Modern Computational Techniques for Environmental Data; Application to the Global Ozone Layer

Varotsos, C.

doi:10.1007/11428862_69

Cited by 69 publications

(31 citation statements)

References 12 publications

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“…Peng et al, 1994;Weber and Talkner, 2001;Chen et al, 2002;Varotsos et al, 2003a,b;Maraun et al, 2004;Varotsos, 2005), are as follows: 1) We construct a new time series, by integrating over time the deseasonalized time series (of TOZ or TRT). More precisely, to integrate the data, we find the fluctuations of the N observations Z(i) from their mean value Z ave notably: Z(i) -Z ave .…”

Section: Methods and Data Analysismentioning

confidence: 99%

“…In particular, for time scales shorter than about 2 years, α 1 =1.11±0.02, while for longer time scales, α 2 =0.64±0.06. Thus, the tropics exhibit stronger persistence at long time scales than the extratropics, but approximately equivalent persistence at short time scales (Varotsos, 2005).…”

Section: The Time Scaling Of the Total Ozone Fluctuationsmentioning

confidence: 97%

“…This means that TOZ or TRT fluctuations at different times are positively correlated, and the corresponding autocorrelation function decays more slowly than exponential decay with increasing lag, possibly by a power-law decay. In other words, "persistence" refers to the "long memory" within the TOZ (or TRT) time series (Stanley, 1999;Hu et al, 2001;Blender and Fraedrich, 2003;Collette and Ausloos, 2004;Varotsos, 2005). Other scientists (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Long-memory processes in ozone and temperature variations at the region 60° S–60° N

2006

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Abstract. Global column ozone and tropospheric temperature observations made by ground-based and satellite-borne (1978-2004) instrumentation are analyzed. Ozone and temperature fluctuations in small time-intervals are found to be positively correlated to those in larger timeintervals in a power-law fashion. For temperature, the exponent of this dependence is larger in the mid-latitudes than in the tropics at long time scales, while for ozone, the exponent is larger in tropics than in the mid-latitudes. In general, greater persistence could be a result of either stronger positive feedbacks or larger inertia. Therefore, the increased slope of the power distribution of temperature in mid-latitudes at long time scales compared to the slope in the tropics could be connected to the poleward increase in climate sensitivity predicted by the global climate models. The detrended fluctuation analysis of model and observed time series provides a helpful tool for visualizing errors in the treatment of long-range correlations, whose correct modeling would greatly enhance confidence in long-term climate and atmospheric chemistry modeling.

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Section: Methods and Data Analysismentioning

confidence: 99%

Section: The Time Scaling Of the Total Ozone Fluctuationsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Long-memory processes in ozone and temperature variations at the region 60° S–60° N

2006

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“…Kondratyev and Varotsos (2001a, b) have pointed out that the prediction of global climate change and of change in the atmosphere's ozone distribution, is impossible without consideration of the complexity of all interactive processes including chemistry and dynamics of the atmosphere. In order to understand the long-range behavior of air pollution, Varotsos (2005a) employed power law relationships (Varotsos and Cartalis 1991). The existence of a background level of ozone in the atmosphere is well established.…”

Section: Introductionmentioning

confidence: 99%

Measurements of surface ozone at semi-arid site Anantapur (14.62°N, 77.65°E, 331 m asl) in India

et al. 2008

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In the present study, an attempt has been made to examine the governing photochemical processes of surface ozone (O 3 ) formation in rural site. For this purpose, measurements of surface ozone and selected meteorological parameters have been made at Anantapur (14.62°N, 77.65°E, 331 m asl), a semi-arid zone in India from January 2002 to December 2003. The annual average diurnal variation of O 3 shows maximum concentration 46 ppbv at noon and minimum 25 ppbv in the morning with 1σ standard deviation. The average seasonal variation of ozone mixing ratios are observed to be maximum (about 60 ppbv) during summer and minimum (about 22 ppbv) in the monsoon period. The monthly daytime and nighttime average surface ozone concentration shows a maximum (55±7 ppbv; 37±7.3 ppbv) in March and minimum (28±3.4 ppbv; 22±2.3 ppbv) in August during the study period. The monthly average high (low) O 3 48.9±7.7 ppbv (26.2± 3.5 ppbv) observed at noon in March (August) is due to the possible increase in precursor gas concentration by anthropogenic activity and the influence of meteorological parameters. The rate of increase of surface ozone is high (1.52 ppbv/h) in March and lower (0.40 ppbv/h) in July. The average rate of increase of O 3 from midnight to midday is 1 ppbv/h. Surface temperature is highest (43-44°C) during March and April months leading to higher photochemical production. On the other hand, relative humidity, which is higher during the rainy season, shows negative correlation with temperature and ozone mixing ratio. It can be seen that among the two parameters are measured, correlation of surface ozone with wind speed is better (R 2 =0.84) in compare with relative humidity (R 2 =0.66).

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“…Afterwards Grassberger (1986); Henderson and Wells (1988); Tsonis and Elsner (1989); Sharifi et al (1990) studied the chaotic behaviour of meteorological processes and variables. In recent times, the identification of the intrinsic chaos of such processes has drawn the attention of scientists in many disciplines (Weigend and Gershenfeld, 1993;Stehlik, 1999;Varotsos, 2005;Sivakumar et al, 2004;Varotsos et al, 2006). Conventional statistical approaches are not capable of dealing with this complexity efficiently because of their data sensitiveness, and dependence upon prior assumptions regarding the system.…”

Section: Introductionmentioning

confidence: 99%

Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach

Chattopadhyay¹,

Jain²,

Chattopadhyay

2009

Meteorological Applications

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This paper reports the development of a neurocomputing-based model for estimating the potential evapotranspiration over Gangetic West Bengal, India during the summer monsoon months of June, July and August. An artificial neural network is implemented in the form of multilayer perceptron to generate the model. Three weather variables, surface temperature, vapour pressure and rainfall are used as the independent variables in generating the model. The performance of the model is judged statistically against non-linear regression in the form of asymptotic regression. The study reveals that an artificial neural network is more efficient than the regression approach to estimate the potential evapotranspiration in the summer monsoon months. Furthermore, it is established that the artificial neural network and non-linear regression have almost equal efficiency in the previously mentioned estimation in the month of June. However, in July and August the higher values of correlation and Willmott's indices, and lower values of estimation error, indicate that the artificial neural network is more reliable than the non-linear regression approach. Since evapotranspiration is one of the basic components of the hydrological cycle and is essential for estimating irrigation water requirement, an efficient estimation procedure may help in agrometeorological modelling and irrigation scheduling in the summer monsoon months, which are of high importance for agriculture in the study zone.

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Modern Computational Techniques for Environmental Data; Application to the Global Ozone Layer

Cited by 69 publications

References 12 publications

Long-memory processes in ozone and temperature variations at the region 60° S–60° N

Long-memory processes in ozone and temperature variations at the region 60° S–60° N

Measurements of surface ozone at semi-arid site Anantapur (14.62°N, 77.65°E, 331 m asl) in India

Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach

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