Chapter Introduction
|
|
- Ralph Porter
- 5 years ago
- Views:
Transcription
1 Chapter Introduction Time series analysis approach for analyzing and understanding real world problems such as climatic and financial data is quite popular in the scientific world (Addison (2002), Feder (1988), Kumar and Foufoula (1993a), Kumar and Foufoula (1997), Lafreniere and Sharp (2003), Mandelbrot and Hudson (2004), Meyer (1998), Rangarajan and Sant (2004)). Till a decade ago, statistical and Fourier analysis methods were quite popular for studying the behavior of time series climatic data. However, recently wavelet and fractal methods are applied for a better understanding of the behavior of such series (Addison (2002), Arneodo et al. (2003), Hu and Nitta (1996), Kulkarni (2000), Kumar and Foufoula (1993b, 1993c), Mallat (1999), Rangarajan and Sant (2004), Turiel et al. (2006)). India is the biggest democratic country whose economy and political system is closely related to agriculture. Agriculture production is mainly dependent on rainfall particularly during rainy season June-July-August-September (abbreviated as JJAS) and winter season October- November-December (abbreviated as OND). In this context months January and February will be abbreviated as JF. Months March-April and May will be abbreviated as MAM. This has been the topic of intense research for more than 100 years applying classical techniques; see for example Singh and Sontakke (1999). The main objective of the work in this chapter is to present the work by Manchanda, Kumar, Khene and Siddiqi on the study of Indian rainfall data during 1813 to 1995 by applying wavelets and wavelet based multifractal formalism. This chapter has been divided into four sections. In the first section above, we have reviewed the work done by various researchers to study the behavior of time series by using wavelet and fractal methods. Section two is divided into five subsections. In the first subsection we discuss the concepts of multifractal, singularity, self-affine function, Hurst exponent and its connection to fractal dimension. In the next three subsections, we have discussed singularity spectrum, Legendre transform, partition function and numerical procedure for calculating the singularity spectrum. Smooth perturbations are discussed in the last subsection. The third section deals with the 94
2 analysis of rainfall data. We have divided this section into four subsections. In the first subsection, we present the wavelet analysis of Indian rainfall data (annual as well as seasonal) with db2, db3 and Coif 5 wavelets. In the second subsection multifractal analysis of Indian rainfall data is performed to calculate box dimension and regularization dimension for different seasons. In the third subsection regularity analysis of the data under consideration is performed. For the regularity analysis we have used wavelet transform based parametric and non parametric approach. The fourth subsection deals with the wavelet based multifractal analysis of the rainfall time series data using Morlet wavelet. Conclusions to the analysis are made in the last section this chapter. This work presented in this chapter is published in Manchanda, P. et al. (2007a) Preliminaries Wavelet Based Multifractal Formalism Sharp signal transitions create large amplitude wavelet coefficients. Singularities are detected by following across scales the local maxima of the wavelet transform. Fractals describe objects that are too irregular to fit into traditional geometrical settings. Various phenomena display fractal features when plotted as functions of time. Some common examples are atmospheric pressure, water level in a reservoir and prices of the stock market, usually when recorded over fairly long time spans. The zooming capability of the wavelet transform not only locates isolated singular events, but can also characterize more complex multifractal signals having non isolated singularities. Multifractals are fractal objects which cannot be completely described using a single fractal dimension (monofractals).they have in fact an infinite number of dimension measures associated with them. The wavelet transform takes advantage of multifractal self-similarities, in order to compute the distribution of their singularities. This singularity spectrum is used to analyze multifractal properties. Signals that are singular at almost every point are multifractals and they appear in the maintenance of economic records, physiological data including heart records, electromagnetic fluctuations in galactic radiation noise, textures in images of natural terrain and variations of traffic flow, etc. 95
3 Concepts of fractals and multifractals and their relevance to the real world systems were introduced by the Mandelbrot and Hudson (2004). In many real world systems, represented by time series, understanding of pattern of the singularities, that is, the graph of points at which time series changes abruptly is quite a challenging task. The time series of rain fall data usually depict fractal or multifractal features. Time series are commonly called self-affine functions as their graphs are self-affine sets that are similar to themselves when transformed by anisotropic dilations, that is., when shrinking along the x-axis by a factor followed by the rescaling of the increments of the function by different factor. Mathematically, if ( ) is a self-affine function then ( ) ( ) ( ( ) ( )) ( ) The exponent here is called roughness or Hurst exponent. Note that if, then is not differentiable and smaller the exponent, the more singular is. Hurst exponent provides an indication of how globally irregular the function is. Indeed it is related to the fractal dimension as. For estimation and properties of the Hurst parameter or exponent we refer to Addison (2002), Feder (1988), Mandelbrot and Hudson (2004), Mallat (1999) and Siddiqi (2005). Fractal functions can posses multi-affine properties in the sense that their roughness (or regularity) may fluctuate from point to point. To describe these multifractal functions, one thus needs to change slightly the definition of the Hurst regularity of so that it becomes a local quantity: ( ) ( ) ( ) ( ) This local Hurst exponent ( ) is generally called H lder exponent of at the point. A more precise mathematical formulation can be given as: To a given signal ( ) a function ( ), the Holder function of, which measures the regularity of at each point is associated. The point wise H lder of at point is defined as: ( ) * ( ) ( ) + ( ) (Here is not an integer and is non-differential). 96
