scholarly journals Space-time variability of hydrological drought and wetness in Iran using NCEP/NCAR and GPCC datasets

2010 ◽  
Vol 7 (3) ◽  
pp. 3249-3279 ◽  
Author(s):  
T. Raziei ◽  
I. Bordi ◽  
L. S. Pereira ◽  
A. Sutera
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Reanalysis Data ◽  
Space Time ◽  
Climatic Conditions ◽  
Hydrological Drought ◽  
Time Variability ◽  
Good Agreement

Abstract. Space-time variability of hydrological drought and wetness over Iran is investigated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and the Global Precipitation Climatology Centre (GPCC) dataset for the common period 1948–2007. The aim is to complement previous studies on the detection of long-term trends in drought/wetness time series and on the applicability of reanalysis data for drought monitoring in Iran. Climatic conditions of the area are assessed through the Standardized Precipitation Index (SPI) on 24-month time scale, while Principal Component Analysis (PCA) and Varimax rotation are used for investigating drought/wetness variability, and drought regionalization, respectively. Singular Spectrum Analysis (SSA) is applied to the time series of interest to extract the leading nonlinear components and compare them with linear fittings. Differences in drought and wetness area coverage resulting from the two datasets are discussed also in relation to the change occurred in recent years. NCEP/NCAR and GPCC are in good agreement in identifying four sub-regions as principal spatial modes of drought variability. However, the climate variability in each area is not univocally represented by the two datasets: a good agreement is found for south-eastern and north-western regions, while noticeable discrepancies occur for central and Caspian sea regions. A comparison with NCEP Reanalysis II for the period 1979–2007, seems to exclude that the discrepancies are merely due to the introduction of satellite data into the reanalysis assimilation scheme.

scholarly journals Space-time variability of hydrological drought and wetness in Iran using NCEP/NCAR and GPCC datasets

2010 ◽  
Vol 14 (10) ◽  
pp. 1919-1930 ◽  
Author(s):  
T. Raziei ◽  
I. Bordi ◽  
L. S. Pereira ◽  
A. Sutera
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Reanalysis Data ◽  
Space Time ◽  
Hydrological Drought ◽  
Time Variability ◽  
Climate Conditions ◽  
Good Agreement

Abstract. Space-time variability of hydrological drought and wetness over Iran is investigated using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and the Global Precipitation Climatology Centre (GPCC) dataset for the common period 1948–2007. The aim is to complement previous studies on the detection of long-term trends in drought/wetness time series and on the applicability of reanalysis data for drought monitoring in Iran. Climate conditions of the area are assessed through the Standardized Precipitation Index (SPI) on 24-month time scale, while Principal Component Analysis (PCA) and Varimax rotation are used for investigating drought/wetness variability, and drought regionalization, respectively. Singular Spectrum Analysis (SSA) is applied to the time series of interest to extract the leading nonlinear components and compare them with linear fittings. Differences in drought and wetness area coverage resulting from the two datasets are discussed also in relation to the change occurred in recent years. NCEP/NCAR and GPCC are in good agreement in identifying four sub-regions as principal spatial modes of drought variability. However, the climate variability in each area is not univocally represented by the two datasets: a good agreement is found for south-eastern and north-western regions, while noticeable discrepancies occur for central and Caspian sea regions. A comparison with NCEP Reanalysis II for the period 1979–2007, seems to exclude that the discrepancies are merely due to the introduction of satellite data into the reanalysis assimilation scheme.

scholarly journals Observed drought and wetness trends in Europe: an update

2009 ◽  
Vol 13 (8) ◽  
pp. 1519-1530 ◽  
Author(s):  
I. Bordi ◽  
K. Fraedrich ◽  
A. Sutera
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Linear Trend ◽  
Singular Spectrum Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Monthly Precipitation ◽  
Nonlinear Trend ◽  
Nonlinear Trends ◽  
Grid Points

Abstract. Linear and nonlinear trends of drought and wetness are analysed in terms of the gridded Standardized Precipitation Index (SPI) determined from monthly precipitation in Europe (NCEP/NCAR). In characterizing the meteorological and hydrological aspects, the index is computed on a seasonal and on a bi-annual time scale. Two datasets are compared: one from 1949 to 1997 and the other one includes the update of the last decade (to February 2009). The following results are noted: (i) time series of drought and wetness area coverage (number of grid points above/below the severity threshold) show a remarkable linear trend until about the end of the last century, which is reversed in the last (update) decade. This recent trend reversal is an indication of a nonlinear trend, which is more pronounced on the hydrological time scale. (ii) A nonlinear trend analysis is performed based on the time series of the principal component (PC) associated to the first spatial SPI-eigenvector after embedding it in a time delay coordinate system using a sliding window of 70 months (singular spectrum analysis). Nonlinearity appears as a clear feature on the hydrological time scale. (iii) The first spatial EOF-patterns of the shorter and the longer (updated) SPI time series fields show similar structure. An inspection of the SPI time behaviour at selected grid points illustrates the spatial variability of the detected trends.

