scholarly journals Spectrum analysis and prediction possibilities of the onset dates of southwest monsoon over Kerala (India)

MAUSAM ◽  
2022 ◽  
Vol 46 (1) ◽  
pp. 15-24
Author(s):  
R. P. KANE
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Zonal Wind ◽  
Monsoon Rainfall ◽  
Late Onset ◽  
A Priori ◽  
Southwest Monsoon ◽  
Random Component ◽  
Maximum Entropy Spectral Analysis ◽  
Early Late

Maximum Entropy Spectral Analysis of the time series for the onset dates of the southwest monsoon over Kerala (India) revealed several periodicities significant at a 2a a priori level. some at a 3 C a  priori level However these contributed only 40-50% to the total variance thus indicating 50-60% as purely random component. Also many of the significant periodicities observed were in the QBO region (T = 2-3 years) which. due to their variable periodicities and amplitudes, are almost equivalent to a random component. Hence predictions were possible only with a  limit exceeding 5 days which are probably not very useful for any planning purposes agricultural or otherwise. No relationship was found between onset dates of established monsoon rainfall and the 50 hPa mean monthly equatorial zonal wind for the months of March, April, May or June. However there is a possibility that a relationship may exist between westerly (easterly) winds in May and early (late) onset of the first monsoon (or pre-monsoon ?) rainfall in Kerala. Meager or otherwise.    

Choice of operator length for maximum entropy spectral analysis

Geophysics ◽  
1978 ◽  
Vol 43 (7) ◽  
pp. 1384-1391 ◽  
Author(s):  
James G. Berryman
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Empirical Evidence ◽  
Discrete Time ◽  
Seismic Data ◽  
A Priori ◽  
Evidence Based ◽  
Maximum Entropy Spectral Analysis ◽  
Discrete Time Series

Empirical evidence based on maximum entropy spectra of real seismic data suggests that M = 2N/ln 2N is a reasonable a priori choice of the operator length M for discrete time series of length N. Various examples support this conclusion.

scholarly journals Periodicities in the time-series of the annual mean temperatures of Central England

MAUSAM ◽  
2021 ◽  
Vol 48 (1) ◽  
pp. 41-44
Author(s):  
R.P. KANE ◽  
N.B. TRIVEDI
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Sunspot Number ◽  
Temperature Series ◽  
Number Series ◽  
Maximum Entropy Spectral Analysis ◽  
Mean Temperature ◽  
Good Comparison

ABSTRACT .Maximum Entropy spectral Analysis (MESA) of the 8IUlua1 mean temperature series for Central England for 1659-1991 indicated significant periodicilies at T = 7.8, 11.1, 12.5, 15, 18, 23, 32, 37, 68, 81, l09 and 203 years. These compare well with T = 22, 30, 80, 200 years obtained for China. Also, a good comparison is obtained with some periodicities in the sunspot number series.    

Periodicities in Atlantic Coast Striped Bass (Morone saxatilis) Commercial Fisheries Data

1979 ◽  
Vol 36 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Webster Van Winkle ◽  
Bernadette L. Kirk ◽  
Bert W. Rust
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Maximum Entropy ◽  
Striped Bass ◽  
Atlantic Coast ◽  
Morone Saxatilis ◽  
Commercial Fisheries ◽  
Time Interval ◽  
Maximum Entropy Spectral Analysis ◽  
The North

Atlantic Coast striped bass (Morone saxatilis) commercial fisheries data are examined for periodicities in the appearance of dominant year-classes using autocorrelation and spectral-analysis techniques. Results obtained using maximum entropy and classical Fourier spectral-analysis methods are compared. Statistically significant periodicities of approximately 20 yr and of 6–8 yr are common to the time series for most states and regions. Since the periodicities are neither very pronounced nor simple, it is difficult to isolate the causative factors, which are more likely to be density-independent environmental factors enhancing survival of the young than intrinsic characteristics of the life cycle of striped bass. The spectra for Maryland landings and Maryland landings per unit gear are nearly identical, suggesting that at least for Maryland the landings and landings-per-unit-gear data are approximately equally reliable as indices of stock size. The structure of the North Carolina time series is unique, which supports the opinion that this stock does not mix appreciably with Chesapeake and Hudson stocks. Impact assessments and monitoring programs should not be predicted on the expectation of pronounced or simple periodicities of 6 years or any other time interval in the appearance of dominant year-classes in Atlantic Coast striped bass populations. Key words: autocorrelation analysis, commercial fisheries data, Fourier spectral analysis, maximum entropy spectral analysis, periodicities, striped bass

MULTIRESOLUTION ANALYSIS FOR SEPARATING CLOSELY SPACED FREQUENCIES WITH AN APPLICATION TO INDIAN MONSOON RAINFALL DATA

2007 ◽  
Vol 05 (05) ◽  
pp. 735-752 ◽  
Author(s):  
SARITA AZAD ◽  
R. NARASIMHA ◽  
S. K. SETT
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Monsoon Rainfall ◽  
Indian Monsoon ◽  
Rainfall Data ◽  
Accurate Estimation ◽  
Discrete Wavelet ◽  
Annual Data ◽  
Indian Monsoon Rainfall ◽  
Spectral Peaks

In this paper we make use of the multiresolution properties of discrete wavelets, including their ability to remove interference, to reveal closely spaced spectral peaks. We propose a procedure which we first verify on two test signals, and then apply it to the time series of homogeneous Indian monsoon rainfall annual data. We show that, compared to empirical mode decomposition, discrete wavelet analysis is more effective in identifying closely spaced frequencies if used in combination with classical power spectral analysis of wavelet-based partially reconstructed time series. An effective criterion based on better localization of specific frequency components and accurate estimation of their amplitudes is used to select an appropriate wavelet. It is shown here that the discrete Meyer wavelet has the best frequency properties among the wavelet families considered (Haar, Daubechies, Coiflet and Symlet). In rainfall data, the present analysis reveals two additional spectral peaks besides the fifteen found by classical spectral analysis. Moreover, these two new peaks have been found to be statistically significant, although a detailed discussion of testing for significance is being presented elsewhere.

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