A principal component analysis based model to predict post-monsoon tropical cyclone activity in the Bay of Bengal using oceanic Niño index and dipole mode index

2017 ◽  
Vol 38 (5) ◽  
pp. 2415-2422 ◽  
Author(s):  
H. R. Biswas ◽  
P. K. Kundu
Keyword(s):  
Principal Component Analysis ◽  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Principal Component ◽  
Component Analysis ◽  
Tropical Cyclone Activity ◽  
Dipole Mode ◽  
Cyclone Activity ◽  
Dipole Mode Index ◽  
Post Monsoon

Modulation of bay of bengal tropical cyclone activity by the madden-julian oscillation

2019 ◽  
Vol 229 ◽  
pp. 23-38 ◽  
Author(s):  
Pankaj Bhardwaj ◽  
Omvir Singh ◽  
D.R. Pattanaik ◽  
Philip J. Klotzbach
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Tropical Cyclone Activity ◽  
Madden Julian Oscillation ◽  
Cyclone Activity

Understanding differences in tropical cyclone activity over the Arabian Sea and Bay of Bengal

MAUSAM ◽  
2021 ◽  
Vol 72 (1) ◽  
pp. 187-198
Author(s):  
ADAM H. SOBEL ◽  
IRIS C. LIU ◽  
SUZANA J. CAMARGO
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Arabian Sea ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity

Impacts of ENSO and IOD on tropical cyclone activity in the Bay of Bengal

2014 ◽  
Vol 75 (2) ◽  
pp. 1105-1125 ◽  
Author(s):  
Biranchi Kumar Mahala ◽  
Birendra Kumar Nayak ◽  
Pratap Kumar Mohanty
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity

scholarly journals A meridional dipole in premonsoon Bay of Bengal tropical cyclone activity induced by ENSO

2016 ◽  
Vol 121 (12) ◽  
pp. 6954-6968 ◽  
Author(s):  
Karthik Balaguru ◽  
L. Ruby Leung ◽  
Jian Lu ◽  
Gregory R. Foltz
Keyword(s):  
Tropical Cyclone ◽  
Bay Of Bengal ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity

scholarly journals Study of Seasonal Variation in Groundwater Quality of Sagar City (India) by Principal Component Analysis

2011 ◽  
Vol 8 (4) ◽  
pp. 2000-2009 ◽  
Author(s):  
Hemant Pathak ◽  
S. N. Limaye
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Load ◽  
Sampling Station ◽  
Entire Study ◽  
Post Monsoon

Groundwater is one of the major resources of the drinking water in Sagar city (India.). In this study 15 sampling station were selected for the investigations on 14 chemical parameters. The work was carried out during different months of the pre-monsoon, monsoon and post-monsoon seasons in June 2009 to June 2010. The multivariate statistics such as principal component and cluster analysis were applied to the datasets to investigate seasonal variations in groundwater quality. Principal axis factoring has been used to observe the mode of association of parameters and their interrelationships, for evaluating water quality. Average value of BOD, COD, ammonia and iron was high during entire study period. Elevated values of BOD and ammonia in monsoon, slightly more value of BOD in post-monsoon, BOD, ammonia and iron in pre-monsoon period reflected contribution on temporal effect on groundwater. Results of principal component analysis evinced that all the parameters equally and significantly contribute to groundwater quality variations. Factor 1 and factor 2 analysis revealed the DO value deteriorate due to organic load (BOD/Ammonia) in different seasons. Hierarchical cluster analysis grouped 15 stations into four clusters in monsoon, five clusters in post-monsoon and five clusters in pre-monsoon with similar water quality features. Clustered group at monsoon, post-monsoon and pre-monsoon consisted one station exhibiting significant spatial variation in physicochemical composition. The anthropogenic nitrogenous species, as fallout from modernization activities. The study indicated that the groundwater sufficiently well oxygenated and nutrient-rich in study places.

scholarly journals Global Stochastic Tropical Cyclone Model Based on Principal Component Analysis and Cluster Analysis

2014 ◽  
Vol 53 (6) ◽  
pp. 1547-1577 ◽  
Author(s):  
Sota Nakajo ◽  
Nobuhito Mori ◽  
Tomohiro Yasuda ◽  
Hajime Mase
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Tropical Cyclone ◽  
Observational Data ◽  
Principal Component ◽  
Component Analysis ◽  
Distribution Functions ◽  
Joint Probability Distribution ◽  
Northwestern Pacific ◽  
And Cluster Analysis

AbstractA global stochastic tropical cyclone model was developed as a means for preparing a large number of artificial tropical cyclone (TC) samples with different values for parameters such as track, minimum sea level pressure, and translation speed. In this paper, the model and the results of its verification are presented in detail. The proposed stochastic model is sensitive to approximations of the joint probability distribution functions (PDFs) of TC parameters and temporal correlations. A newly introduced accurate method for approximating joint PDFs by using principal component analysis and cluster analysis resulted in improved reproducibility of TC parameters. The simulation results were compared with historical observational data from the northwestern Pacific, southwestern Pacific, and North Atlantic Oceans. The grid-averaged mean values and distribution patterns of PDFs of TC parameters were in agreement with observational data.

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