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Introducing libeemd: a program package for performing the ensemble empirical mode decomposition

P. J. J. Luukko, J. Helske, E. Räsänen
2015 Computational statistics (Zeitschrift)  
The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD).  ...  We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the decomposition.  ...  Acknowledgements This work was supported by the Finnish Cultural Foundation, the Emil Aaltonen Foundation, the Academy of Finland, and the European Community's FP7 through the CRONOS project, Grant Agreement  ... 
doi:10.1007/s00180-015-0603-9 fatcat:gsls3ywvdnf7ldcvdm5g4ahuim

Ocean reanalysis data‐driven deep learning forecast for sea surface multivariate in the South China Sea

Qi Shao, Guangchao Hou, Wei Li, Guijun Han, Kangzhuang Liang, Yang Bai
2021 Earth and Space Science  
The authors would like to thank the following data and tool providers: National Marine Data and Information Service (http://www.cmoc-china.cn) for providing the daily CORA data, Google for providing the  ...  This research is cosponsored by grants from the National Key Research and Development Program (2016YFC1401800), the National Natural Science Foundation of China (41876014) and the Open Project of Tianjin  ...  (Wu et al., 2019) used complete ensemble empirical mode decomposition (CEEMD) and ANN to perform the prediction of SST.  ... 
doi:10.1029/2020ea001558 fatcat:eleegwszdvccnmu2x6opiie7by

Evaluation of decomposition tools for sea floor pressure data

Matthias Joachim Ehrhardt, H. Villinger, S. Schiffler
2012 Computers & Geosciences  
In this paper we investigate the novel approaches Empircial Mode Decomposition and Sparse Decomposition for long-term sea floor pressure data analysis and compare them with the classical ones.  ...  Our results indicate that none of the methods fulfils all the requirements but Sparse Decomposition performed best except for computing efficiency.  ...  Acknowledgements We would like to thank Hans-Hermann Gennerich and Earl Davis for providing the sea floor pressure data and Peter Maass for supporting one of us (ME) during this study.  ... 
doi:10.1016/j.cageo.2012.03.022 fatcat:z6fhjtiokncp7ptyw7k3v3n6sq

The Influence of Autumn Eurasian Snow Cover on the Atmospheric Dynamics Anomalies during the Next Winter in INMCM5 Model Data

Maria A. Tarasevich, Evgeny M. Volodin
2021 Supercomputing Frontiers and Innovations  
The influence of autumn Eurasian snow cover on the atmospheric dynamics anomalies during the following winter is studied based on the INM RAS climate model data.  ...  The North Atlantic Oscillation is the leading pattern that causes the weather and climate variability in the Northern hemisphere.  ...  Acknowledgements The research was supported by the Russian Science Foundation, project No. 20-17-00190 (analysis of the piControl and historical runs) and by the Russian Foundation for Basic Research,  ... 
doi:10.14529/jsfi210403 dblp:journals/superfri/TarasevichV21 fatcat:vakobcncwrco5m32c4ywhlly4y

D7.2.1: Interim report on collaboration with communities

Giovanni Erbacci
2011 Zenodo  
the EC-Earth 3 suite for Meteo-climatology, SPECFEM3D for Earth Sciences (earthquakes) and OpenFOAM and Code_Saturne for Engineering and CFD.  ...  For five of these applications (GROMACS, Quantum Espresso, GPAW, EC-Earth 3 and OpenFOAM) the enabling work started in December 2010 and some preliminary results are presented.  ...  OASIS Ensemble Mode, OASIS handles separate ensemble members in a similar way to the pseudo-parallel mode.  ... 
doi:10.5281/zenodo.6552913 fatcat:ciibhwsuybe7hjfyaiwsjz7s5u

Earth system data cubes unravel global multivariate dynamics

Miguel D. Mahecha, Fabian Gans, Gunnar Brandt, Rune Christiansen, Sarah E. Cornell, Normann Fomferra, Guido Kraemer, Jonas Peters, Paul Bodesheim, Gustau Camps-Valls, Jonathan F. Donges, Wouter Dorigo (+8 others)
2020 Earth System Dynamics  
In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary  ...  Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using  ...  Data streams and implementation The concept as described in Sect. 2 is generic, i.e. independent of the implemented Earth system data cube and of the technical solution of the implementation.  ... 
doi:10.5194/esd-11-201-2020 fatcat:zlkp5tg5f5gohcy3ytukej2qgu

DasPy 1.0 – the Open Source Multivariate Land Data Assimilation Framework in combination with the Community Land Model 4.5

X. Han, X. Li, G. He, P. Kumbhar, C. Montzka, S. Kollet, T. Miyoshi, R. Rosolem, Y. Zhang, H. Vereecken, H.-J. H. Franssen
2015 Geoscientific Model Development Discussions  
Online 1-D and 2-D visualization of data assimilation results is also implemented to facilitate the post simulation analysis.  ...  LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be introduced by perturbed atmospheric forcing data  ...  The study of this work was supported by the DFG (Deutsche Forschungs-  ... 
doi:10.5194/gmdd-8-7395-2015 fatcat:6xdddppbw5f7jepwcthyebwbde

A Novel Runoff Forecasting Model Based on the Decomposition-Integration-Prediction Framework