4 One may also define a local exponent ( ) as: ( ) * ( ) ( ) + ( ) where and are different in general. For example for ( ) ( ) ( ) They have quite different properties. For instance is stable through differentiation ( ( ) ( ) ), whereas is not. The smaller ( ) is, the more irregular the function is at. Fraclab and Benoit software can be used to estimate H lder exponent. H lder exponent is often called Lipschitz exponent. We now recapitulate in the following subsections, some concepts and results which are required in the multifractal analysis of the rainfall time series Singularity Spectrum The pattern of singularities (singularity spectrum) is a graph of points at which a time series changes abruptly. The singularity spectrum measures the global repartition of singularities having different Lipschitz regularity. We recall that if is the set of all points where the pointwise Lipschitz regularity of is equal to, the spectrum of singularity, ( ), of is the fractal dimension of. The support of ( ) is the set of such that is not empty. The singularity spectrum gives the proportion of Lipschitz singularities that appear at any scale Partition Function Pointwise Lipschitz (H lder) regularity of a multifractal can not be computed because its singularities are not isolated, and the finite numerical resolution is not sufficient to discriminate them. To overcome this difficulty Arneodo, Bacry and Muzy (Muzy et al. (1994)) have introduced the concept of wavelet transform modulus maximum using a global partition function (for updated references related to this method see Arneodo et al. (2003)). 97
5 Let be a wavelet with vanishing moments. Mallat has shown that if has a pointwise Holder (Lipschitz) regularity at then the wavelet transform ( ) has a sequence of modulus maxima that converges towards at fine scales. The set of maxima at the scale can thus be interpreted as a covering of the singular support of with wavelets of scale. At these maxima locations ( ) ( ) Let { ( )} be the position of all local maxima of ( ) at a fixed scale. The partition function measures the sum at a power of all these wavelet modulus maxima: ( ) ( ) ( ) For each the scaling exponent ( ) measures the asymptotic decay of ( ) at fine scales : ( ) ( ) ( ) This typically means that ( ) ( ) We recall the following theorems which relates ( ) to the Legendre transform of ( ) for self-similar signals. Theorem was established in Bacry et al. (1993) for a particular class of fractal signals and generalized by Jaffard (1997). Theorem (Arneodo, Bacry, Jaffard, Muzy) (Mallat (1999)) Let [ ] be the support of ( ) Let be a wavelet with vanishing moments. If is a self similar signal then ( ) ( ( ) ( )) ( ) Theorem (Mallat (1999)) (a) The scaling exponent ( ) is a convex and increasing function of. (b) The Legendre transform (4.8) is invertible if and only if ( ) is convex, in which case ( ) ( ( ) ( )) ( ) 98
6 The spectrum ( ) of self-similar signals is convex. Theorem (Mallat (1999)) Let ( ) where Gaussian. For any ( ), the modulus maxima of ( ) belong to connected curves that are never interrupted when the scale decreases. For detailed properties of singularities spectrum we refer to Meyer (1998). We first calculate ( ) ( ) then derive the decay scaling component ( ), and finally compute ( ) with a Legendre transform. If then the value of ( ) depends mostly on the small amplitude maxima ( ) Procedure for Numerical Calculation of Singularity Spectrum ( ) 1. Compute maxima ( ) and the maximum of its absolute value at each scale. 2. Chain the wavelet maxima across scales. 3. Compute the partition function ( ) ( ) ( ) 4. Compute the scaling coefficient ( ) with a linear regression of ( ) as a function of ( ) i.e, ( ) ( ) ( ) ( ) 5. Compute the singularity spectrum ( ) ( ( ) ( )) ( ) Smooth Perturbations Let be a multifractal whose spectrum of singularity ( ) is calculated from ( ). If a regular signal is added to then the singularities are not modified and the singularity spectrum of remains unchanged. We study the effect of this smooth perturbation on the spectrum calculation. The wavelet transform of is ( ) ( ) ( ) ( ) 99
7 Let ( ) and ( ) be the scaling exponent of the partition functions ( ) and ( ) calculated from the modulus maxima respectively of ( ) and ( ). The following theorem relates ( ) and ( ) Theorem (Arneodo, Bacry, Muzy) (Mallat (1999)) Let be a wavelet with exactly vanishing moments. Suppose that is a self similar function. If is a polynomial of degree then ( ) ( ) for all If ( ) is almost everywhere non zero then ( ) ( ) {( ) (4.14) where is defined by ( ) ( ) (4.15) 4.3. Analysis In this section we have performed wavelet analysis, multifractal analysis, regularity analysis and wavelet based multifractal analysis of Indian rainfall data Wavelet Analysis of Indian Rainfall Data Long period instrumental series are vital in studies on climate variation, its modeling, monitoring and prediction. Longest seasonal and annual Indian rainfall series have been reconstructed from the past records over different spatial rainfall zones and for the country as a whole. Kumar et al. (2006) have studied Indian rainfall series from by applying MATLAB wavelet toolbox. Wavelet analysis of the association between southern oscilation and the Indian summer monsoon is discussed by Kulkarni (2000). Wavelet analysis of the summer rainfall over Northern China and India has been carried out by Hu and Nitta (1996). In this section, we visualize time series of the Indian rain fall (seasonal and annual) through the microscope of different wavelets. The discrete wavelet analysis of the Indian meteorological time series data is carried out in terms of decomposition of different rainy seasons in terms of approximations and details. The decomposition analysis of different four rainy seasons as well as the 100
8 annual rainfall data is shown in figures In these figures, the x-axis represents the time period of the data under consideration. Each of these figures consists of six parts. The first figure (on left top) representing original signal for the rainfall time series data, the second one (on right top) gives the approximation. This approximation part corresponds to the amplitude of the signal for respective wavelets used at level 4. The other four parts and represent detail of the signal. Figure 1: DWT decomposition of Jan-Feb season rainfall (Daubechies 2 wavelet). The discrete wavelet analysis for the first two seasons Jan-Feb and March-April-May is performed using two different wavelets db2 and Coif 5 (Fig. 1-4), both at level 4. The rainfall signal for the next two seasons JJAS (June-July-August-September) and OND (Oct-Nov-Dec) as well as annual data ( ) is analyzed using two different wavelets db3 and Coif 5 at level 4 (Fig. 5-10). 101
9 Figure 2: DWT decomposition of Jan-Feb season rainfall (Coifman 5 wavelet). Figure 3: DWT decomposition of March-April-May season rainfall (Daubechies 2 wavelet). 102
10 Figure 4: DWT decomposition of Mar-Apr-May season rainfall (Coifman 5 wavelet). Figure 5: DWT decomposition of Jun-July-Aug-Sept season rainfall (Daubechies 3 wavelet). 103
11 Figure 6: DWT decomposition of Jun-July-Aug-Sept season rainfall (Coifman 5 wavelet). Figure 7: DWT decomposition of Oct-Nov-Dec season rainfall (Daubechies 3 wavelet). 104
12 Figure 8: DWT decomposition of Oct-Nov-Dec season rainfall (Coifman 5 wavelet). Figure 9: DWT decomposition of annual rainfall ( ) (Daubechies 3 wavelet). 105
