Multichannel Singular Spectral Analysis Research Articles

Examining relations between sea-level anomalies in the Niño 3.4 region and terrestrial hydroclimatic conditions in China

As a key driver of global climate variability, El Niño–Southern Oscillation (ENSO) significantly impacts the regional hydrological cycle. Similar to the recognized ENSO monitoring index (i.e., sea surface temperature anomaly), a potential advancement in the usage of sea level anomaly (SLA) in the equatorial Pacific is expected to enhance our knowledge of ENSO and its influence on regional terrestrial hydroclimatic conditions (THCs). In this study, we employed wavelet coherence and multi-channel singular spectral analysis to investigate the ability of satellite altimetry-based SLA, and its two major components (i.e., steric SLA (SSL) and non-steric SLA (NSSL)) averaged over Niño 3.4 region, to identify ENSO-related signals. Subsequently, we explored their spatial association with THCs at interannual scales in China using a cross-correlation method. We found that the time series of sea surface temperature anomaly index (SSTaI), SLA index (SLAI), and SSL index (SSLI) displayed high consistency. SSTaI was found to present higher correlations to SLAI (0.91) and SSLI (0.88), compared to NSSL index (NSSLI; 0.40). Furthermore, the high (low) wavelet coherence and in-phase (orthogonal) relationship between SSTaI and SLAI/SSLI (NSSLI) was also revealed. This indicated that SLAI and SSLI may be good complementary indices to SSTaI in observing ENSO-related signals. Similar to the temporal association of SSTaI and SLAI/SSLI, their spatial correlation pattern with THCs also displayed consistency. The time lag characterizing the relationship between SSTaI/SLAI/SSLI and THCs was also consistent, despite some regional differences. In summary, our results indicated that the combined analyses of the time lag determined between SSTaI/SLAI/SSLI and THCs can eventually complement the time lag analysis based solely on SSTaI. This, in turn, provides a more comprehensive understanding of ENSO’s influence on THCs in China.

Revealing the spatiotemporal patterns of water vapor and its link to North Atlantic Oscillation over Greenland using GPS and ERA5 data

Precipitation plays an important role in the interannual mass variations of Greenland Ice Sheet (GrIS) and is highly influenced by atmospheric circulation change. The relationship between precipitation and North Atlantic Oscillation (NAO) has been revealed by many studies, but the role of water vapor transportation in the NAO-precipitation relationship was rarely investigated. Therefore, to fill the knowledge gap of how water vapor changes and responds to NAO in space and time, we applied Multichannel Singular Spectral Analysis (MSSA) to the Global Positioning System (GPS) and the fifth-generation reanalysis dataset of the European Center for Medium-Range Weather Forecasting (ERA5) Precipitable Water Vapor (PWV) data to extract the interannual PWV signals in Greenland. Results show that the interannual PWV signals overall increased in 2008–2011, decreased in 2011–2015, and increased in 2015–2021. The amplitudes of the interannual signals derived from both the GPS PWV and ERA5 basin-averaged PWV exhibited an overall southwest-northeast decreasing gradient. We also found anticorrelation between the interannual PWV signals and the NAO signal over Greenland but the correlation coefficients are not statistically significant, and the correlation coefficients in most cases were less than −0.65, indicating that positive (negative) NAO phase decreased (increased) the water vapor content. The Fast Fourier Transform (FFT) results illustrated that the interannual signals derived from both the GPS site-dependent and the ERA5 basin-averaged PWV had similar dominant frequencies to that of the NAO signal, reinforcing their correlations. This study reveals the spatiotemporal pattern of the interannual water vapor and its linkage to the NAO, providing a new perspective for understanding the climate change on Greenland.

