Mutual information applications to estimate the solar/ geomagnetic signatures in the drought indices in the Danube basin

2020 ◽  
Author(s):  
Ileana Mares ◽  
Venera Dobrica ◽  
Constantin Mares ◽  
Crisan Demetrescu
Keyword(s):  
Mutual Information ◽  
Drought Index ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Drought Indices ◽  
Drought Severity ◽  
Orthogonal Functions ◽  
Danube Basin ◽  
The Difference ◽  
Geomagnetic Activities

<p>The climatic condition for the dry or wet situations from 15 meteorological stations in the Danube basin has been evaluated using four indices: Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Weighted PDSI (WPLM) and Palmer Z-index (ZIND).</p><p>The overall temporal characteristic of the four indices has been analysed by means of the principal component of the Multivariate Empirical Orthogonal Functions decomposition (PC1-MEOF). Also, a simple drought index (TPPI) calculated as the difference between PC1 of the standardized temperature and precipitation, was considered.</p><p>To find the simultaneous influence of both solar and geomagnetic activities on drought indices in the Danube basin, the difference between synergistic and redundant components for each season was estimated, using the mutual information between the analyzed variables. The greater this difference is, the greater the simultaneous signature of the two variables in the drought indices is more significant, than by taking each of the two variables separately.</p><p>The solar activity was highlighted by Wolf numbers for the period 1901-2000 and for 1948-2000 by solar radio flux. For both periods the geomagnetic activity was quantified by the aa index.</p><p>The most significant results for the 100-year period were obtained for the autumn season for which the two predictors representing solar and geomagnetic activities, if considered simultaneously could be one of the causes that produce extreme hydroclimatic events. The analysis from 1948-2000 revealed that the simultaneous consideration of the two external factors is more significant in the summer and autumn time.</p>

scholarly journals On the Nonlinearity of Winter Northern Hemisphere Atmospheric Variability

2019 ◽  
Vol 76 (1) ◽  
pp. 333-356 ◽  
Author(s):  
A. Hannachi ◽  
W. Iqbal
Keyword(s):  
North Atlantic ◽  
Polar Vortex ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
The Arctic ◽  
Climate Change Signal ◽  
Two Dimensional ◽  
Orthogonal Functions ◽  
Local Flow ◽  
The North

Abstract Nonlinearity in the Northern Hemisphere’s wintertime atmospheric flow is investigated from both an intermediate-complexity model of the extratropics and reanalyses. A long simulation is obtained using a three-level quasigeostrophic model on the sphere. Kernel empirical orthogonal functions (EOFs), which help delineate complex structures, are used along with the local flow tendencies. Two fixed points are obtained, which are associated with strong bimodality in two-dimensional kernel principal component (PC) space, consistent with conceptual low-order dynamics. The regimes reflect zonal and blocked flows. The analysis is then extended to ERA-40 and JRA-55 using daily sea level pressure (SLP) and geopotential heights in the stratosphere (20 hPa) and troposphere (500 hPa). In the stratosphere, trimodality is obtained, representing disturbed, displaced, and undisturbed states of the winter polar vortex. In the troposphere, the probability density functions (PDFs), for both fields, within the two-dimensional (2D) kernel EOF space are strongly bimodal. The modes correspond broadly to opposite phases of the Arctic Oscillation with a signature of the negative North Atlantic Oscillation (NAO). Over the North Atlantic–European sector, a trimodal PDF is also obtained with two strong and one weak modes. The strong modes are associated, respectively, with the north (or +NAO) and south (or −NAO) positions of the eddy-driven jet stream. The third weak mode is interpreted as a transition path between the two positions. A climate change signal is also observed in the troposphere of the winter hemisphere, resulting in an increase (a decrease) in the frequency of the polar high (low), consistent with an increase of zonal flow frequency.