13 Figure 10: DWT decomposition of annual rainfall ( ) (Coifman 5 wavelet) Multifractal Analysis of Indian Rainfall Data Fractal dimensions are the best known part of the fractal analysis. Various kinds of dimensions have been defined in different fields. In the wavelet based multifractal study of the Indian rainfall, we only focus on two types of dimension, namely the box dimension and the regularization dimension. These dimensions aim at approximating the more formal Hausdorff dimension. The regularization dimension is estimated by first computing smoother and smoother versions of the original signal, obtained through convolution with a kernel. If the original signal is a fractal, its graph has infinite length, while all regularized versions have finite length. When the smoothing parameter tends to zero, the smoothed version tends to the original signal, and its length will tend to infinity. The regularization dimension measures the speed at which this convergence to infinity takes place. In many cases, this coincides with the usual box dimension. In general, it can be shown that the regularization dimension is more precise than the box dimension, in the sense that it is always smaller, but still larger 106
14 than Hausdorff dimension. In addition, the regularization dimension lends to more robust estimation procedures for various reasons. One of them is that we may choose the regularization kernel. Also, the smoothed versions are adaptive by construction. Finally, the smoothing parameter can be varied in very small steps, as box sizes have to undergo sudden changes. Another advantage is that, due to the fully analytical definition of the regularization dimension, it is easy to derive an estimator in the presence of noise. Table 1 Box vs. regularization dimensions for seasonal and annual rainfall data Jan-Feb MAM JJAS OND Annual Box method (least squares regression) Regularization method Kernel : Gaussian Regression : Least Squares Regularity Analysis In this section we estimate the exponents that arise constantly in the multifractal analysis of signals to assess the regularity of a time series. The most used exponent is the Hölder exponent, which characterizes the regularity of the measure/function under consideration at either any given point (pointwise regularity) or around any given point (local regularity). Many techniques have been developed to estimate pointwise (and local) Hölder exponents, none of which give satisfactory results in all cases. Estimating a local regularity indeed on discrete data without any a priori assumption is indeed a difficult task. This can be done using: 1. Parametric approach. Such estimators have been developed mainly in case of Brownian motion (bm) and its extensions such as fractional Brownian motion (fbm). 2. Non Parametric Methods, which are more numerous and generally give the correct estimation only when some technical conditions are met. We focus on the following methods: (a) Method based on the continuous wavelet transform (CWT). (b) Method based on the discrete wavelet transform (DWT). 107
15 Figures 11 to 13 show the estimation of point wise Hölder regularity of the Indian rainfall data for the period for annual as well as seasonal data. We used the parametric approach codes using Fraclab. The results of the non parametric approach using the DWT for four different wavelets (db2, db20, Coif 6, Coif 24) are shown in figures Figure 11: Parametric pointwise Hölder regularity estimation for the annual rainfall ( ). 108
16 Figure 12: Parametric pointwise Hölder regularity estimation for the first two seasons. Figure 13: Parametric pointwise Hölder regularity estimation for the last two seasons. 109
17 Figure 14: Annual non-parametric point wise Hölder regularity estimation using DWT with two different wavelets. Figure 15: Non-parametric point wise Hölder regularity estimation using DWT with two different wavelets (db2 and db20) for the first two seasons Jan-Feb and Mar-Apr- May. 110
18 Figure 16: Non-parametric point wise Hölder regularity estimation using DWT with two different wavelets (db2 and db20) for the last two seasons JJAS and OND Wavelet Based Multifractal Analysis Various phenomena (data) show fractal and multifractal behavior when plotted against time. Multifractals have infinite number of dimensions associated with them. Signals which are multifractals, are singular (i.e. changes abruptly) at almost every point. There exist three main multifractal spectra, viz. the Hausdroff, large deviation and Legendre spectra. Basically, any of these three spectra provides information as to which singularities occur in the signal, and which are dominant: a spectrum is a one dimensional curve where abscissa represents the Hölder exponents actually present in the signal, and ordinates are related to the amount of points where we encounter a given singularity. The Hausdroff spectrum gives geometrical information pertaining to the dimension of sets of points in a signal having a given Hölder exponent. This is the most precise spectrum from a mathematical point of view, but is also the most difficult one to estimate. 111
19 Large deviation spectrum yields statistical information, related to the probability of finding a point with a given Holder exponent in the signal. More precisely, it measures how this probability behaves with the change in resolution. Legendre spectrum is a concave approximation to the large deviation spectrum. Its main purpose is to yield much more robust estimates, though at the expense of a loss of information. It could be based on box method or CWT techniques. In the sequel we show some sample results for the spectra computed with the Legendre technique (see theorems and ). Figures 17 to 24 show the results of the CWT (Morlet wavelet) based estimation of the Legendre spectrum which represents an approximation of the Hausdroff spectrum for the four different seasons. Figures 25 and 26 represent the results for the annual rainfall. Figure 17: CWT of Jan-Feb season rainfall using a Morlet wavelet of size 8 and 128 voices. Each of the figures 17, 19, 21, 23, 25 consists of two parts. The first part on the top of each of these figures represents the signal or raw data. The second part of these figures shows the analysed pattern with the application of Morlet wavelet of size 8 and 128 voices. Figures 18, 20, 22, 24 show the Legendre spectrum estimation by the 112
20 application of CWT (Morlet wavelet of size 8, 128 voices) of four different seasons. Figure 26 shows multifractal analysis of the annual rainfall data. Figure 18: Multifractal analysis of Jan-Feb rainfall data using CWT (Morlet wavelet of size 8 and 128 voices, LS regression and local maxima). Figure 19: CWT of March-April-May season rainfall using a Morlet wavelet of size 8 and 128 voices. 113
21 Figure 20: Multifractal analysis of March-April-May rainfall data using CWT (Morlet wavelet of size 8 and 128 voices, LS regression and local maxima). Figure 21: CWT of June-July-Aug-Sept season rainfall using a Morlet wavelet of size 8 and 128 voices. 114
22 Figure 22: Multifractal analysis of June-July-Aug-Sept rainfall data using CWT (Morlet wavelet size 8, 128 voices, LS regression and local maxima). Figure 23: CWT of Oct-Nov-Dec season rainfall using a Morlet wavelet of size 8 and 128 voices. 115
23 Figure 24: Multifractal analysis of Oct-Nov-Dec rainfall data using CWT (Morlet wavelet of size 8, 128 voices, LS regression and local maxima). Figure 25: CWT of the annual rainfall using a Morlet wavelet of size 8 and 128 voices. 116
