besides we collect lots of open-source projects from GitHub website to construct a new large-scale unlabeled dataset called GITHUB dataset. It contains 26,407 modules and is greater than each public project dataset. It has been public online and can be extended continuously. Experiments on the GITHUB dataset and other public datasets indicate that unlabeled GITHUB data can help prediction model improve prediction performance, and CKSCA is effective and efficient for solving SHDP problem.

scholarly journals Large scale, voxel‐to‐voxel correlation analysis of tau positron emission tomography signal to histological pathology in non‐Alzheimer tauopathies

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Yuheng Chen ◽  
Maryana Alegro ◽  
Dulce Ovando Morales ◽  
Renaud Joie ◽  
Anubhav Shankar ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Correlation Analysis ◽  
Large Scale ◽  
Emission Tomography ◽  
Positron Emission

scholarly journals Large scale genotype phenotype correlation analysis based on phylogenetic trees

2007 ◽  
Vol 23 (7) ◽  
pp. 785-788 ◽  
Author(s):  
F. Habib ◽  
A. D. Johnson ◽  
R. Bundschuh ◽  
D. Janies
Keyword(s):  
Correlation Analysis ◽  
Phylogenetic Trees ◽  
Large Scale ◽  
Phenotype Correlation ◽  
Genotype Phenotype Correlation

scholarly journals Impact of the North Atlantic Oscillation on Transatlantic Flight Routes and Clear-Air Turbulence

2016 ◽  
Vol 55 (3) ◽  
pp. 763-771 ◽  
Author(s):  
Jung-Hoon Kim ◽  
William N. Chan ◽  
Banavar Sridhar ◽  
Robert D. Sharman ◽  
Paul D. Williams ◽  
...  
Keyword(s):  
New York ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
Large Scale ◽  
Jet Streams ◽  
Weather Patterns ◽  
Teleconnection Patterns ◽  
The North ◽  
Air Turbulence ◽  
The North Atlantic Oscillation

AbstractThe variation of wind-optimal transatlantic flight routes and their turbulence potential is investigated to understand how upper-level winds and large-scale flow patterns can affect the efficiency and safety of long-haul flights. In this study, the wind-optimal routes (WORs) that minimize the total flight time by considering wind variations are modeled for flights between John F. Kennedy International Airport (JFK) in New York, New York, and Heathrow Airport (LHR) in London, United Kingdom, during two distinct winter periods of abnormally high and low phases of North Atlantic Oscillation (NAO) teleconnection patterns. Eastbound WORs approximate the JFK–LHR great circle (GC) route following northerly shifted jets in the +NAO period. Those WORs deviate southward following southerly shifted jets during the −NAO period, because eastbound WORs fly closely to the prevailing westerly jets to maximize tailwinds. Westbound WORs, however, spread meridionally to avoid the jets near the GC in the +NAO period to minimize headwinds. In the −NAO period, westbound WORs are north of the GC because of the southerly shifted jets. Consequently, eastbound WORs are faster but have higher probabilities of encountering clear-air turbulence than westbound ones, because eastbound WORs are close to the jet streams, especially near the cyclonic shear side of the jets in the northern (southern) part of the GC in the +NAO (−NAO) period. This study suggests how predicted teleconnection weather patterns can be used for long-haul strategic flight planning, ultimately contributing to minimizing aviation’s impact on the environment.

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