Short-Term Traffic Speed Prediction for Multiple Road Segments

Short-term traffic prediction has been an essential part of real-time applications in modern transportation systems for the last few decades. Despite the recent progress in the voluminous models and data sources, many existing studies have focused on prediction for either a single or a few locations. In addition, the spatiotemporal dependency in the traffic data was narrowly accounted for. Therefore, this paper finds a new short-term traffic speed prediction algorithm that can efficiently cope with the complexity and immensity of the prediction process derived from the network size and amount of data in order to provide accurate predictions in real time. This algorithm consists of two modules: (a) principal component analysis (PCA) for data dimensionality reduction and feature selection, and (b) multichannel singular spectral analysis (MSSA) for multivariate time-series data prediction. A large amount of traffic data is efficiently compressed by PCA with high accuracy, then used as an input in the multivariate time-series analysis. The algorithm was compared with a vector autoregressive (VAR) model to predict traffic speeds five minutes ahead for a 21.3-mile-long highway segment, using the traffic detector data, and for 451-mile-long segment, using probe-based speed data in Tennessee. The tested algorithm is found to provide accurate predictions with a computation time of less than one second without training. Furthermore, the algorithm shows a better prediction performance under congested flow conditions, compared to VAR. This indicates that the tested algorithm is suitable for real-time prediction and scalable for a large network analysis.

Time-Lapse Monitoring of Daily Velocity Changes in Binchuan, Southwestern China, Using Large-Volume Air-Gun Source Array Data

Abstract Temporal changes of seismic velocities in the Earth’s crust can be induced by stress perturbations or material damage from reasons such as strong ground motion, volcanic activities, and atmospheric effects. However, monitoring the temporal changes remains challenging, because most of them generally exist in small travel-time differences of seismic data. Here, we present an excellent case of daily variations of the subsurface structure detected using a large-volume air-gun source array of one-month experiment in Binchuan, Yunnan, southwestern China. The seismic data were recorded by 12 stations within ∼10 km away from the source and used to detect velocity change in the crust using the deconvolution method and sliding window cross-correlation method, which can eliminate the “intercept” error when cutting the air-gun signals and get the real subsurface variations. Furthermore, the multichannel singular spectral analysis method is used to separate the daily change (∼1 cycle per day) from the “long-period” change (<1 cycle per day) or noise. The result suggests that the daily velocity changes at the two nearest stations, 53277 (offset ∼700 m) and 53278 (offset ∼2.3 km), are well correlated with air temperature variation with a time lag of 5.0 ± 1.5 hr, which reflects that the velocity variations at the subsurface are likely attributed to thermoelastic strain. In contrast, both daily and long-period velocity changes at distant stations correlate better with the varying air pressure than the temperature, indicating that the velocity variations at deeper depth are dominated by the elastic loading of air pressure. Our results demonstrate that the air-gun source is a powerful tool to detect the velocity variation of the shallow crust media.

A novel ENSO monitoring index and its potential for drought application

The El Niño–Southern Oscillation (ENSO) changes the air flow in the tropics, causing global droughts, floods, typhoons, and other natural disasters. Therefore, monitoring and forecasting of ENSO events effectively play an important role in disaster prevention and mitigation. This paper proposes a novel ENSO monitoring index using GNSS-derived zenith tropospheric delay (ZTD) and temperature (T), which is called the standardized ENSO monitoring index (SEMI), and validated its performance by introducing the standardized precipitation evapotranspiration index (SPEI). Multichannel singular spectral analysis (MSSA) and Lomb-Scargle periodogram are first applied to determine the periodic signals of ZTD and T. Empirical Orthogonal Function (EOF) is then used to analyze the spatio-temporal characteristics of ZTD and T. Finally, the response thresholds of ZTD and T to ENSO events are determined using the moving window correlation analysis (MWCA) method, and the SEMI is established based on the relationship between abnormal changes of sea surface temperature anomaly (SSTa) and ZTD/T. ZTD and T of 97 GNSS stations and the corresponding SPEI over the period of 2006–2016 in Taiwan province has been obtained to validate the capacity of the proposed SEMI for ENSO monitoring. Experimental results reveal that (1) annual and semi-annual periods existed in ZTD and T with the values of 365.25 and 182.625 days, respectively. (2) ZTD and T have good correlations with SSTa, and the coefficients are 0.64 and 0.70, respectively. (3) The response thresholds of ZTD and T to ENSO events in Taiwan province are [−1.13 °C, 0.84 °C] and [−1.09 °C, 0.69 °C], respectively. (4) The proposed SEMI has a good relationship with SSTa with a correlation coefficient of 0.77 and has been proven to have good ENSO monitoring capacity by comparing with SPEI with a correlation coefficient of 0.83. This result is of great significance for drought and flood disaster monitoring by the GNSS technique.