24 Figure 26: Multifractal analysis of the annual rainfall data using CWT (Morlet wavelet of size 8, 128 voices, LS regression and local maxima) Conclusion Wavelet analysis provided visualization of the real life data at different levels. Fig. 1 to 10 provides decomposition of seasonal and annual Indian rainfall data for the period at different levels through applications of Daubechies 3 and Coifman 5 wavelets. Wavelet toolbox of MATLAB has been used. Multifractal analysis provides both a local and a global description of the singularities of a signal. The local description is obtained via the Hölder exponent where as the global one is contained in various multifractal spectra (Addison (2002), Feder (1988), Mallat (1999), Mandelbrot and Hudson (2004) and Turiel et al. (2006)). Table 1 presents fractal dimension of seasonal and annual rainfall data. Figs. 11 to 13 represent the pointwise regularity of Indian rainfall data for the period for annual as well as seasonal data using the parametric approach codes using Fraclab. The results of the non-parametric approach using the DWT for four different wavelets are shown in Figs. 14 to 16. Figs. 17 to 26 represent singularity spectrum for the seasonal as well as annual rainfall estimated using Morlet wavelet. The results of this work provide a general view of the pattern of Indian rainfall data of different seasons along with the 117
25 annual pattern. Whether we can connect these results with predictability indices studied by Rangarajan and Sant (2004) is an open problem. By applying results of Table 1, we can estimate the Hurst exponent of the data under consideration using relation. On the basis of the values of H, persistency of the data can be predicted (We know a datum is persistent if ( ). ************* 118
The Influence of Solar Activity on the Rainfall over India: Cycle-to-Cycle Variations
J. Astrophys. Astr. (2006) 27, 367 372 The Influence of Solar Activity on the Rainfall over India: Cycle-to-Cycle Variations K. M. Hiremath Indian Institute of Astrophysics, Bangalore 560 034, India. e-mail:
More informationWHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and Rainfall For Selected Arizona Cities
WHEN IS IT EVER GOING TO RAIN? Table of Average Annual Rainfall and 2001-2002 Rainfall For Selected Arizona Cities Phoenix Tucson Flagstaff Avg. 2001-2002 Avg. 2001-2002 Avg. 2001-2002 October 0.7 0.0
More informationInterannual variation of MODIS NDVI in Lake Taihu and its relation to climate in submerged macrophyte region
Yale-NUIST Center on Atmospheric Environment Interannual variation of MODIS NDVI in Lake Taihu and its relation to climate in submerged macrophyte region ZhangZhen 2015.07.10 1 Outline Introduction Data
More informationWAVELET TRANSFORMS IN TIME SERIES ANALYSIS
WAVELET TRANSFORMS IN TIME SERIES ANALYSIS R.C. SINGH 1 Abstract The existing methods based on statistical techniques for long range forecasts of Indian summer monsoon rainfall have shown reasonably accurate
More informationDROUGHT IN MAINLAND PORTUGAL
DROUGHT IN MAINLAND Ministério da Ciência, Tecnologia e Ensino Superior Instituto de Meteorologia, I. P. Rua C Aeroporto de Lisboa Tel.: (351) 21 844 7000 e-mail:informacoes@meteo.pt 1749-077 Lisboa Portugal
More informationChanging Hydrology under a Changing Climate for a Coastal Plain Watershed
Changing Hydrology under a Changing Climate for a Coastal Plain Watershed David Bosch USDA-ARS, Tifton, GA Jeff Arnold ARS Temple, TX and Peter Allen Baylor University, TX SEWRU Objectives 1. Project changes
More informationTABLE -I RAINFALL RECORDED AT PORT BLAIR (MM) FROM 1949 TO 2009
A. RAINFALL TABLE -I RAINFALL RECORDED AT PORT BLAIR (MM) FROM 1949 TO 2009 MONTH/YEAR 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 JANUARY 0.0 0.8 82.5 0.0 26.9 37.3 71.4 46.2 10.2 28.7 FEBRUARY
More informationAnalysis on Characteristics of Precipitation Change from 1957 to 2015 in Weishan County
Journal of Geoscience and Environment Protection, 2017, 5, 125-133 http://www.scirp.org/journal/gep ISSN Online: 2327-4344 ISSN Print: 2327-4336 Analysis on Characteristics of Precipitation Change from
More informationAnalysis of Historical Pattern of Rainfall in the Western Region of Bangladesh
24 25 April 214, Asian University for Women, Bangladesh Analysis of Historical Pattern of Rainfall in the Western Region of Bangladesh Md. Tanvir Alam 1*, Tanni Sarker 2 1,2 Department of Civil Engineering,
More informationStatistical Analysis of Temperature and Rainfall Trend in Raipur District of Chhattisgarh
Current World Environment Vol. 10(1), 305-312 (2015) Statistical Analysis of Temperature and Rainfall Trend in Raipur District of Chhattisgarh R. Khavse*, R. Deshmukh, N. Manikandan, J. L Chaudhary and
More informationSINGULARITY DETECTION AND PROCESSING WITH WAVELETS
SINGULARITY DETECTION AND PROCESSING WITH WAVELETS Stephane Mallat and Wen Liang Hwang Courant Institute of Mathematical Sciences New York University, New York, NY 10012 Technical Report March 1991 Abstract
More informationBMKG Research on Air sea interaction modeling for YMC
BMKG Research on Air sea interaction modeling for YMC Prof. Edvin Aldrian Director for Research and Development - BMKG First Scientific and Planning Workshop on Year of Maritime Continent, Singapore 27-3
More informationAgricultural Science Climatology Semester 2, Anne Green / Richard Thompson
Agricultural Science Climatology Semester 2, 2006 Anne Green / Richard Thompson http://www.physics.usyd.edu.au/ag/agschome.htm Course Coordinator: Mike Wheatland Course Goals Evaluate & interpret information,
More informationEvaluation of solar fraction on north partition wall for various shapes of solarium by Auto-Cad
Evaluation of solar fraction on north partition wall for various shapes of solarium by Auto-Cad G.N. Tiwari*, Amita Gupta, Ravi Gupta Centre for energy studies, Indian Institute of technology Delhi, Rauz
More informationInternational Journal of Advanced Research in Computer Science and Software Engineering
Volume 4, Issue 4, April 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Wavelet-based
More informationINTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 2, No 1, Copyright 2010 All rights reserved Integrated Publishing Association
INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCES Volume 2, No 1, 2011 Copyright 2010 All rights reserved Integrated Publishing Association Research article ISSN 0976 4402 Prediction of daily air pollution
More informationS most important information in signals. In images, the
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 38, NO. 2, MARCH 1992 617 Singularity Detection and Processing with Wavelets Stephane Mallat and Wen Liang Hwang Abstract-Most of a signal information is often
More informationSeasonal Climate Watch September 2018 to January 2019
Seasonal Climate Watch September 2018 to January 2019 Date issued: Aug 31, 2018 1. Overview The El Niño-Southern Oscillation (ENSO) is still in a neutral phase and is still expected to rise towards an
More informationChiang Rai Province CC Threat overview AAS1109 Mekong ARCC
Chiang Rai Province CC Threat overview AAS1109 Mekong ARCC This threat overview relies on projections of future climate change in the Mekong Basin for the period 2045-2069 compared to a baseline of 1980-2005.
