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

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 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.

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

scholarly journals Estimates of reference evapotranspiration in the municipality of Ariquemes (RO) using neural networks GMDH-type

2019 ◽  
Vol 23 (5) ◽  
pp. 324-329
Author(s):  
Roberto L. da S. Carvalho ◽  
Angel R. S. Delgado
Keyword(s):  
Time Series ◽  
Reference Evapotranspiration ◽  
Moving Average ◽  
Group Method ◽  
Data Handling ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Irrigation Methods ◽  
Artificial Neural Network Ann ◽  
Climatic Time Series

ABSTRACT Reference evapotranspiration is a climatological variable of great importance for water use dimensioning in irrigation methods. In order to contribute to the climatic understanding of Ariquemes, Rodônia state, Brazil, the study aims to model the behavior of the time series of reference evapotranspiration using a GMDH-type (Group Method of Data Handling) artificial neural network (ANN) and to compare it with the SARIMA (Seasonal Autoregressive Integrated Moving Average) methodology. Data from the National Institute of Meteorology - INMET, obtained at the Automatic Weather Station of Ariquemes, from January 2011 to January 2014, were used. Data analysis was performed using software R version 3.3.1 through the GMDH-type ANN package. Modeling by GMDH-type ANN led to results similar to the results of the SARIMA model, thus constituting an option to predict climatic time series. GMDH-type models with larger numbers of inputs and layers presented lowest mean square error.

Sign in / Sign up

Export Citation Format

Share Document