Contributions of Greenland GPS Observed Deformation From Multisource Mass Loading Induced Seasonal and Transient Signals

AbstractGlobal Positioning System (GPS) provides critical geodetic constraints on Earth's elastic response induced by ice and other mass loadings in Greenland. Previous studies focused on long‐term ice mass changes, with relatively fewer studies on transient signals and had used fewer GPS stations. Here we reconstructed 44 Greenland‐wide coastal decadal GPS time series using multichannel singular spectral analysis and quantified the origins of spatiotemporal patterns of transient to seasonal signals. We used an exhaustive list of geophysical processes, including surface mass balance (SMB), atmospheric pressure, continental hydrology, nontidal ocean loading, bedrock thermal expansion, precipitation, runoff, and ice discharge, and identified that most of Greenland's GPS sites exhibit SMB‐induced transient signals during 2012–2015, with maximum displacement reaching 20.45 mm. Analyses of differenced time series between adjacent GPS stations near peripheral glaciers reveal that locally varying seasonal signals are primarily attributable to SMB, followed by atmosphere.

FORECASTING FINANCIAL CYCLES: CAN BIG DATA HELP?

Financial cycles as a source of financial crisis and business cycles that was demonstrated during the financial crisis of 2008, so it is important to understand proper methods of measuring and forecasting them to unravel their true nature. We searched financial big data for the UK, USA, Japan and China for a period 2004Q1 to 2019Q1 to find important data corresponding to the research and determine their importance for the financial cycle studies. We use singular spectral analysis (SSA without financial big data) and multichannel singular spectral analysis (MSSA with financial big data) to identify significant deterministic cycles in the residential property prices, credits to private non-financial sector and credit share in the GDP. The forecast test results show on the data for the UK, USA, Japan and China that inclusion of the financial big data significantly (on the level from 30% to four times) improves forecast accuracy for financial cycle components. This is a first study on the importance of the link between financial cycles and financial big data. Policymakers, practitioners and financial cycles research should take into the account the importance of financial big data for the studies of financial cycles for a better understanding of their true nature and improving their forecast accuracy.

A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China

Southeast China, a non-core region influenced by the El Niño–Southern Oscillation (ENSO), has been seldom investigated before. However, the occurrence of ENSO will affect the redistribution of precipitation and the temperature (T) spatial pattern on a global scale. This condition will further lead to flood or drought disasters in Southeast China. Therefore, the method of monitoring the occurrence of ENSO is important and is the focus of this paper. The spatiotemporal characteristics of precipitable water vapor (PWV) and T are first analyzed during ENSO using the empirical orthogonal function (EOF). The results showed that a high correlation spatiotemporal consistency exist between PWV and T. The response thresholds of PWV and T to ENSO are determined by moving the window correlation analysis (MWCA). If the sea surface temperature anomaly (SSTA) at the Niño 3.4 region exceeded the ranges of (−1.17°C, 1.04°C) and (−1.15°C, 1.09°C), it could cause the anomalous change of PWV and T in Southeast China. Multichannel singular spectral analysis (MSSA) is introduced to analyze the multi-type signals (tendency, period, and anomaly) of PWV and T over the period of 1979–2017. The results showed that the annual abnormal signal and envelope line fluctuation of PWV and T agreed well in most cases with the change in SSTA. Therefore, a standard PWV and T index (SPTI) is proposed on the basis of the results to monitor ENSO events. The PWV and T data derived from the grid-based European Center for Medium-Range Weather Forecasting (ECMWF) reanalysis products and GNSS/RS stations in Southeast China were used to validate the performance of the proposed SPTI. Experimental results revealed that the time series of average SPTI calculated in Southeast China corresponded well to that of SSTA with a correlation coefficient of 0.66 over the period of 1979–2017. The PWV values derived from the Global Navigation Satellite System (GNSS) and radiosonde data at two specific stations (WUHN and 45004) were also used to calculate the SPTI. The results showed that the correlation coefficients between SPTI and SSTA were 0.73 and 0.71, respectively. Such results indicate the capacity of the proposed SPTI to monitor the ENSO in Southeast China.