Zhanxing Xu, Jianzhong Zhou, Li Mo, Benjun Jia, Yuqi Yang, Wei Fang, Zhou Qin
2021 Water  
Decomposition-Integration-Prediction (DIP) using parallel-input neural network, and proposed a novel runoff forecasting model with Variational Mode Decomposition (VMD), Gated Recurrent Unit (GRU), and  ...  To explore the better application of time-frequency decomposition technology in runoff forecasting and improve the prediction accuracy, this research has developed a framework of runoff forecasting named  ...  Forward Prediction of Runoff Data in Data-Scarce Basins with an Improved Ensemble Empirical Mode Decomposition (EEMD) Model. Water 2018, 10, 388. [CrossRef] 36. Sankaran, A.; Janga Reddy, M.  ... 
doi:10.3390/w13233390 fatcat:emirtplmkjhmpgxjl2en66eaze

Data assimilation for wildland fires

2009 IEEE Control Systems  
The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.  ...  Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on semi-empirical fire spread by the level let method.  ...  The authors would like to thank Craig Douglas, Deng Li, Wei Li, and Adam Zornes from the University of Kentucky for their contributions to the software infrastructure that some of the codes used here were  ... 
doi:10.1109/mcs.2009.932224 fatcat:rvvdzrrtb5dr5a46hellzzpvni

South American Summer Monsoon variability over the last millennium in paleoclimate records and isotope-enabled climate models

Rebecca Orrison, Mathias Vuille, Jason E. Smerdon, James Apaéstegui, Vitor Azevedo, Jose Leandro P. S. Campos, Francisco W. Cruz, Marcela Eduarda Della Libera, Nicolás M. Stríkis
2022 Climate of the Past  
We extract the coherent modes of variability of the SASM over the last millennium (LM) using a Monte Carlo empirical orthogonal function (MCEOF) decomposition of 14 δ18O proxy records and compare them  ...  The spatial characteristics of these modes appear to be robust features of the LM hydroclimate over South America and are reproduced both in the proxy data and in isotope-enabled climate models, regardless  ...  To isolate the influence of the SASM requires the application of a network analysis such as Monte Carlo empirical orthogonal function (MCEOF) decomposition (Anchukaitis and Tierney, 2013) .  ... 
doi:10.5194/cp-18-2045-2022 fatcat:iofa6okkzvesjo4o77g3k4fwfq

Uncertainties of gross primary productivity of Chinese grasslands based on multi-source estimation

Panxing He, Xiaoliang Ma, Zhiming Han, Xiaoyu Meng, Zongjiu Sun
2022 Frontiers in Environmental Science  
In this study, uncertainty analysis of GPP datasets estimated based on terrestrial ecosystem models and remote sensing was conducted using cross-validation, standard error statistics, and ensemble empirical  ...  This study highlighted the need for uncertainty analysis when GPP is applied to spatio-temporal analysis, and suggested that when comparing and assessing carbon balance conditions, multiple source data  ...  To solve this problem, researcher proposed Ensemble Empirical Mode Decomposition (EEMD) (Wu and Huang 2009) , a method to aid data analysis by controlled addition of white noise, which can improve EMD  ... 
doi:10.3389/fenvs.2022.928351 fatcat:q66khkpwavbndfzjuvbnhd6seu

The JMA Nonhydrostatic Model and Its Applications to Operation and Research [chapter]

Kazuo Saito
2012 Atmospheric Model Applications  
A hydrostatic version of MRI-NHM was developed by Kato and Saito (1995) and was used to examine the applicability of hydrostatic approximation to a high-resolution simulation of moist convection.  ...  Density was defined by the sum of masses of moist air and the water substances per unit volume as www.intechopen.com  ...  Results of the ensemble prediction will also be used for input data of river and flood models for risk management applications at Kyoto University.  ... 
doi:10.5772/35368 fatcat:gdq3fyhe7naahmwarbapbtcula

On the time-varying trend in global-mean surface temperature

Zhaohua Wu, Norden E. Huang, John M. Wallace, Brian V. Smoliak, Xianyao Chen
2011 Climate Dynamics  
The shape of the secular trend and rather globally-uniform spatial pattern associated with it are both suggestive of a response to the buildup of well-mixed greenhouse gases.  ...  The Earth has warmed at an unprecedented pace in the decades of the 1980s and 1990s (IPCC in Climate change 2007: the scientific basis, Cambridge University Press, Cambridge, 2007). In Wu et al.  ...  Protocols for testing statistical significance of components derived from Empirical Mode Decomposition (EMD) or Ensemble Empirical Mode Decomposition (EEMD) have not yet been fully developed.  ... 
doi:10.1007/s00382-011-1128-8 fatcat:ogezkdqyzba7pl4d5ru6rl23fy

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Amir Mosavi, Mohsen Salimi, Sina Faizollahzadeh Ardabili, Timon Rabczuk, Shahaboddin Shamshirband, Annamaria Varkonyi-Koczy
2019 Energies  
During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems.  ...  This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications.  ...  The original data of wind speed was divided into a set of signal components using ensemble empirical mode decomposition.  ... 
doi:10.3390/en12071301 fatcat:vzwylqto3zdjhofwfot7jdpvce

Uncertainty Propagation in Coupled Atmosphere–Wave–Ocean Prediction System: A Study of Hurricane Earl (2010)

Guotu Li, Milan Curcic, Mohamed Iskandarani, Shuyi S. Chen, Omar M. Knio
2019 Monthly Weather Review  
models for time evolution of both the maximum wind speed and minimum sea level pressure in Earl.  ...  In addition, for the range of initial conditions considered RI seems mostly sensitive to azimuthally averaged maximum wind speed and asymmetry orientation, rather than storm size and asymmetry magnitude  ...  This research was made possible in part by a grant from the Gulf of Mexico Research Initiative, and by the U.S. Department of Energy (DOE), Office of Science, Office  ... 
doi:10.1175/mwr-d-17-0371.1 fatcat:jwnzm4mhtjasfirtbrd3mlqwme
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