A novel index for ecological drought monitoring based on ecological water deficit: a case study of Northwestern China

2021 ◽  
Author(s):  
Tianliang Jiang ◽  
Xiaoling Su
Keyword(s):  
Water Deficit ◽  
Time Scales ◽  
Drought Index ◽  
Vegetation Index ◽  
Standardized Precipitation Index ◽  
Northwestern China ◽  
Drought Monitoring ◽  
Drought Indices ◽  
Drought Severity ◽  
Different Time Scales

<p>Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted drought index for monitoring ecological drought. Therefore, this study constructed a new ecological drought monitoring index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirements and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performances of the SEWDI and four widely-used drought indices [standardized root soil moisture index (SSI), self-calibrated Palmer drought index (scPDSI), standardized precipitation-evaporation drought index (SPEI), and SPI) in monitoring ecological drought were evaluated through comparing the Pearson correlations between these indices and the standardized normalized difference vegetation index (SNDVI) under different time scales, wetness, and water use efficiencies (WUEs) of vegetation. Finally, the rotational empirical orthogonal function (REOF) was used to decompose the SEWDI at a 12-month scale in the NWRC during 1982–2015 to obtain five ecological drought regions. The characteristics of ecological drought in the NWRC, including intensity, duration, and frequency, were extracted using run theory. The results showed that the performance of the SEWDI in monitoring ecological drought was highest among the commonly-used drought indices evaluated under different time scales [average correlation coefficient values (r) between SNDVI and drought indices: SEWDI<sub></sub>= 0.34, SSI<sub></sub>= 0.24, scPDSI<sub></sub>= 0.23, SPI<sub></sub>= 0.20, SPEI<sub></sub>= 0.18), and the 12-month-scale SEWDI was largely unaffected by wetness and WUE. In addition, the results of the monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982–1986, 1990–1996, and 2005–2010, primarily in regions I, II, and V, regions II, and IV, and in region III, IV, and V, respectively. This study provides a robust approach for quantifying ecological drought severity across natural vegetation areas and scientific evidence for governmental decision makers.</p>

scholarly journals Dynamical delimitation of the Central American Dry Corridor (CADC) using drought indices and aridity values

2019 ◽  
Vol 43 (5) ◽  
pp. 627-642 ◽  
Author(s):  
Luis Eduardo Quesada-Hernández ◽  
Oscar David Calvo-Solano ◽  
Hugo G Hidalgo ◽  
Paula M Pérez-Briceño ◽  
Eric J Alfaro
Keyword(s):  
Drought Index ◽  
Pacific Coast ◽  
Potential Evapotranspiration ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Rainfall Anomaly ◽  
Central American ◽  
Drought Indices ◽  
Drought Severity ◽  
Southern Mexico

The Central American Dry Corridor (CADC) is a sub-region in the isthmus that is relatively drier than the rest of the territory. Traditional delineations of the CADC’s boundaries start at the Pacific coast of southern Mexico, stretching south through Central America’s Pacific coast down to northwestern Costa Rica (Guanacaste province). Using drought indices (Standardized Precipitation Index, Modified Rainfall Anomaly Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Palmer Drought Z-Index and the Reconnaissance Drought Index) along with a definition of aridity as the ratio of potential evapotranspiration (representing demand of water from the atmosphere) over precipitation (representing the supply of water), we proposed a CADC delineation that changes for normal, dry and wet years. The identification of areas that change their classification during extremely dry conditions is important because these areas may indicate the location of future expansion of aridity associated with climate change. In the same way, the delineation of the CADC during wet extremes allows the identification of locations that remain part of the CADC even during the wettest years and that may require special attention from the authorities.

scholarly journals Spatial and temporal variability of precipitation and drought in Portugal

2012 ◽  
Vol 12 (5) ◽  
pp. 1493-1501 ◽  
Author(s):  
D. S. Martins ◽  
T. Raziei ◽  
A. A. Paulo ◽  
L. S. Pereira
Keyword(s):  
Principal Component Analysis ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Maximum Temperature ◽  
Drought Indices ◽  
Drought Severity ◽  
Spatial And Temporal Variability ◽  
Monthly Precipitation ◽  
Precipitation Regimes