Vertical crustal deformations and climate variability through PCA and SVD

2021 ◽  
Author(s):  
Letizia Elia ◽  
Susanna Zerbini ◽  
Fabio Raicich
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Space Time ◽  
Time Variability ◽  
Large Network ◽  
Coupled Modes ◽  
Entire Study ◽  
Modes Of Variability ◽  
Multivariate Enso Index ◽  
Enso Index

<p>We investigated a large network of permanent GPS stations to identify and analyse common patterns in the series of the GPS height, environmental parameters, and climate indexes.</p><p>The study is confined to Europe, the Mediterranean, and the North-eastern Atlantic area, where 114 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive. The GPS time series were selected on the basis of the completeness and the length of the series.</p><p>In addition to the GPS height, the parameters analysed in this study are the atmospheric surface pressure (SP), the terrestrial water storage (TWS), and a few climate indexes, such as MEI (Multivariate ENSO Index). The Principal Component Analysis (PCA) is the methodology adopted to extract the main patterns of space/time variability of the parameters.</p><p>Moreover, the coupled modes of space/time interannual variability between pairs of variables was investigated. The methodology adopted is the Singular Value Decomposition (SVD).</p><p>Over the study area, main modes of variability in the time series of the GPS height, SP and TWS were identified. For each parameter, the main modes of variability are the first four. In particular, the first mode explains about 30% of the variance for GPS height and TWS and about 46% for SP. The relevant spatial patterns are coherent over the entire study area in all three cases.</p><p>The SVD analysis of coupled parameters, namely H-AP and H-TWS, shows that most of the common variability is explained by the first 3 modes, which account for almost 80% and 45% of the covariance, respectively.</p><p>Finally, we investigated the relation between the GPS height and a few climate indexes. Significant correlations, up to 50%, were found between the MEI (Multivariate Enso Index) and about half of the stations in the network.</p>

scholarly journals Observed drought and wetness trends in Europe: an update

2009 ◽  
Vol 6 (3) ◽  
pp. 3891-3915 ◽  
Author(s):  
I. Bordi ◽  
K. Fraedrich ◽  
A. Sutera
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Linear Trend ◽  
Singular Spectrum Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Monthly Precipitation ◽  
Nonlinear Trend ◽  
Nonlinear Trends ◽  
Grid Points

Abstract. Linear and nonlinear trends of drought and wetness are analysed in terms of the gridded Standardized Precipitation Index (SPI) determined from monthly precipitation in Europe (NCEP/NCAR), which characterizes the meteorological and hydrological aspects on a seasonal and on a bi-annual time scale, respectively. Two datasets are compared: one from 1949 to 1997 and the other one includes the update of the last decade (to February 2009). The following results are noted: (I) Time series of drought and wetness area coverage (number of grid points above/below the severity threshold) show a remarkable linear trend until about the end of the last century, which is reversed in the last (update) decade. This recent trend reversal is an indication of a nonlinear trend, which is more pronounced on the hydrological time scale. (II) A nonlinear trend analysis is performed based on the time series of the principal component (PC) associated to the first spatial SPI-eigenvector after embedding it in a time delay coordinate system using a sliding window of 70 months (singular spectrum analysis). Nonlinearity appears as a clear feature on the hydrological time scale. (III) The first spatial EOF-patterns of the shorter and the longer (updated) SPI time series fields show similar structure. An inspection of the SPI time behaviour at selected grid points illustrates the spatial variability of the detected trends.

scholarly journals Towards Reliable Velocities of Permanent GNSS Stations

2016 ◽  
Vol 100 (1) ◽  
pp. 17-26
Author(s):  
Janusz Bogusz ◽  
Anna Klos ◽  
Marta Gruszczynska ◽  
Maciej Gruszczynski
Keyword(s):  
Time Series ◽  
Singular Spectrum Analysis ◽  
Principal Component ◽  
North East ◽  
Temporal Correlations ◽  
The North ◽  
Spatio Temporal ◽  
Permanent Station