More informationGAMINGRE 8/1/ of 7
FYE 09/30/92 JULY 92 0.00 254,550.00 0.00 0 0 0 0 0 0 0 0 0 254,550.00 0.00 0.00 0.00 0.00 254,550.00 AUG 10,616,710.31 5,299.95 845,656.83 84,565.68 61,084.86 23,480.82 339,734.73 135,893.89 67,946.95
More informationarxiv:cond-mat/ v1 [cond-mat.stat-mech] 15 Feb 1999
Fractional Brownian Motion Approximation arxiv:cond-mat/9902209v1 [cond-mat.stat-mech] 15 Feb 1999 Based on Fractional Integration of a White Noise A. V. Chechkin and V. Yu. Gonchar Institute for Theoretical
More informationClimate Change Scenarios 2030s
Climate Change Scenarios 2030s Ashwini Kulkarni ashwini@tropmet.res.in K Krishna Kumar, Ashwini Kulkarni, Savita Patwardhan, Nayana Deshpande, K Kamala, Koteswara Rao Indian Institute of Tropical Meteorology,
More informationGeostatistical Analysis of Rainfall Temperature and Evaporation Data of Owerri for Ten Years
Atmospheric and Climate Sciences, 2012, 2, 196-205 http://dx.doi.org/10.4236/acs.2012.22020 Published Online April 2012 (http://www.scirp.org/journal/acs) Geostatistical Analysis of Rainfall Temperature
More informationAtmospheric circulation analysis for seasonal forecasting
Training Seminar on Application of Seasonal Forecast GPV Data to Seasonal Forecast Products 18 21 January 2011 Tokyo, Japan Atmospheric circulation analysis for seasonal forecasting Shotaro Tanaka Climate
More informationAnalysis of Rainfall and Other Weather Parameters under Climatic Variability of Parbhani ( )
International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 7 Number 06 (2018) Journal homepage: http://www.ijcmas.com Original Research Article https://doi.org/10.20546/ijcmas.2018.706.295
More informationPROJECT REPORT (ASL 720) CLOUD CLASSIFICATION
PROJECT REPORT (ASL 720) CLOUD CLASSIFICATION SUBMITTED BY- PRIYANKA GUPTA 2011CH70177 RINI KAPOOR 2011CH70179 INDIVIDUAL CONTRIBUTION- Priyanka Gupta- analysed data of region considered in India (West:80,
More informationMISSION DEBRIEFING: Teacher Guide
Activity 2: It s Raining Again?! Using real data from one particular location, students will interpret a graph that relates rainfall to the number of cases of malaria. Background The relationship between
More informationMULTIFRACTALBEHAVIOURINNATURALGASPRICESBYUSINGMF-DFAANDWTMMMETHODS
Global Journal of Management and Business Research Finance Volume 13 Issue 11 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA)
More informationSierra Weather and Climate Update
Sierra Weather and Climate Update 2014-15 Kelly Redmond Western Regional Climate Center Desert Research Institute Reno Nevada Yosemite Hydroclimate Workshop Yosemite Valley, 2015 October 8-9 Percent of
More informationTIME SERIES ANALYSIS AND FORECASTING USING THE STATISTICAL MODEL ARIMA
CHAPTER 6 TIME SERIES ANALYSIS AND FORECASTING USING THE STATISTICAL MODEL ARIMA 6.1. Introduction A time series is a sequence of observations ordered in time. A basic assumption in the time series analysis
More informationChampaign-Urbana 2001 Annual Weather Summary
Champaign-Urbana 2001 Annual Weather Summary ILLINOIS STATE WATER SURVEY 2204 Griffith Dr. Champaign, IL 61820 wxobsrvr@sws.uiuc.edu Maria Peters, Weather Observer January: After a cold and snowy December,
More informationANNUAL CLIMATE REPORT 2016 SRI LANKA
ANNUAL CLIMATE REPORT 2016 SRI LANKA Foundation for Environment, Climate and Technology C/o Mahaweli Authority of Sri Lanka, Digana Village, Rajawella, Kandy, KY 20180, Sri Lanka Citation Lokuhetti, R.,
More informationPRELIMINARY ASSESSMENT OF SURFACE WATER RESOURCES - A STUDY FROM DEDURU OYA BASIN OF SRI LANKA
PRELIMINARY ASSESSMENT OF SURFACE WATER RESOURCES - A STUDY FROM DEDURU OYA BASIN OF SRI LANKA THUSHARA NAVODANI WICKRAMAARACHCHI Hydrologist, Water Resources Secretariat of Sri Lanka, Room 2-125, BMICH,
More informationChapter-3 GEOGRAPHICAL LOCATION, CLIMATE AND SOIL CHARACTERISTICS OF THE STUDY SITE
Chapter-3 GEOGRAPHICAL LOCATION, CLIMATE AND SOIL CHARACTERISTICS OF THE STUDY SITE Chapter-3 GEOGRAPHICAL LOCATION, CLIMATE AND SOIL CHARACTERISTICS OF THE STUDY SITE Assam, the eastern most state of
More informationDirect Normal Radiation from Global Radiation for Indian Stations
RESEARCH ARTICLE OPEN ACCESS Direct Normal Radiation from Global Radiation for Indian Stations Jaideep Rohilla 1, Amit Kumar 2, Amit Tiwari 3 1(Department of Mechanical Engineering, Somany Institute of
More informationUPPLEMENT A COMPARISON OF THE EARLY TWENTY-FIRST CENTURY DROUGHT IN THE UNITED STATES TO THE 1930S AND 1950S DROUGHT EPISODES
UPPLEMENT A COMPARISON OF THE EARLY TWENTY-FIRST CENTURY DROUGHT IN THE UNITED STATES TO THE 1930S AND 1950S DROUGHT EPISODES Richard R. Heim Jr. This document is a supplement to A Comparison of the Early
More informationScarborough Tide Gauge
Tide Gauge Location OS: 504898E 488622N WGS84: Latitude: 54 16' 56.990"N Longitude: 00 23' 25.0279"W Instrument Valeport 740 (Druck Pressure Transducer) Benchmarks Benchmark Description TGBM = 4.18m above
More informationWavelet & Correlation Analysis of Weather Data
International Journal of Current Engineering and Technology ISSN 2277 4106 2012INPRESSCO. All Rights Reserved. Available at http://inpressco.com/category/ijcet Research Article Wavelet & Correlation Analysis
More informationPublic Library Use and Economic Hard Times: Analysis of Recent Data
Public Library Use and Economic Hard Times: Analysis of Recent Data A Report Prepared for The American Library Association by The Library Research Center University of Illinois at Urbana Champaign April
More informationPRELIMINARY DRAFT FOR DISCUSSION PURPOSES
Memorandum To: David Thompson From: John Haapala CC: Dan McDonald Bob Montgomery Date: February 24, 2003 File #: 1003551 Re: Lake Wenatchee Historic Water Levels, Operation Model, and Flood Operation This
More informationUPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017)
UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (December 2017) 1. Review of Regional Weather Conditions for November 2017 1.1 In November 2017, Southeast Asia experienced inter-monsoon conditions in the first
More informationHighlights of the 2006 Water Year in Colorado
Highlights of the 2006 Water Year in Colorado Nolan Doesken, State Climatologist Atmospheric Science Department Colorado State University http://ccc.atmos.colostate.edu Presented to 61 st Annual Meeting
More informationAnnex I to Target Area Assessments
Baltic Challenges and Chances for local and regional development generated by Climate Change Annex I to Target Area Assessments Climate Change Support Material (Climate Change Scenarios) SWEDEN September
More informationJackson County 2013 Weather Data
Jackson County 2013 Weather Data 61 Years of Weather Data Recorded at the UF/IFAS Marianna North Florida Research and Education Center Doug Mayo Jackson County Extension Director 1952-2008 Rainfall Data
More informationNon-Glacial Watersheds of Uttarakhand
Impact of Climate Change in the Non- Glacial Fed Himalayan River System: A Case Study From the Kosi River in District Almora, Uttarakhand State (India) J.S.Rawat Director Centre of Excellence for Natural
More informationLong Range Forecasts of 2015 SW and NE Monsoons and its Verification D. S. Pai Climate Division, IMD, Pune
Long Range Forecasts of 2015 SW and NE Monsoons and its Verification D. S. Pai Climate Division, IMD, Pune Other Contributors: Soma Sen Roy, O. P. Sreejith, Kailas, Madhuri, Pallavi, Mahendra and Jasmine
More informationYACT (Yet Another Climate Tool)? The SPI Explorer
YACT (Yet Another Climate Tool)? The SPI Explorer Mike Crimmins Assoc. Professor/Extension Specialist Dept. of Soil, Water, & Environmental Science The University of Arizona Yes, another climate tool for
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015
RESEARCH ARTICLE OPEN ACCESS Analysis of Meteorological Data in of Tamil Nadu Districts Based On K- Means Clustering Algorithm M. Mayilvaganan [1], P. Vanitha [2] Department of Computer Science [2], PSG
More informationChapter 2 Variability and Long-Term Changes in Surface Air Temperatures Over the Indian Subcontinent
Chapter 2 Variability and Long-Term Changes in Surface Air Temperatures Over the Indian Subcontinent A.K. Srivastava, D.R. Kothawale and M.N. Rajeevan 1 Introduction Surface air temperature is one of the
More informationJANUARY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY
Vocabulary (01) The Calendar (012) In context: Look at the calendar. Then, answer the questions. JANUARY MONDAY TUESDAY WEDNESDAY THURSDAY FRIDAY SATURDAY SUNDAY 1 New 2 3 4 5 6 Year s Day 7 8 9 10 11
More informationAverage temperature ( F) World Climate Zones. very cold all year with permanent ice and snow. very cold winters, cold summers, and little rain or snow
P r e v i e w Look carefully at the climagraph of Mumbai, India. What is the wettest month (or months) in Mumbai? What is the driest month (or months) in Mumbai? What effects might this city s climate
More informationAN ASSESSMENT OF THE RELATIONSHIP BETWEEN RAINFALL AND LAKE VICTORIA LEVELS IN UGANDA
AN ASSESSMENT OF THE RELATIONSHIP BETWEEN RAINFALL AND LAKE VICTORIA LEVELS IN UGANDA BY CATHERINE MULINDE BA (Environmental Management), PGD (Meteorology) Teaching Assistant Department of Geography, Meteorology
More informationRainfall variation and frequency analysis study in Dharmapuri district, India
Indian Journal of Geo Marine Sciences Vol. 45 (11), November 216, pp. 156-1565 Rainfall variation and frequency analysis study in Dharmapuri district, India V. Rajendran 1*, R. Venkatasubramani 2 & G.
More informationConstructing a typical meteorological year -TMY for Voinesti fruit trees region and the effects of global warming on the orchard ecosystem
Constructing a typical meteorological year -TMY for Voinesti fruit trees region and the effects of global warming on the orchard ecosystem ARMEANU ILEANA*, STĂNICĂ FLORIN**, PETREHUS VIOREL*** *University
More informationTechnical note on seasonal adjustment for M0
Technical note on seasonal adjustment for M0 July 1, 2013 Contents 1 M0 2 2 Steps in the seasonal adjustment procedure 3 2.1 Pre-adjustment analysis............................... 3 2.2 Seasonal adjustment.................................
More informationA Comparison of Fractal Dimension Algorithms by Hurst Exponent using Gold Price Time Series
ISS: 3-9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 6 Issue II, February 08- Available at www.ijraset.com A Comparison of Fractal Dimension Algorithms by Hurst Exponent using Gold Price Time Series
More informationA SUMMARY OF RAINFALL AT THE CARNARVON EXPERIMENT STATION,
A SUMMARY OF RAINFALL AT THE CARNARVON EXPERIMENT STATION, 1931-213 J.C.O. Du Toit 1#, L. van den Berg 1 & T.G. O Connor 2 1 Grootfontein Agricultural Development Institute, Private Bag X529, Middelburg
More informationPhysical Features of Monsoon Asia. 192 Unit 7 Teachers Curriculum Institute 60 N 130 E 140 E 150 E 60 E 50 N 160 E 40 N 30 N 150 E.
50 N 60 E 70 E 80 E 90 E 100 E 60 N 110 E 120 E 130 E 140 E 150 E 50 N 160 E 40 N 40 N 30 N 60 E 30 N 150 E Tropic of Cancer 20 N Tropic of Cancer 20 N 10 N 10 N 0 Equator 0 Equator 10 S 10 S 0 500 1,000
More informationResearch Article Weather Forecasting Using Sliding Window Algorithm
ISRN Signal Processing Volume 23, Article ID 5654, 5 pages http://dx.doi.org/.55/23/5654 Research Article Weather Forecasting Using Sliding Window Algorithm Piyush Kapoor and Sarabjeet Singh Bedi 2 KvantumInc.,Gurgaon22,India
More informationOn wavelet techniques in atmospheric sciences.