Abstract. The spatial variability of precipitation and drought are investigated for Portugal using monthly precipitation from 74 stations and minimum and maximum temperature from 27 stations, covering the common period of 1941–2006. Seasonal precipitation and the corresponding percentages in the year, as well as the precipitation concentration index (PCI), was computed for all 74 stations and then used as an input matrix for an R-mode principal component analysis to identify the precipitation patterns. The standardized precipitation index at 3 and 12 month time scales were computed for all stations, whereas the Palmer Drought Severity Index (PDSI) and the modified PDSI for Mediterranean conditions (MedPDSI) were computed for the stations with temperature data. The spatial patterns of drought over Portugal were identified by applying the S-mode principal component analysis coupled with varimax rotation to the drought indices matrices. The result revealed two distinct sub-regions in the country relative to both precipitation regimes and drought variability. The analysis of time variability of the PC scores of all drought indices allowed verifying that there is no linear trend indicating drought aggravation or decrease. In addition, the analysis shows that results for SPI-3, SPI-12, PDSI and MedPDSI are coherent among them.

Physical insights into principal component thermography

2020 ◽  
Vol 62 (5) ◽  
pp. 277-280 ◽  
Author(s):  
K Kaur ◽  
A Sharma ◽  
A Rani ◽  
V Kher ◽  
R Mulaveesala
Keyword(s):  
Principal Component ◽  
Steel Specimen ◽  
Vital Role ◽  
Empirical Orthogonal Functions ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Orthogonal Functions ◽  
Pulsed Thermography ◽  
Physical Insight ◽  
Non Destructive

Among widely used non-destructive testing (NDT) methods, infrared thermography (IRT) has gained importance due to its fast, whole-field, remote and quantitative inspection capabilities for the evaluation of various materials. Being fast and easy to implement, pulsed thermography (PT) plays a vital role in the infrared thermographic community. This paper provides a physical insight into the selection of empirical orthogonal functions obtained from principal component pulsed thermography for the detection of subsurface defects located inside a mild steel specimen.

scholarly journals A Comparison Study of EOF Techniques: Analysis of Nonstationary Data with Periodic Statistics

1999 ◽  
Vol 12 (1) ◽  
pp. 185-199 ◽  
Author(s):  
Kwang-Y. Kim ◽  
Qigang Wu
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Second Mode ◽  
Nonstationary Data ◽  
Cyclic Statistics ◽  
Surface Temperature Field ◽  
Climate Studies ◽  
Moving Patterns

Abstract Identification of independent physical/dynamical modes and corresponding principal component time series is an important aspect of climate studies for they serve as a tool for detecting and predicting climate changes. While there are a number of different eigen techniques their performance for identifying independent modes varies. Considered here are comparison tests of eight eigen techniques in identifying independent patterns from a dataset. A particular emphasis is given to cyclostationary processes such as deforming and moving patterns with cyclic statistics. Such processes are fairly common in climatology and geophysics. Two eigen techniques that are based on the cyclostationarity assumption—cyclostationary empirical orthogonal functions (EOFs) and periodically extended EOFs—perform better in identifying moving and deforming patterns than techniques based on the stationarity assumption. Application to a tropical Pacific surface temperature field indicates that the first dominant pattern and the corresponding principal component (PC) time series are consistent among different techniques. The second mode and the PC time series, however, are not very consistent from one another with hints of significant modal mixing and splitting in some of derived patterns. There also is a detailed difference of intraannual scale between PC time series of a stationary technique and those of a cyclostationary one. This may bear an important implication on the predictability of El Niño. Clearly there is a choice of eigen technique for improved predictability.

Uncertainties in the variation of compound dry and hot events due to differences in drought indices

2021 ◽  
Author(s):  
Sifang Feng ◽  
Zengchao Hao
Keyword(s):  
Palmer Drought Severity Index ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Standardized Precipitation Evapotranspiration Index ◽  
The Past ◽  
Drought Indicators ◽  
The Difference ◽  
Negative Impacts

<p>Compound dry and hot events (CDHEs) are commonly defined as the concurrent or consecutive occurrences of the two events, which could lead to larger negative impacts than do individual extremes. The variation of CDHEs has gained increased attention in the past decades. Previous studies have detected changes in the frequency, duration, and spatial extent at regional and global scales based on observations and model simulations. However, these studies mainly focus on a single drought indicator. In the past decades, different drought indicators have been applied to characterize drought conditions, such as Standardized Precipitation Index (SPI), and Standardized Precipitation-Evapotranspiration Index (SPEI), and Palmer Drought Severity Index (PDSI). Due to the difference in these drought indicators in characterizing droughts, evaluation of CDHEs based on different drought indices may lead to a different magnitude of changes (or even opposite direction of changes). However, quantitative analysis of the uncertainties in the variation of CDHEs is still lacking. In this study, we quantitatively evaluate the uncertainties of CDHEs variations ove global areas due to differences in drought indices. Results from this study could further our understanding of changes in CDHEs under global warming.</p>

scholarly journals Metastable Decomposition of High-Dimensional Meteorological Data with Gaps