Abstract In the modern geodesy the role of the permanent station is growing constantly. The proper treatment of the time series from such station lead to the determination of the reliable velocities. In this paper we focused on some pre-analysis as well as analysis issues, which have to be performed upon the time series of the North, East and Up components and showed the best, in our opinion, methods of determination of periodicities (by means of Singular Spectrum Analysis) and spatio-temporal correlations (Principal Component Analysis), that still exist in the time series despite modelling. Finally, the velocities of the selected European permanent stations with the associated errors determined following power-law assumption in the stochastic part is presented.

scholarly journals Identification of the Space-Time Variability of Hydrological Drought in the Arid Region of Northwestern China

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1051 ◽  
Author(s):  
Huaijun Wang ◽  
Zhongsheng Chen ◽  
Yaning Chen ◽  
Yingping Pan ◽  
Ru Feng
Keyword(s):  
Arid Region ◽  
Water Resource Management ◽  
Space Time ◽  
Hydrological Drought ◽  
Northwestern China ◽  
Logistic Distribution ◽  
Time Variability ◽  
Drought Duration ◽  
Monthly Streamflow ◽  
Drought Duration And Severity

Drought monitoring is crucial to water resource management and strategic planning. Thus, the objective of this study is to identify the space-time variability of hydrological drought across the broad arid region of northwestern China. Seven distributions were applied to fitting monthly streamflow records of 16 gauging stations from 10 rivers. Finally, the general logistic distribution was selected as the most appropriate one to compute the Standardized Streamflow Index (SSI). The severity and duration of hydrological droughts were also captured from the SSI series. Moreover, we investigate the relationship between hydrological drought (SSI) and meteorological drought (Standardized Precipitation-Evapotranspiration Index (SPEI)) at different time scales. The results show that drought duration and severity decreased over time in the Aibihu, Irtysh, Kaidu, Aksu, Yarkand, Hoton, Shule, Heihe (upstream), and Shiyang Rivers. However, the Tarim (upstream) and Heihe (middle stream) Rivers showed increasing drought duration and severity and this can be attributed to recent decades human activities. Furthermore, two correlation coefficient patterns between SSI and SPEI were found for the rivers of interest, an “increasing-decreasing” pattern for the Irtysh, Heihe, and Shiyang Rivers, where the precipitation is the main runoff supply, and an “increasing-stable” pattern for Aibihu and the Kaidu, Aksu, Yarkand, Hotan, and Shule Rivers, where glacier melt water provided a relatively high supply of runoff. Our findings are a contribution towards implementing effective water resources evaluation and planning in this arid region.

scholarly journals Low-frequency variability of European runoff

2011 ◽  
Vol 8 (1) ◽  
pp. 1705-1727 ◽  
Author(s):  
L. Gudmundsson ◽  
L. M. Tallaksen ◽  
K. Stahl ◽  
A. K. Fleig
Keyword(s):  
Time Series ◽  
River Flow ◽  
Low Frequency ◽  
Space Time ◽  
Climatic Conditions ◽  
Data Set ◽  
Time Patterns ◽  
Frequency Components ◽  
The Impact ◽  
Precipitation And Temperature

Abstract. This study investigates the low-frequency components of observed monthly runoff in Europe, to better understand the runoff response to long-term variations in the climate system. The relative variance and the dominant space-time patterns of the low-frequency components of runoff were considered, in order to quantify their relative importance and to get insights in to the controlling factors. The analysis of a recently updated European data set of observed streamflow and corresponding time series of precipitation and temperature, showed that the fraction of low-frequency variance of runoff is on average larger than, and not correlated to, the fraction of low-frequency variance of precipitation and temperature. However, it is correlated with catchment properties as well as mean climatic conditions. The fraction of low-frequency variance of runoff decreases for catchments that respond more directly to precipitation. Furthermore, it increases (decreases) under drier (wetter) conditions – indicating that the average degree of catchment saturation may be a primary control of low-frequency runoff dynamics. The dominant space-time patterns of low-frequency runoff, identified using nonlinear dimension reduction, revealed that low-frequency runoff can be described with three modes, explaining together 80.6% of the variance. The dominant mode has opposing centers of simultaneous variations in northern and southern Europe. The secondary mode features a west-east pattern and the third mode has its centre of influence in central Europe. All modes are closely related to the space-time patterns extracted from time series of precipitation and temperature. In summary, it is shown that the dynamics of low-frequency runoff follows large-scale atmospheric features, whereas the proportion of variance attributed to low-frequency fluctuations is controlled by catchment processes and varies with the mean climatic conditions. The results may have implications for interpreting the impact of changes in temperature and precipitation on river-flow dynamics.

