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

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.

Interannual variability of atmospheric Nitrous Oxide (N2O)

The 12-monthly running means of N2O measured at seven locations during 1977-91 were used for obtaining the yearly percentage growth rate series (4 values per year, centered 3 months apart), which were subjected to MESA (Maximum Entropy Spectral Analysis). The spectra revealed significant QBO and QTO (Quasi-biennial and Quasi-triennial oscillations) with QBO periods in the range (2.04-2.38) years and QTO periods near 4.0 years. These do not resemble the QBO of 2.58 years of the 50 hPa low latitude wnal wind but do resemble the QBO of 2.31 years and the 4.1 year periods of the Southern oscillation phenomenon, represented by Tahiti minus Darwin sea level atmospheric pressure difference (T-D).

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

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.

Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China

Identifying implicit periodicities in hydrological data is significant for managing river–basin water resources and establishing flood forecasting systems. However, the complexity and randomness of hydrological systems make it difficult to detect hidden oscillatory characteristics. This study discusses the performance and applicability of five period identification methods, namely periodograms, autocorrelation analysis (AA), maximum entropy spectral analysis (MESA), wavelet analysis (WA), and the Hilbert–Huang transform (HHT). The annual and monthly runoff data are sampled from two stations (Huayuankou and Lijin on the Yellow River in China) in the years 1949–2015. The conclusions are as follows: (i) All methods identify the significant periods of 6 months, 12 months, and 18–19 months, which have relatively high energy of peaks; (ii) WA and HHT perform best when dealing with nonstationary time series, but they are ineffective for identifying large-scale periods; (iii) MESA has high resolution and stability but is prone to oscillate at small-scale periods when applied to monthly series; and (iv) periodograms and AA are relatively simple, but their results lack stability and are significantly affected by the data length—the resolution of AA is too low when applied to annual data, and periodograms can easily produce “false peaks”. Generally, it is better to apply multiple methods comprehensively than each method singularly, and this can be effective in reducing subjective influences.

0323 Associations Between Sleep and Activity Patterns in the Mother-Child Dyad

Introduction Animal studies show links between light-dark patterns in gestating dams and offsprings’ sleep and circadian rhythms. Activity patterns between mothers and infants show synchrony as early as 12 weeks postpartum. Our goal was to investigate maternal sleep/activity patterns at two points in the perinatal period and assess associations with activity in their young children. Methods Participants were 20 mother-child dyads recruited from previous studies. Mothers (age±SD = 31.7±5.5 years, range 21-40 years) wore wrist actigraphs during their 33rd week of pregnancy and 2nd week postpartum. Children (age±SD = 2.13±1.36 years, range 8 months-4.6 years) were assessed for 5 days and nights. Circadian patterns were analyzed using Maximum Entropy Spectral Analysis (MESA) to estimate best-fitting circadian period, tau. We used cosinor analysis to calculate rhythm amplitude, Midline Statistic of Rhythm (MESOR, representing mean activity), and acrophase (time of peak amplitude). We used circadian quotient (CQ; amplitude÷MESOR) to assess rhythm strength while normalizing for intersubject variation in activity levels. Autocorrelation, or degree to which data is consistent for a particular period, was calculated to analyze regularity of activity patterns. Results Mothers’ activity pattern autocorrelation was significantly correlated at the two time points (r=.530, p=.016), such that women with inconsistent activity patterns in pregnancy also demonstrated more irregularity at postpartum week 2. Child CQ correlated with age, with older children showing greater rhythm strength (r=.530, p=0.016). We observed a moderate correlation between mothers’ CQs during pregnancy and children’s CQs (r=.413,p=.07). In mother-child dyads, longer tau in mothers during pregnancy predicted lower autocorrelation of the child’s rhythm to a 24-hr period (r=-.520, p=.019). Finally, later maternal acrophase at postpartum week 2 was associated with longer tau in children (r=.504, p=.024). Conclusion These data show that associations between mother-child sleep/activity patterns may begin during pregnancy and support the notion that mothers’ perinatal sleep patterns could affect the health of both mothers and their children. Support Supported by R34MH104377, K23MH086689, the Seleni Institute, the Depression and Bipolar Disorder Alternative Treatment Foundation, and a Karen T. Romer Undergraduate Teaching and Research Award from Brown University.