Geodetic and model data reveal different spatio-temporal patterns of transient mass changes over Greenland from 2007 to 2017

Much of the research to understand the ice mass changes of Greenland ice sheet (GrIS) has focused on detecting linear rates and accelerations at decadal or longer periods. The transient (short-term, non-secular) mass changes show large variability, and if not properly accounted for, can introduce significant biases into estimates of long-term ice mass loss rates and accelerations. Despite the growing number of geodetic observations, in terms of spatial coverage, types of observables, and the extent of the time series, studies of the transient mass changes over GrIS are lacking. To address this limitation, we apply multi-channel singular spectral analysis to the Gravity Recovery and Climate Experiment (GRACE) mass concentrations (mascon), surface mass balance (SMB) model output, and ice discharge data, to determine the transient mass changes over Greenland over the decade (2007 to 2017). The goal of this analysis is to elucidate the spatio-temporal variability of the ice mass change. For the entire GrIS, both the mascon and SMB transient mass changes are characterized by a sustained mass gain from late 2007 to early 2010, a sustained mass loss from early 2010 to early 2013, and a mass gain from early 2013 to mid-2015. Global Positioning System sites deployed along the coast of Greenland showed uplift from early 2010 to early 2013 and subsidence from early 2013 to 2015, consistent with the corresponding ice mass loss and gain of the entire GrIS. The peak-to-peak amplitude of the transient mass change was estimated to be −294 ± 27 Gt from GRACE mascons and -252 ± 16 Gt from the SMB where the latter value includes the effect of ice discharge. The transient mass change due to ice discharge accounted for less than 10% of the total transient mass change. Our regional assessment reveals that the central-west, southwest, northeast, and southeast regions display similar time-varying patterns as we found for the entire GrIS, but the north and northwest regions show different patterns. Atmospheric circulation anomalies as measured by the Greenland Blocking Index (GBI) are able to explain most of these transient anomalies. More specifically, high-GBI-associated high temperature was one of the main reasons for the transient mass loss of the entire GrIS during 2010-2012 while low GBI can explain the transient mass gain during 2013-2015. Contrasting behaviors of precipitation anomalies in east and west Greenland under abnormally high or low GBI conditions may explain the different patterns of the transient mass change in the northwest and the rest of Greenland.

Assessing the Potential Benefits of the Geostationary Vantage Point for Generating Daily Chlorophyll-a Maps in the Baltic Sea

Currently, observations from low-Earth orbit (LEO) ocean color sensors represent one of the most used tools to study surface optical and biogeochemical properties of the ocean. LEO observations are available at daily temporal resolution, and are often combined into weekly, monthly, seasonal, and annual averages in order to obtain sufficient spatial coverage. Indeed, daily satellite maps of the main oceanic variables (e.g., surface phytoplankton chlorophyll-a) generally have many data gaps, mainly due to clouds, which can be filled using either Optimal Interpolation or the Empirical Orthogonal Functions approach. Such interpolations, however, may introduce large uncertainties in the final product. Here, our goal is to quantify the potential benefits of having high-temporal resolution observations from a geostationary (GEO) ocean color sensor to reduce interpolation errors in the reconstructed hourly and daily chlorophyll-a products. To this aim, we used modeled chlorophyll-a fields from the Copernicus Marine Environment Monitoring Service’s (CMEMS) Baltic Monitoring and Forecasting Centre (BAL MFC) and satellite cloud observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor (on board the geostationary satellite METEOSAT). The sampling of a GEO was thus simulated by combining the hourly chlorophyll fields and clouds masks, then hourly and daily chlorophyll-a products were generated after interpolation from neighboring valid data using the Multi-Channel Singular Spectral Analysis (M-SSA). Two cases are discussed: (i) A reconstruction based on the typical sampling of a LEO and, (ii) a simulation of a GEO sampling with hourly observations. The results show that the root mean square and interpolation bias errors are significantly reduced using hourly observations.