scholarly journals Electricity consumption forecasting using singular spectrum analysis

DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 138-146 ◽  
Author(s):  
Moises Lima de Menezes ◽  
Reinaldo Castro Souza ◽  
José Francisco Moreira Pessanha
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Computational Experiment ◽  
Singular Spectrum Analysis ◽  
Electricity Consumption ◽  
Principal Component ◽  
Graphical Analysis ◽  
Singular Spectrum ◽  
Trend Extraction ◽  
Residential Consumption

Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil.

scholarly journals Identifying Convectively Coupled Equatorial Waves Using Theoretical Wave Eigenvectors

2016 ◽  
Vol 144 (6) ◽  
pp. 2235-2264 ◽  
Author(s):  
H. Reed Ogrosky ◽  
Samuel N. Stechmann
Keyword(s):  
Longwave Radiation ◽  
Warm Pool ◽  
Reanalysis Data ◽  
Space Time ◽  
Boreal Summer ◽  
Equatorial Waves ◽  
Convectively Coupled Equatorial Waves ◽  
The Pacific ◽  
Potential Applications ◽  
Good Agreement

Abstract Convectively coupled equatorial waves (CCEWs) are often identified by space–time filtering techniques that make use of the eigenvalues of linear shallow water theory. Here, instead, a method is presented for identifying CCEWs by projection onto the eigenvectors of the theory. This method does not use space–time filtering; instead, wave signals corresponding to the first baroclinic Kelvin, Rossby, and mixed Rossby–gravity (MRG) waves are constructed from reanalysis data by a series of projections onto (i) vertical and meridional modes and (ii) the wave eigenvectors. In accordance with the theory, only dry variables, that is, winds and geopotential height, are used; no proxy for convection is used. Using lag–lead regression, composites of the structures associated with each eigenvector signal during boreal summer are shown to contain all the features of the theory as well as some additional features seen in previous observational studies, such as vertical tilts. In addition, these composites exhibit propagation in good agreement with the theory in certain regions of the tropics: over the eastern Pacific ITCZ for the Kelvin and MRG composites and over the Pacific warm pool for the Rossby composite. In these respective regions, the Kelvin eigenvector signal is also in good agreement with space–time-filtered outgoing longwave radiation (OLR), and the Rossby and MRG eigenvector signals are in reasonable agreement with space–time-filtered OLR; it is shown that the eigenvector projections used here contribute to this agreement. Finally, a space–time-filtered version of the eigenvector projection is briefly discussed, as are potential applications of the method.

ADAPTIVE FILTERING AND PREDICTION OF NOISY MULTIVARIATE SIGNALS: AN APPLICATION TO SUBANNUAL VARIABILITY IN ATMOSPHERIC ANGULAR MOMENTUM

1993 ◽  
Vol 03 (03) ◽  
pp. 625-634 ◽  
Author(s):  
CHRISTIAN L. KEPPENNE ◽  
MICHAEL GHIL
Keyword(s):  
Time Series ◽  
Angular Momentum ◽  
Multivariate Time Series ◽  
Singular Spectrum Analysis ◽  
Predictive Ability ◽  
Principal Component ◽  
Entropy Method ◽  
Empirical Orthogonal Functions ◽  
Atmospheric Angular Momentum ◽  
Orthogonal Functions

Principal component analysis (PCA) in the space and time domains is applied to filter adaptively the dominant modes of subannual (SA) variability of a 12-year long multivariate time series of Northern Hemisphere atmospheric angular momentum (AAM); AAM is computed in 23 latitude bands of equal area from operational analyses of the U.S. National Meteorological Center. PCA isolates the leading empirical orthogonal functions (EOFs) of spatial dependence, while multivariate singular spectrum analysis (M-SSA) yields filtered time series that capture the dominant low-frequency modes of SA variability. The time series prefiltered by M-SSA lend themselves to prediction by the maximum entropy method (MEM). Whole-field predictions are made by combining the forecasts so obtained with the leading spatial EOFs obtained by PCA. The combination of M-SSA and MEM has predictive ability up to about a month. These methods are essentially linear but data-adaptive. They seem to perform well for short, noisy, multivariate time series, to which purely nonlinear, deterministically based methods are difficult to apply.

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