CIRCADA: Shiny Apps for Exploration of Experimental and Synthetic Circadian Time Series with an Educational Emphasis

Circadian rhythms are daily oscillations in physiology and behavior that can be assessed by recording body temperature, locomotor activity, or bioluminescent reporters, among other measures. These different types of data can vary greatly in waveform, noise characteristics, typical sampling rate, and length of recording. We developed 2 Shiny apps for exploration of these data, enabling visualization and analysis of circadian parameters such as period and phase. Methods include the discrete wavelet transform, sine fitting, the Lomb-Scargle periodogram, autocorrelation, and maximum entropy spectral analysis, giving a sense of how well each method works on each type of data. The apps also provide educational overviews and guidance for these methods, supporting the training of those new to this type of analysis. CIRCADA-E (Circadian App for Data Analysis–Experimental Time Series) allows users to explore a large curated experimental data set with mouse body temperature, locomotor activity, and PER2::LUC rhythms recorded from multiple tissues. CIRCADA-S (Circadian App for Data Analysis–Synthetic Time Series) generates and analyzes time series with user-specified parameters, thereby demonstrating how the accuracy of period and phase estimation depends on the type and level of noise, sampling rate, length of recording, and method. We demonstrate the potential uses of the apps through 2 in silico case studies.

Multichannel maximum-entropy method for the Wigner-Ville distribution

The Wigner-Ville distribution is a powerful technique for the time-frequency spectral analysis of nonstationary seismic data. However, the Wigner-Ville distribution suffers from cross-term interference between different wave components in seismic data. To mitigate cross-term interference, we have developed a multichannel maximum-entropy method (MEM) to modify the Wigner-Ville kernel. The method is related to the conventional maximum-entropy spectral analysis (MESA) algorithm because both algorithms use Burg’s reflection coefficients for the calculation of the prediction-error filter (PEF). The MESA algorithm works on the standard autocorrelation sequence, but it does not work for the Wigner-Ville kernel, which is an instantaneous autocorrelation sequence. Our multichannel MEM algorithm uses the PEF to modify any single Wigner-Ville kernel sequence by exploiting multiple Wigner-Ville kernel sequences simultaneously. This multichannel implementation is capable of robustly determining the reflection coefficient and a minimum-phased PEF for the Wigner-Ville kernel sequence. The Wigner-Ville distribution and the multichannel MEM algorithm in conjunction with each other in turn can produce a high-resolution time-frequency spectrum by mitigating the cross-term interferences and suppressing the spurious energy in the spectrum.

Utilizing Maximum Entropy Spectral Analysis (MESA) to identify Milankovitch cycles in Lower Member of Miocene Zhujiang Formation in north slope of Baiyun Sag, Pearl River Mouth Basin, South China Sea

Logs in the petroleum boreholes indirectly records the sedimentary cycles in the deep burial formation. In order to extract and understand the periodicity and cyclicity, it is necessary to process the data by digital signal analysis method. Taking the gamma ray (GR) log as the primary material, an identification approach of Milankovitch cycles in boreholes is proposed in this paper, which is based on the Maximum Entropy Spectral Analysis (MESA). The first stage chooses the appropriate windows for calculating the frequency spectral properties in a short section of the data. In each depth window, the second stage generates the two-dimension frequency spectrum utilizing the MESA. At each depth point, the third stage finds the potential Milankovitch cycles in the one-dimension frequency spectrum, in which the average amplitude spectrum peak would be matched to the ratio of Milankovitch period. According to the frequency and wavelength of the maximum amplitude in Milankovitch cycles, the fourth stage estimates the sedimentation rate controlled by cyclical factor. Finally, the Milankovitch cycles in Lower Member of Miocene Zhujiang Formation in north slope of Baiyun Sag, Pearl River Mouth Basin, are identified and the cyclical sedimentation rate is estimated. The results demonstrate that the proposed method is feasible and effective to identify Milankovitch cycles in boreholes, which may contribute to the other geological researches.