An automatic statistical methodology to extract pulse-like forcing factors in climatic time series: Application to volcanic events

2003 ◽  
pp. 177-186 ◽  
Author(s):  
Philippe Naveau ◽  
Caspar M. Ammann ◽  
Hee-Seok Oh ◽  
Wensheng Guo
Keyword(s):  
Time Series ◽  
Statistical Methodology ◽  
Volcanic Events ◽  
Climatic Time Series

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

scholarly journals A 20-Year Hydroacoustic Time Series of Seismic and Volcanic Events in the Northeast Pacific Ocean

Oceanography ◽  
2011 ◽  
Vol 24 (3) ◽  
pp. 280-293 ◽  
Author(s):  
Robert Dziak ◽  
Stephen Hammond ◽  
Christopher Fox
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Northeast Pacific ◽  
Volcanic Events ◽  
Northeast Pacific Ocean

scholarly journals Revisiting Climate Region Definitions via Clustering

2009 ◽  
Vol 22 (7) ◽  
pp. 1787-1800 ◽  
Author(s):  
Robert Lund ◽  
Bo Li
Keyword(s):  
Time Series ◽  
Clustering Methods ◽  
Distance Metric ◽  
Climatic Zones ◽  
Time Series Clustering ◽  
Climate Region ◽  
Covariate Effects ◽  
Weather Stations ◽  
Climatic Time Series ◽  
Natural Way

Abstract This paper introduces a new distance metric that enables the clustering of general climatic time series. Clustering methods have been frequently used to partition a domain of interest into distinct climatic zones. However, previous techniques have neglected the time series (autocorrelation) component and have also handled seasonal features in a suboptimal way. The distance proposed here incorporates the seasonal mean and autocorrelation structures of the series in a natural way; moreover, trends and covariate effects can be considered. As an important by-product, the methods can be used to statistically assess whether two stations can serve as reference stations for one another. The methods are illustrated by partitioning 292 weather stations within the state of Colorado into six different zones.

Chaos and periodicities in a climatic time series of the Iberian Margin

2020 ◽  
Vol 30 (6) ◽  
pp. 063126
Author(s):  
Berenice Rojo-Garibaldi ◽  
David Alberto Salas-de-León ◽  
María Adela Monreal-Gómez ◽  
Simone Giannerini ◽  
Julyan H. E. Cartwright
Keyword(s):  
Time Series ◽  
Climatic Time Series ◽  
Iberian Margin

Disaggregation Modeling Process for Climatic Time Series

1996 ◽  
Vol 122 (3) ◽  
pp. 205-212
Author(s):  
Susan E. Firor ◽  
Brad A. Finney ◽  
Robert Willis ◽  
John A. Dracup
Keyword(s):  
Time Series ◽  
Modeling Process ◽  
Climatic Time Series

Recent advances in statistical methodology applied to the Hjort liver index time series (1859–2012) and associated influential factors

2016 ◽  
Vol 73 (2) ◽  
pp. 279-295 ◽  
Author(s):  
Gudmund H. Hermansen ◽  
Nils Lid Hjort ◽  
Olav S. Kjesbu
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Length Distribution ◽  
Detailed Examination ◽  
Influential Factors ◽  
Statistical Methodology ◽  
Related Factors ◽  
Recent Advances ◽  
Winter Temperatures ◽  
Index Time Series

Certain recent advances in statistical methodology have promising potential for fruitful use in general biology and the fisheries sciences. This paper reviews and discusses some of the relevant themes, including accurate modelling via focused model selection techniques, dynamic goodness-of-fit testing of processes evolving over time, finding break points for phenomena experiencing regime shifts, prediction uncertainty, and optimal combination of information across diverse sources via confidence distributions. The methods are illustrated for the Hjort liver quality index time series. Its roots lie in the classic study by Hjort in 1914, where liver quality of the Northeast Arctic cod (Gadus morhua) for 1880–1912 was reported on and studied, along with related factors, making it one of the first teleost time series ever published. The series has been extended both backwards and forwards in time, to 1859–2012, due to comprehensive archival and calibration efforts of Kjesbu et al. in 2014, yielding one of the longest time series of marine science. Our study offers a detailed examination of this series and how it relates to and interacts with associated factors, including winter temperatures, length distribution parameters, cod mortality, and a certain index related to availability of food. We identify certain mild nonstationary aspects of the time series, show that there is a regime shift around 1990 in the ways the liver series interacts with winter temperatures, and demonstrate that mortality and food availability play important roles.

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