Interdecadal variations of different types of summer heatwaves in Northeast China associated with AMO and PDO

2021 ◽  
pp. 1-46
Author(s):  
Xiaoye Yang ◽  
Gang Zeng ◽  
Guwei Zhang ◽  
Jingwei Li ◽  
Zhongxian Li ◽  
...  
Keyword(s):  
Northeast China ◽  
Clustering Algorithm ◽  
Wave Train ◽  
Atlantic Multidecadal Oscillation ◽  
Interdecadal Variation ◽  
Positive Temperature ◽  
Community Atmosphere Model ◽  
Silk Road Pattern ◽  
Wave Trains ◽  
Hierarchical Clustering Algorithm

AbstractThe summer heatwaves (HWs) in Northeast China (NEC) during 1961-2016 can be classified into two types, namely wave-train HWs and blocking HWs based on the hierarchical clustering algorithm by using ERA-Interim daily reanalysis datasets. Wave-train HWs occurred accompanied by eastward-moving wave trains with a "-+-+" structure formed over Eurasia, while the blocking HWs occurred with blocking circulation anomalies over Eurasia. In general, the blocking HWs could cause the positive temperature anomalies in NEC to last longer than wave-train HWs. During the period from 1961 to 2016, the wave-train HWs experienced an interdecadal variation from less to more, while the blocking HWs experienced interdecadal variations of less-more-less. Regression analysis and information flow indicate that the interdecadal variation of the wave-train HWs is associated with Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), while the interdecadal variation of the blocking HWs is more likely associated with PDO. The positive phase of AMO (negative phase of PDO) could increase the wave-train (blocking) HWs by strengthening the zonal wave-train similar to the Silk Road pattern (the arched wave-train like the polar-Eurasian pattern). The observed results are in agreement with the numerical experiments with the NCAR Community Atmosphere Model version 5.3.

scholarly journals Interdecadal Variations of the Silk Road Pattern

2017 ◽  
Vol 30 (24) ◽  
pp. 9915-9932 ◽  
Author(s):  
Lin Wang ◽  
Peiqiang Xu ◽  
Wen Chen ◽  
Yong Liu
Keyword(s):  
Wave Train ◽  
Atlantic Multidecadal Oscillation ◽  
Teleconnection Pattern ◽  
Silk Road ◽  
Boreal Summer ◽  
Northeastern Asia ◽  
Western Asia ◽  
Silk Road Pattern ◽  
Decadal Climate ◽  
The Silk Road

Based on several reanalysis and observational datasets, this study suggests that the Silk Road pattern (SRP), a major teleconnection pattern stretching across Eurasia in the boreal summer, shows clear interdecadal variations that explain approximately 50% of its total variance. The interdecadal SRP features a strong barotropic wave train along the Asian subtropical jet, resembling its interannual counterpart. Additionally, it features a second weak wave train over the northern part of Eurasia, leading to larger meridional scale than its interannual counterpart. The interdecadal SRP contributes approximately 40% of the summer surface air temperature’s variance with little uncertainty and 10%–20% of the summer precipitation’s variance with greater uncertainty over large domains of Eurasia. The interdecadal SRP shows two regime shifts in 1972 and 1997. The latter shift explains over 40% of the observed rainfall reduction over northeastern Asia and over 40% of the observed warming over eastern Europe, western Asia, and northeastern Asia, highlighting its importance to the recent decadal climate variations over Eurasia. The Atlantic multidecadal oscillation (AMO) does not show a significant linear relationship with the interdecadal SRP. However, the Monte Carlo bootstrapping resampling analysis suggests that the positive (negative) phases of the spring and summer AMO significantly facilitate the occurrence of negative (positive) phases of the interdecadal SRP, implying plausible prediction potentials for the interdecadal variations of the SRP. The reported results are insensitive to the long-term trends in datasets and thereby have little relevance to externally forced climate change.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

scholarly journals Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 370
Author(s):  
Shuangsheng Wu ◽  
Jie Lin ◽  
Zhenyu Zhang ◽  
Yushu Yang
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Research Gaps ◽  
Judicial Practice ◽  
Linguistic Term ◽  
Clustering Effect ◽  
Hierarchical Clustering Algorithm ◽  
Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

scholarly journals Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining

2021 ◽  
Vol 13 (3) ◽  
pp. 1089
Author(s):  
Hailin Zheng ◽  
Qinyou Hu ◽  
Chun Yang ◽  
Jinhai Chen ◽  
Qiang Mei
Keyword(s):  
Clustering Algorithm ◽  
Pattern Mining ◽  
Transportation Networks ◽  
Automatic Identification ◽  
Identification System ◽  
Classification Models ◽  
Transmission Path ◽  
The Hierarchical Structure ◽  
Hierarchical Clustering Algorithm ◽  
And Control

Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.

A bio-inspired hierarchical clustering algorithm with backtracking strategy

2014 ◽  
Vol 42 (2) ◽  
pp. 174-194 ◽  
Author(s):  
Akil Elkamel ◽  
Mariem Gzara ◽  
Hanêne Ben-Abdallah
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Hierarchical Clustering Algorithm
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