2008 ◽  
Vol 65 (11) ◽  
pp. 3479-3496 ◽  
Author(s):  
Illia Horenko ◽  
Stamen I. Dolaptchiev ◽  
Alexey V. Eliseev ◽  
Igor I. Mokhov ◽  
Rupert Klein
Keyword(s):  
Markov Models ◽  
Transition Probabilities ◽  
Meteorological Data ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Metastable States ◽  
High Dimensional ◽  
Observation Data ◽  
Orthogonal Functions ◽  
Mean Values

Abstract This paper presents an extension of the recently developed method for simultaneous dimension reduction and metastability analysis of high-dimensional time series. The modified approach is based on a combination of ensembles of hidden Markov models (HMMs) with state-specific principal component analysis (PCA) in extended space (guaranteeing that the overall dynamics will be Markovian). The main advantage of the modified method is its ability to deal with the gaps in the high-dimensional observation data. The proposed method allows for (i) the separation of the data according to the metastable states, (ii) a hierarchical decomposition of these sets into metastable substates, and (iii) calculation of the state-specific extended empirical orthogonal functions simultaneously with identification of the underlying Markovian dynamics switching between those metastable substates. The authors discuss the introduced model assumptions, explain how the quality of the resulting reduced representation can be assessed, and show what kind of additional insight into the underlying dynamics such a reduced Markovian representation can give (e.g., in the form of transition probabilities, statistical weights, mean first exit times, and mean first passage times). The performance of the new method analyzing 500-hPa geopotential height fields [daily mean values from the 40-yr ECMWF Re-Analysis (ERA-40) dataset for a period of 44 winters] is demonstrated and the results are compared with information gained from a numerically expensive but assumption-free method (Wavelets–PCA), and the identified metastable states are interpreted w.r.t. the blocking events in the atmosphere.

scholarly journals Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

2019 ◽  
Vol 11 (23) ◽  
pp. 2742 ◽  
Author(s):  
Tran ◽  
Tran ◽  
Myint ◽  
Latorre-Carmona ◽  
Ho ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Drought Index ◽  
Negative Impact ◽  
Statistical Significance ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Series Data ◽  
Drought Indices ◽  
Drought Severity

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

Singular systems analysis as a moving-window spectral method

1998 ◽  
Vol 9 (1) ◽  
pp. 55-79 ◽  
Author(s):  
A. C. FOWLER ◽  
G. KEMBER
Keyword(s):  
Systems Analysis ◽  
Singular Systems ◽  
A Priori ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Moving Window ◽  
Window Length ◽  
Sampled Data ◽  
Orthogonal Functions ◽  
Local Filters

Singular Systems Analysis (SSA), or time domain Principal Component Analysis (PCA), is most appropriately analysed in terms of local, moving-window spectral analysis. The behaviour of Empirical Orthogonal Functions (EOF) of this theory are examined, for continuously sampled data, in the limits of large and small window length, and for centre or end projection. Filters obtained by projecting on to these EOFs are shown to approximate local, linear band pass filters, where the EOFs depend upon the correlation structure (or the power spectral density) of the signal and the window length. Power in the spectra is not generally conserved, and projection to the endpoints of a window may not converge to the underlying signal in the absence of noise. The filters are independent of the phase of the Fourier transform, and are therefore unable to distinguish dynamically between a signal and a surrogate (phase-randomized) transform of it. Iteration of such local filters using a prediction error-based stopping criterion can and does lead to improved results, but the choice of window length must be made a priori. Hence, we introduce an iterative local filter with the window length being determined as part of the filtering procedure. This involves the determination of the predictability of the projected time series, and hence allows SSA to be used in a genuinely nonlinear way.

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