Geodetic measurements reveal short-term changes of glacial mass near Jakobshavn Isbræ (Greenland) from 2007 to 2017

The Global Positioning System (GPS) and Gravity Recovery and Climate Experiment (GRACE) provide important geodetic datasets to study glacial mass change. Applying the multichannel singular spectral analysis to the GPS-measured vertical and horizonal crustal displacement and GRACE-derived vertical displacement near Jakobshavn Isbræ (JI) in western Greenland from 2007 to 2017, we reconstruct the short-term loading displacements due to ice mass changes. Both the vertical and east displacements show strong seasonal variability. They also reveal three episodes of transient displacements: downward and eastward motion from late 2007 to around 2010, sustained upward and westward motion from 2010 to early 2013, and downward and eastward motion till late 2016. We also forward model the seasonal and transient displacements caused by surface mass balance (SMB) and glacier dynamics. Our model agrees well with the geodetic observations and provides quantitative insights into the contribution from SMB and ice dynamics to the ice mass changes. We find that SMB is the dominant contributor to the seasonal and transient displacements at three out of four GPS sites (AASI, ILUL, and QEQE). While, at the fourth GPS site (KAGA) that is closest to the glacier, the contributions to the transient displacements from SMB and glacier dynamics are comparable. The forward modeling also suggests that the dynamic mass change in the JI catchment underwent strong seasonal variations and these variations correlated more with the seasonal retreat and advance of the calving front than with the changes of glacial velocities. Our altimetry results reveal that the frontal portion of JI catchment lost 34 Gt in 2012 and this loss of ice declined to only 11 Gt in 2016 due to widespread thickening along the main flowline.

A Co-Evolutionary, Long-Term, Macroeconomic Forecast for the UK Using Demographic Projections

This paper is based around outlining and illustrating the use of a co-evolutionary method for long-term macro-economic forecasting. The paper includes economic forecasts for the UK to 2060 using a novel approach based on Multichannel Singular Spectral Analysis (MSSA). The forecasts are based on projections of the working-age population and their educational attainment, as well as building on the historic trends of these variables. The variables forecasted are Gross Domestic Product (GDP), investment and productivity, based on historic time-series dating back to 1856, and their interactions with the projected variables. Other long-term forecasts for the UK are examined and the important impact of demographic change and plateauing educational attainment is assessed. Additionally, the power of the new MSSA forecasting technique proposed here is illustrated.

Transient Variations in Glacial Mass Near Upernavik Isstrøm (West Greenland) Detected by the Combined Use of GPS and GRACE Data

AbstractThe continuously operating Global Positioning System (GPS) sites mounted on bedrock around the coast of Greenland provide important geodetic data sets to quantify the solid Earth's response to historical and present‐day ice mass variations. However, the presence of colored noise and irregular seasonal signals makes it difficult to detect transient changes in GPS time series. Here we apply the multichannel singular spectral analysis to the combination of GPS data and Gravity Recovery and Climate Experiment (GRACE) data so that we can identify and fully utilize the spatial correlations from these two independent data sets. Using the GPS and GRACE data near Upernavik Isstrøm in West Greenland as an example, we demonstrate that this method successfully detects two transient signals in ice mass variations during 2008 and 2014. Our forward modeling of loading displacements due to changes in surface mass balance (SMB) and ice dynamics suggests that the transient change starting in mid‐2008 was due to the combined contributions from dynamically induced mass loss and SMB. The transient change starting in mid‐2011 was mainly due to ablation. Specifically, the ice melted more in 2012 and less in 2013 with little contribution from anomalies in accumulation.