On wavelet techniques in atmospheric sciences. Margarete Oliveira Domingues Odim Mendes Jr. Aracy Mendes da Costa INPE Advanced School on Space Environment - ASSEINP, São José dos Campos,2004 p.1/56 Preliminary
More informationWill a warmer world change Queensland s rainfall?
Will a warmer world change Queensland s rainfall? Nicholas P. Klingaman National Centre for Atmospheric Science-Climate Walker Institute for Climate System Research University of Reading The Walker-QCCCE
More informationSTOCHASTIC MODELING OF MONTHLY RAINFALL AT KOTA REGION
STOCHASTIC MODELIG OF MOTHLY RAIFALL AT KOTA REGIO S. R. Bhakar, Raj Vir Singh, eeraj Chhajed and Anil Kumar Bansal Department of Soil and Water Engineering, CTAE, Udaipur, Rajasthan, India E-mail: srbhakar@rediffmail.com
More informationDrought in Southeast Colorado
Drought in Southeast Colorado Nolan Doesken and Roger Pielke, Sr. Colorado Climate Center Prepared by Tara Green and Odie Bliss http://climate.atmos.colostate.edu 1 Historical Perspective on Drought Tourism
More informationLinear Regression. Aarti Singh. Machine Learning / Sept 27, 2010
Linear Regression Aarti Singh Machine Learning 10-701/15-781 Sept 27, 2010 Discrete to Continuous Labels Classification Sports Science News Anemic cell Healthy cell Regression X = Document Y = Topic X
More informationProject No India Basin Shadow Study San Francisco, California, USA
Project No. 432301 India Basin Shadow Study San Francisco, California, USA Numerical Modelling Studies 04 th June 2018 For Build Inc. Report Title: India Basin Shadow Study San Francisco, California, USA
More information1. INTRODUCTION 2. HIGHLIGHTS
Bulletin Issue January 2017 Issue Number: ICPAC/03/44 IGAD Climate Prediction and Applications Centre Seasonal Bulletin, Review for October to December (OND) Season 2016 For referencing within this bulletin,
More informationOVERVIEW OF IMPROVED USE OF RS INDICATORS AT INAM. Domingos Mosquito Patricio
OVERVIEW OF IMPROVED USE OF RS INDICATORS AT INAM Domingos Mosquito Patricio domingos.mosquito@gmail.com Introduction to Mozambique /INAM Introduction to AGRICAB/SPIRITS Objectives Material & Methods Results
More informationNOTES AND CORRESPONDENCE. A Quantitative Estimate of the Effect of Aliasing in Climatological Time Series
3987 NOTES AND CORRESPONDENCE A Quantitative Estimate of the Effect of Aliasing in Climatological Time Series ROLAND A. MADDEN National Center for Atmospheric Research,* Boulder, Colorado RICHARD H. JONES
More informationDiscrete Wavelet Transform
Discrete Wavelet Transform [11] Kartik Mehra July 2017 Math 190s Duke University "1 Introduction Wavelets break signals up and then analyse them separately with a resolution that is matched with scale.
More informationpeak half-hourly New South Wales
Forecasting long-term peak half-hourly electricity demand for New South Wales Dr Shu Fan B.S., M.S., Ph.D. Professor Rob J Hyndman B.Sc. (Hons), Ph.D., A.Stat. Business & Economic Forecasting Unit Report
More informationChampaign-Urbana 2000 Annual Weather Summary
Champaign-Urbana 2000 Annual Weather Summary ILLINOIS STATE WATER SURVEY 2204 Griffith Dr. Champaign, IL 61820 wxobsrvr@sws.uiuc.edu Maria Peters, Weather Observer January: January started on a mild note,
More informationStudy of Changes in Climate Parameters at Regional Level: Indian Scenarios
Study of Changes in Climate Parameters at Regional Level: Indian Scenarios S K Dash Centre for Atmospheric Sciences Indian Institute of Technology Delhi Climate Change and Animal Populations - The golden
More informationTime Series Analysis
Time Series Analysis A time series is a sequence of observations made: 1) over a continuous time interval, 2) of successive measurements across that interval, 3) using equal spacing between consecutive
More informationVariability and trends in daily minimum and maximum temperatures and in diurnal temperature range in Lithuania, Latvia and Estonia
Variability and trends in daily minimum and maximum temperatures and in diurnal temperature range in Lithuania, Latvia and Estonia Jaak Jaagus Dept. of Geography, University of Tartu Agrita Briede Dept.
More informationPrincipal Component Analysis of Sea Surface Temperature via Singular Value Decomposition
Principal Component Analysis of Sea Surface Temperature via Singular Value Decomposition SYDE 312 Final Project Ziyad Mir, 20333385 Jennifer Blight, 20347163 Faculty of Engineering Department of Systems
More information"STUDY ON THE VARIABILITY OF SOUTHWEST MONSOON RAINFALL AND TROPICAL CYCLONES FOR "
"STUDY ON THE VARIABILITY OF SOUTHWEST MONSOON RAINFALL AND TROPICAL CYCLONES FOR 2001 2010" ESPERANZA O. CAYANAN, Ph.D. Chief, Climatology & Agrometeorology R & D Section Philippine Atmospheric Geophysical
More informationTHE SIGNIFICANCE OF AIR TEMPERATURE OSCILLATIONS IN THE LAST DECADE IN SPLIT - CROATIA
Croatian Operational Research Review (CRORR), Vol., 11 THE SIGNIFICANCE OF AIR TEMPERATURE OSCILLATIONS IN THE LAST DECADE IN SPLIT - CROATIA Abstract Elza Jurun University of Split/Faculty of Economics
More informationRainfall variation and frequency analysis study of Salem district Tamil Nadu
Indian Journal of Geo Marine Sciences Vol. 46 (1), January 217, pp. 213-218 Rainfall variation and frequency analysis study of Salem district Tamil Nadu Arulmozhi.S 1* & Dr. Prince Arulraj.G 2 1 Department
More informationTemporal Trends in Forest Fire Season Length
Temporal Trends in Forest Fire Season Length Alisha Albert-Green aalbertg@sfu.ca Department of Statistics and Actuarial Science Simon Fraser University Stochastic Modelling of Forest Dynamics Webinar March
More informationMULTIFRACTAL BEHAVIOUR IN OIL PRICES BY USING MF-DFA AND WTMM METHODS
MULTIFRACTAL BEHAVIOUR IN OIL PRICES BY USING MF-DFA AND WTMM METHODS Kemal Ayten Experian and Yeditepe University Title : Business Development Manager, Credit Bureau and Phd Candidate E-mail: kemal.ayten@experian.com
