2-D Empirical Mode Decomposition on the sphere, application to the spatial scales of surface temperature variations (original) (raw)
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Empirical Mode Decomposition (EMD) is applied here in two dimensions over the sphere to demonstrate its potential as a data-adaptive method of separating the different scales of spatial variability in a geophysical (climatological/meteorological) field. After a brief description of the basics of the EMD in 1 then 2 dimensions, the principles of its application on the sphere are explained, in particular via the use of a zonal equal area partitioning. EMD is first applied to an artificial dataset, demonstrating its capability in extracting the different (known) scales embedded in the field. The decomposition is then applied to a global mean surface temperature dataset, and we show qualitatively that it extracts successively larger scales of temperature variations related, for example, to topographic and large-scale, solar radiation forcing. We propose that EMD can be used as a global dataadaptive filter, which will be useful in analysing geophysical phenomena that arise as the result of forcings at multiple spatial scales.
Hydrology and Earth System Sciences, 2008
Empirical Mode Decomposition (EMD) is applied here in two dimensions over the sphere to demonstrate its potential as a data-adaptive method of separating the different scales of spatial variability in a geophysical (climatological/meteorological) field. After a brief description of the basics of the EMD in 1 then 2 dimensions, the principles of its application on the sphere are explained, in particular via the use of a zonal equal area partitioning. EMD is first applied to an artificial dataset, demonstrating its capability in extracting the different (known) scales embedded in the field. The decomposition is then applied to a global mean surface temperature dataset, and we show qualitatively that it extracts successively larger scales of temperature variations related, for example, to topographic and large-scale, solar radiation forcing. We propose that EMD can be used as a global dataadaptive filter, which will be useful in analysing geophysical phenomena that arise as the result of forcings at multiple spatial scales.
Empirical mode decomposition analysis of climate changes with special reference to rainfall data
Discrete Dynamics in Nature and Society, 2006
We have used empirical mode decomposition (EMD) method, which is especially well fitted for analyzing time-series data representing nonstationary and nonlinear processes. This method could decompose any time-varying data into a finite set of functions called "intrinsic mode functions" (IMFs). The EMD analysis successively extracts the IMFs with the highest local temporal frequencies in a recursive way. The extracted IMFs represent a set of successive low-pass spatial filters based entirely on the properties exhibited by the data. The IMFs are mutually orthogonal and more effective in isolating physical processes of various time scales. The results showed that most of the IMFs have normal distribution. Therefore, the energy density distribution of IMF samples satisfies χ 2 -distribution which is statistically significant. This study suggested that the recent global warming along with decadal climate variability contributes not only to the more extreme warm events, but also to more frequent, long lasting drought and flood.
ICEEMDAN, a variant of Empirical Mode Decomposition (EMD), is used to extract temperature cycles with periods from half a year to multiple decades from the \nobreak{HadCRUT5} global temperature anomaly data. The residual indicates an overall warming trend. The analysis is repeated for the Southern and Northern Hemispheres as well as the Tropics, defined as areas lying at or below 30 degrees of latitude. Multiannual cycles explain the apparently anomalous pause in global warming starting around 2000. The previously identified multidecadal cycle is found to be the most energetic and to account for recent global warming acceleration, beginning around 1993. This cycle's amplitude is found to be more variable than by previous work. Moreover, this variability varies by latitude. Sea ice loss acceleration is proposed as an explanation for global warming acceleration.
Analysis of Rainfall and Temperature Data Using Ensemble Empirical Mode Decomposition
Data Science Journal
Climatic variables such as rainfall and temperature have nonlinear and non-stationary characteristics such that analysing them using linear methods inconclusive results are found. Ensemble empirical mode decomposition (EEMD) is a data-adaptive method that is best suitable for data with nonlinear and non-stationary characteristics. The average monthly rainfall and temperature data for a selected region in South Africa are decomposed into intrinsic mode functions (IMFs) at different time scales using EEMD. The IMFs exhibit an inter-annual to inter-decadal variability. The influence of climatic oscillations such as El-Niño Southern Oscillation (ENSO) and quasi-biennial oscillation (QBO) is identified. The influence of temperature variability on rainfall is also shown at different time scales. Based on the results obtained, the EEMD method is found to be suitable to identify different oscillations in the rainfall and temperature data.
On-Line Empirical Mode Decomposition of Environmental Data
2011
This paper is devoted to an analysis of environmental time series using the empirical mode decomposition (EMD) method. This is a new method, which was developed for the off-line analysis of non-stationary and stochastic signals. The purpose of the work was to modify the EMD algorithm for on-line analysis and to find out the states of ecosystems in real time. The practical realization of the algorithm was implemented using the MATLAB software and its simulation toolbox, SIMULINK. The modified algorithm was verified using the environmental data that was measured by meteorological stations deployed in the southern part of the Czech Republic. The results obtained are shown graphically.
Communications in Computer and Information Science, 2019
Global warming is a real threat for the survival of life on Earth in the following 80 years. The effects of Global Warming are particularly harmful for inhabitants of very saturated urban settlements, which is the case of Mexico City. In this work, we analyse temperature time series from Mexico City Downtown, taken hourly between 1986 and 2019. The gaps in the time series were interpolated through the kriging method. Then, temporal tendencies and main frequencies were obtained through Empirical Mode Decomposition. The first frequency mode reveals a clear increasing tendency driven by Global Warming, which for 2019 was of 0.72 • C above a 30-year baseline period mean between 1986 and 2016. Furthermore, the shorter periods identified in the first intrinsic mode functions are likely driven by the solar activity periods. It remains to find the origin of the smallest identified periods in the time series (<0.36 years).
11-Year solar cycle in the stratosphere extracted by the empirical mode decomposition method
Advances in Space Research, 2004
We apply a novel method to extract the solar cycle signal from stratospheric data. An alternative to traditional analysis is a nonlinear empirical mode decomposition (EMD) method. This method is adaptive and therefore highly efficient at identifying embedded structures, even those with small amplitudes. Using this analysis, the geopotential height in the Northern Hemisphere can be completely decomposed into five non-stationary temporal modes including an annual cycle, a QBO signal, an ENSO-like mode, a solar cycle signal and a trend. High correlations with the sunspot cycle unambiguously establish that the fourth mode is an 11-year solar cycle signal.