Spatial and temporal patterns of the inter-annual oscillations of glacier mass over Central Asia inferred from Gravity Recovery and Climate Experiment (GRACE) data

Monitoring glacier mass balance is crucial to managing water resources and also to understanding climate change for the arid and semi-arid regions of Central Asia. This study extracted the inter-annual oscillations of glacier mass over Central Asia from the first ten principal components (S-PCs) of filtered variability via multichannel singular spectral analysis (MSSA), based on gridded data of glacier mass inferred from Gravity Recovery and Climate Experiment (GRACE) data obtained from July 2002 to March 2015. Two significant cycles of glacier mass balance oscillations were identified. The first cycle with a period of 6.1-year accounted for 54.5% of the total variance and the second with a period of 2.3-year accounted for 4.3%. The 6.1-year oscillation exhibited a stronger variability compared with the 2.3-year oscillation. For the 6.1-year oscillation, the results from lagged cross-correlation function suggested that there were significant correlations between glacier mass balances and precipitation variations with the precipitation variations leading the response of glacier mass balances by 9–16 months.

Patterns of parasite transmission in polar seas: Daily rhythms of cercarial emergence from intertidal snails

Trematodes are common parasites in intertidal ecosystems. Cercariae, their dispersive larvae, ensure transmission of infection from the first intermediate molluscan host to the second intermediate (invertebrates and fishes) or the final (fishes, marine birds and mammals) host. Trematode transmission in polar seas, while interesting in many respects, is poorly studied. This study aimed to elucidate the patterns of cercarial emergence from intertidal snails at the White Sea and Barents Sea. The study, involving cercariae of 12 species, has provided the most extensive material obtained so far in high latitude seas (66–69° N). The experiments were conducted in situ. Multichannel singular spectral analysis (MSSA) used for processing primary data made it possible to estimate the relative contribution of different oscillations into the analysed time series and to separate the daily component from the other oscillatory components and the noise. Cercarial emergence had pronounced daily rhythms, which did not depend on the daily tidal schedule but were regulated by thermo- and photoperiod. Daily emergence maximums coincided with periods favourable for infecting the second intermediate hosts. Cercarial daily emergence rhythms differed in species using the same molluscan hosts which can be explained by cercarial host searching behaviour. Daily cercarial output (DCO) correlated negatively with larval volume and positively with that of the molluscan host except in cercariae using ambuscade behaviour. In the Barents Sea cercariae emerged from their molluscan hosts at lower temperatures than in the warmer White Sea but the daily emergence period was prolonged. Thus, DCO of related species were similar in these two seas and comparable with DCO values reported for boreal seas. Local temperature adaptations in cercarial emergence suggests that in case of Arctic climate warming trematode transmission in coastal ecosystems is likely to be intensified not because of the increased summer temperature but because of the prolongation of the warm season favouring cercarial emergence (transmission window).

Dynamic Image Prediction Using Principal Component and Multi-Channel Singular Spectral Analysis: A Feasibility Study

Respiratory motion induces the limit in delivery accuracy due to the lack of the consideration of the anatomy motion in the treatment planning. Therefore, image-guided radiation therapy (IGRT) system plays an essential role in respiratory motion management and real-time tumor tracking in external beam radiation therapy. The objective of this research is the prediction of dynamic time-series images considering the motion and the deformation of the tumor and to compensate the delay that occurs between the motion of the tumor and the beam delivery. For this, we propose a prediction algorithm for dynamic time-series images. Prediction is performed using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA). Using PCA, the motion can be denoted as a vector function and it can be estimated by its principal component which is the linear combination of eigen vectors corresponding to the largest eigen values. Time-series set of 320-detector-row CT images from lung cancer patient and kilovolt (kV) fluoroscopic images from a moving phantom were used for the evaluation of the algorithm, and both image sets were successfully predicted by the proposed algorithm. The accuracy of prediction was quite high, more than 0.999 for CT images, whereas 0.995 for kV fluoroscopic images in cross-correlation coefficient value. This algorithm for image prediction makes it possible to predict the tumor images over the next breathing period with significant accuracy.

