Predicting daily total ozone over Kolkata, India: skill assessment of different neural network models

Chattopadhyay, Goutami; Chattopadhyay, Surajit

doi:10.1002/met.97

Cited by 13 publications

(6 citation statements)

References 61 publications

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“…Model estimates and measurements suggested that the amplitude of the solar cycle in ozone concentration is less than 4% throughout the stratosphere, although there are apparent differences between the models and observations at some altitudes. Finally, the lower stratospheric solar cycle in tropical ozone appeared to be caused indirectly through a dynamical response to solar variations (Chattopadhyay and Chattopadhyay, , ).…”

Section: Introductionmentioning

confidence: 99%

On the 11 year solar cycle signature in global total ozone dynamics

Efstathiou

Varotsos

2012

Meteorological Applications

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ABSTRACT:The main aim of the present study is to investigate further the association between total ozone (TOZ) and the 11 year solar cycle (SC) during the period 1979-2010, by employing satellite observations of TOZ made by Nimbus-7, Meteor-3, Earth Probe Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) instrumentation. A statistically significant correlation between the annual mean TOZ over both hemispheres and sunspot number (SN) is found. On the contrary, focusing on the January and February mean monthly TOZ fluctuations from the equator to the high latitudes of the Northern Hemisphere, no association between TOZ and SN is derived. This is attributed to the existence of the quasi-biennial-oscillation (QBO) and the El Niño-Southern oscillation (ENSO) in the TOZ time series. The latter oscillation is herewith expressed by the recently introduced Ozone ENSO Index (OEI). However, when considering the TOZ zonal means centred at 17.5 and 27.5°N during the east phase years of the QBO in the equatorial zonal wind at 50 hPa, a statistically significant correlation between TOZ and SN is revealed. It is an indication that the quasiperiodic fluctuations (i.e. QBO, ENSO) strongly contaminate the relationship between TOZ and solar activity. Plausible mechanisms are discussed, exploring the momentum flux (MF) measurements between 45 and 75°N, in the periods of increased dynamical variability. The findings obtained point to the conclusion that the 11 year solar cycle response in TOZ is caused by dynamical changes which are caused by solar activity. These are of crucial importance because solar radiation is a major driving force of the climate system.

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Section: Introductionmentioning

confidence: 99%

On the 11 year solar cycle signature in global total ozone dynamics

Efstathiou

Varotsos

2012

Meteorological Applications

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“…Chattopadhyay (2007), and Chattopadhyay and Chattopadhyay (2008) found that there is no periodicity or persistence in the summer monsoon rainfall time series over India. Chattopadhyay and Chattopadhyay (2009a, 2009b) found a periodicity in the monthly total ozone time series over a region of India, however, the ACF gradually tends to 0. Chattopadhyay et al (2009) found that no periodicity exists in the potential evapotranspiration time series over Gangetic West Bengal belonging to India.…”

Section: Statistical Features Of the Datamentioning

confidence: 99%

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach

Chattopadhyay

Jhajharia

Chattopadhyay

2011

Meteorological Applications

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ABSTRACT:The present paper has adopted an autoregressive approach to inspect the time series of monthly maximum temperature (T max ) over northeast India. Through autocorrelation analysis the T max time series of northeast India is identified as non-stationary, with a seasonality of 12 months, and it is also found to show an increasing trend by using both parametric and non-parametric methods. The autoregressive models of the reduced T max time series, which has become stationary on removal of the seasonal and the trend components from the original time series, were generated through Yule-Walker equations. The sixth order autoregressive model (AR (6)) is identified as a suitable representative of the T max time series based on the Akaike information criteria, and the prediction potential of AR (6) is also established statistically through Willmott's indices. Subsequently, autoregressive neural network models were generated as a multilayer perceptron, a generalized feed forward neural network and a modular neural network. An autoregressive neural network model of order four (AR-NN (4)), in the form of a modular neural network (MNN), has performed comparably well with that of AR (6) based on the high values of Willmott's indices and the low values of the prediction error. Therefore, AR-NN(4)-MNN will be a better option than AR(6) to forecast a time series, i.e. the monthly T max time series of northeast India, because AR-NN(4)-MNN requires fewer predictors for a superior forecast of a time series.

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“…Among other approaches, neural networks have been intensely used in TCO estimation problems (Monge and Medrano, 2004;Chattopadhyay, 2007, 2009b, Salcedo et al, 2010. In Monge and Medrano (2004), a multi-layer perceptron neural network (MLP) (Hagan and Menhaj, 1994) is applied to the prediction of TCO series in Arosa (Switzerland), Lisbon (Portugal) and Vigna di Valle (Italy).…”

Section: Introductionmentioning

confidence: 99%

Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

Carro-Calvo¹,

Casanova-Mateo²,

Sanz-Justo³

et al. 2017

Atm

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RESUMENSe propone un nuevo método de pronóstico para la columna total de ozono (CTO) basado en la combinación de algoritmos de vectores de soporte para regresión (VSR) y variables de predicción provenientes del saté-lite de colaboración nacional en órbita polar Suomi, así como de modelos numéricos del Sistema Global de Predicción (SGP) y mediciones directas. Los datos de satélite incluyen perfiles de temperatura y humedad a diferentes alturas, y mediciones de CTO realizadas en los días anteriores al pronóstico. El modelo SGP proporciona datos de temperatura y humedad para el día del pronóstico. El sistema también considera los datos alternos de mediciones in situ, p. ej. de la profundidad óptica de aerosoles a diferentes longitudes de onda. Mediante la metodología VSR se puede obtener un pronóstico exacto de la CTO a partir de estas variables de predicción, con mejores resultados que los obtenidos con otros métodos de regresión, p. ej. redes neuronales. También se efectúa un análisis del mejor subconjunto de características del pronóstico de CTO. La parte experimental de la investigación consiste en la aplicación de VSR a datos de observación directa obtenidos en el laboratorio radiométrico de Madrid, España, donde están disponibles mediciones de ozono adquiridas por medio de un espectrofotómetro Brewer, lo que posibilita el entrenamiento del sistema y la evaluación de sus resultados. ABSTRACTThis paper proposes a novel prediction method for Total Column Ozone (TCO), based on the combination of Support Vector Regression (SVR) algorithms and different predictive variables coming from satellite data (Suomi National Polar-orbiting Partnership satellite), numerical models (Global Forecasting System model, GFS) and direct measurements. Data from satellite consists of temperature and humidity profiles at different heights, and TCO measurements the days before the prediction. GFS model provides predictions of temperature and humidity for the day of prediction. Alternative data measured in situ, such as aerosol optical depth at different wavelengths, are also considered in the system. The SVR methodology is able to obtain an accurate TCO prediction from these predictive variables, outperforming other regression methodologies such as neural networks. Analysis on the best subset of features in TCO prediction is also carried out in this paper. The experimental part of the paper consists in the application of the SVR to real data collected at the radiometric observatory of Madrid, Spain, where ozone measurements obtained with a Brewer spectrophotometer are available, and allow the system's training and the evaluation of its performance.

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Predicting daily total ozone over Kolkata, India: skill assessment of different neural network models

Cited by 13 publications

References 61 publications

On the 11 year solar cycle signature in global total ozone dynamics

On the 11 year solar cycle signature in global total ozone dynamics

Univariate modelling of monthly maximum temperature time series over northeast India: neural network versus Yule–Walker equation based approach

Efficient prediction of total column ozone based on support vector regression algorithms, numerical models and Suomi-satellite data

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