More informationStudy of Hydrometeorology in a Hard Rock Terrain, Kadirischist Belt Area, Anantapur District, Andhra Pradesh
Open Journal of Geology, 2012, 2, 294-300 http://dx.doi.org/10.4236/ojg.2012.24028 Published Online October 2012 (http://www.scirp.org/journal/ojg) Study of Hydrometeorology in a Hard Rock Terrain, Kadirischist
More informationExemplar for Internal Achievement Standard. Mathematics and Statistics Level 3
Exemplar for internal assessment resource Mathematics and Statistics for Achievement Standard 91580 Exemplar for Internal Achievement Standard Mathematics and Statistics Level 3 This exemplar supports
More informationSeasonal Weather Forecast Talk Show on Capricorn FM and North West FM
Seasonal Weather Forecast Talk Show on Capricorn FM and North West FM Categories of Weather Forecast Nowcast (0-6 hours) DETERMINISTIC Short-term (1-7 days) DETERMINISTIC Medium-term (up to 30 days) DETERMINISTIC/PROBABILISTIC
More informationDetermine the trend for time series data
Extra Online Questions Determine the trend for time series data Covers AS 90641 (Statistics and Modelling 3.1) Scholarship Statistics and Modelling Chapter 1 Essent ial exam notes Time series 1. The value
More informationSeasonal Climate Outlook for South Asia (June to September) Issued in May 2014
Ministry of Earth Sciences Earth System Science Organization India Meteorological Department WMO Regional Climate Centre (Demonstration Phase) Pune, India Seasonal Climate Outlook for South Asia (June
More informationMozambique. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1
UNDP Climate Change Country Profiles Mozambique C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2.Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk
More informationThird Grade Math and Science DBQ Weather and Climate/Representing and Interpreting Charts and Data
Third Grade Math and Science DBQ Weather and Climate/Representing and Interpreting Charts and Data A document based question (DBQ) is an authentic assessment where students interact with content related
More informationLocation. Datum. Survey. information. Etrometa. Step Gauge. Description. relative to Herne Bay is -2.72m. The site new level.
Tide Gauge Location OS: 616895E 169377N WGS84: Latitude: 51 o 22.919196 N Longitude: 01 o 6.9335907 E Instrument Type Etrometa Step Gauge Benchmarks Benchmark TGBM = 5.524m above Ordnance Datum Newlyn
More informationTwelve Moons Curriculum Overview
Twelve Moons Twelve Moons Curriculum Overview 1. Instructions The curriculum is arranged by book section, in order. Feel free to teach in any order you wish. The curriculum adheres to three objectives
More informationCHAPTER-11 CLIMATE AND RAINFALL
CHAPTER-11 CLIMATE AND RAINFALL 2.1 Climate Climate in a narrow sense is usually defined as the "average weather", or more rigorously, as the statistical description in terms of the mean and variability
More informationEstimation of Diffuse Solar Radiation for Yola, Adamawa State, North- Eastern, Nigeria
International Research Journal of Engineering and Technology (IRJET) e-issn: - Volume: Issue: Nov- www.irjet.net p-issn: - Estimation of Diffuse Solar Radiation for Yola, Adamawa State, North- Eastern,
More informationMalawi. General Climate. UNDP Climate Change Country Profiles. C. McSweeney 1, M. New 1,2 and G. Lizcano 1
UNDP Climate Change Country Profiles Malawi C. McSweeney 1, M. New 1,2 and G. Lizcano 1 1. School of Geography and Environment, University of Oxford. 2. Tyndall Centre for Climate Change Research http://country-profiles.geog.ox.ac.uk
More informationLocation. Datum. Survey. information. Etrometa. Step Gauge. Description. relative to Herne Bay is -2.72m. The site new level.
Tide Gauge Location OS: 616895E 169377N WGS84: Latitude: 51 o 22.919196 N Longitude: 01 o 6.9335907 E Instrument Type Etrometa Step Gauge Benchmarks Benchmark TGBM = 5.524m above Ordnance Datum Newlyn
More informationCHARACTERISATION OF THE DYNAMIC RESPONSE OF THE VEGETATION COVER IN SOUTH AMERICA BY WAVELET MULTIRESOLUTION ANALYSIS OF NDVI TIME SERIES
CHARACTERISATION OF THE DYNAMIC RESPONSE OF THE VEGETATION COVER IN SOUTH AMERICA BY WAVELET MULTIRESOLUTION ANALYSIS OF NDVI TIME SERIES Saturnino LEGUIZAMON *, Massimo MENENTI **, Gerbert J. ROERINK
More informationForecasting. Copyright 2015 Pearson Education, Inc.
5 Forecasting To accompany Quantitative Analysis for Management, Twelfth Edition, by Render, Stair, Hanna and Hale Power Point slides created by Jeff Heyl Copyright 2015 Pearson Education, Inc. LEARNING
More informationWhat is happening to the Jamaican climate?
What is happening to the Jamaican climate? Climate Change and Jamaica: Why worry? Climate Studies Group, Mona (CSGM) Department of Physics University of the West Indies, Mona Part 1 RAIN A FALL, BUT DUTTY
More informationSeasonal Climate Watch June to October 2018
Seasonal Climate Watch June to October 2018 Date issued: May 28, 2018 1. Overview The El Niño-Southern Oscillation (ENSO) has now moved into the neutral phase and is expected to rise towards an El Niño
More informationUsing Reanalysis SST Data for Establishing Extreme Drought and Rainfall Predicting Schemes in the Southern Central Vietnam
Using Reanalysis SST Data for Establishing Extreme Drought and Rainfall Predicting Schemes in the Southern Central Vietnam Dr. Nguyen Duc Hau 1, Dr. Nguyen Thi Minh Phuong 2 National Center For Hydrometeorological
More informationCOUNTRY REPORT. Jakarta. July, th National Directorate of Meteorology and Geophysics of Timor-Leste (DNMG)
The Southeastern Asia-Oceania Flash Flood COUNTRY REPORT Jakarta. July, 10-12 th 2017 National Directorate of Meteorology and Geophysics of Timor-Leste (DNMG) Carla Feritas and Crisostimo Lobato Democratic
More information2015 Fall Conditions Report
2015 Fall Conditions Report Prepared by: Hydrologic Forecast Centre Date: December 21 st, 2015 Table of Contents Table of Figures... ii EXECUTIVE SUMMARY... 1 BACKGROUND... 2 SUMMER AND FALL PRECIPITATION...
More information