Variability of tidal constants in the Río de la Plata estuary associated to the natural cycles of the runoff

We analyze the only long-term (47-years long) hourly tide-gauge record available for the Río de la Plata estuary for variability in the amplitude and phase of the dominant principal lunar semi-diurnal (M2) constituent. The tidal record was first filtered to isolate the signal at the frequency of M2. Then harmonic analysis was applied to the filtered data for periods of one year with progressive lags of one month. This way, monthly time series of M2 amplitude and phase were obtained, which were then analyzed by means of several spectral techniques. Both M2 amplitude and phase present a long-term trend/inter-decadal cycles and significant pseudo-periodicities at frequencies which are known as typical of the El Niño – Southern Oscillation (ENSO) cycles. A joint multi-channel singular spectral analysis of the tidal constants and the runoff to the estuary (which variability is dominated by the ENSO cycles effect on precipitation over southern South America) reveals co-movements at periods around 2.5, 3.5 and 6.5 years, as well as long-term trends/inter-decadal cycles. Positive (negative) runoff anomalies are associated with negative (positive) amplitude anomalies and negative (positive) phase anomalies. The detected variability in the tidal constants seems to be the result of the interaction between the tidal wave propagating upstream and the downstream currents due to the runoff, rather than a result of the variability in the mean sea level caused by the runoff.

T-x frequency filtering of high resolution seismic reflection data using singular spectral analysis

Abstract We develop here an efficient approach using singular spectral analysis (SSA) for frequency filtering of seismic reflection data in t-x domain. The abrupt change in geophysical records creates ringing artifacts in the Fourier based filtering operations. We use here complete data adaptive basis functions in SSA filtering, which enables the self-similarity of the data in reconstruction of such sudden changes. We first tested the SSA based filtering algorithm on synthetic seismic data and then applied to real seismic reflection data from Singareni coalfields, Andhra Pradesh, India. The individual trace from each channel in the shot gathers is processed and compared with Fourier and multichannel SSA filtered output. Our analysis demonstrates that SSA filtering attenuated the low frequency ground role and high frequency noise embedded in the seismic record in a more efficient way than the other two methods. The coal formations and faults identified in the stack section of filtered data match quite well with the geological information available in the study region.

Seasonal Variability of the Tyrrhenian Sea Surface Geostrophic Circulation as Assessed by Altimeter Data

AbstractNew insights into the structure and variability of the Tyrrhenian Sea's surface circulation are obtained through the analysis of a very long series of altimetric observations (1993–2010). In late winter and part of spring, a consistent mean flow is individuated in the eastern Tyrrhenian Sea, formed by a stream of Atlantic water that meanders around four anticyclonic structures located along the Italian coast, which have smaller cyclonic companions offshore. The signatures of these vortices are also found in images of chlorophyll and sea surface temperature, as well as in modeling results, both from a high-resolution operational model of the Tyrrhenian Sea's circulation and from a dedicated numerical simulation. Analysis of the energy exchange between eddies and mean flow, together with numerical evidence, suggests that this winter–spring circulation pattern may result from basin-scale instability of the Atlantic stream. In summer, the dynamic is dominated by a well-known dipole located to the east of the Bonifacio Strait. However, in the eastern part of the basin, an anticyclonic cell is also found, probably driven by the negative wind stress curl present in summer in this region. The cell encompasses two anticyclonic vortices located in the areas of the Vavilov and Marsili Seamounts. A multichannel singular spectral analysis of the altimetric time series reveals that, besides the expected, dominant seasonal mode, a significant low-frequency mode of variability is also present. This mode has a period of about six years and is mostly localized in the western part of the basin.

Spatio‐temporal patterns of pressure over the North Atlantic

AbstractThe North Atlantic mean sea level pressure field variability is analysed. A space–time study is performed using multichannel singular spectral analysis, allowing the detection of significant space–time modes of variability with periodicity behaviour. It is shown that there is a space variability associated with the time variability of the pressure field. The oscillation is not quasi‐meridional but has different orientations, rotating in a cycle, with a periodicity of about 9 years, from the positive North Atlantic oscillation (NAO) phase through the negative NAO phase and again to the positive phase. This periodicity behaviour was previously detected in the temporal principal components extracted from a principal component analysis but, in the time domain, it was found as not significant. Furthermore, the analysis of a long series of an NAO index had already revealed similar periodicity behaviour. Copyright © 2009 Royal Meteorological Society