Analysis of the influence of solar activity and atmospheric factors on 7Be air concentration by seasonal-trend decomposition

Bas, M.C.; Ortiz, J.; Ballesteros, L.; Martorell, Sebastián

doi:10.1016/j.atmosenv.2016.09.027

Cited by 22 publications

(14 citation statements)

References 23 publications

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“…According to Bas et al (2016), 7 Be concentration variability can be decomposed into terms of three main components: 1) trend-cycle, 2) seasonal and 3) irregular variations. Solar activity is a cosmogenic factor with a high influence on the trend-cycle component of 7 Be variability.…”

Section: Resultsmentioning

confidence: 99%

“…Considering the results obtained in Bas et al (2016) and the model proposed in this paper (Eq. 11), the following relation between the SARIMA parameters and the atmospheric factors could be interpreted.…”

Section: Resultsmentioning

confidence: 99%

“…The coefficient (1 + 0.814 12 ) is associated with the parameter P=1, which determines the influence of the months of previous years on the forecasting month; for instance, the forecasting value for 7 Be activity in January is influenced by the 7 Be activity observed in January 2014. The dependence observed between the seasonal observations could be influenced by solar radiation, temperature and relative humidity, which are regular atmospheric variables that affect seasonal 7 Be variations (Bas et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

“…The first part of the SARIMA defined by the ( , , ) parameters is related to the regular part of the time series and the ( , , ) with seasonal variations. Considering the annual periodicity observed in Bas et al (2016) and the 7 Be values measured monthly, the parameters of the SARIMA model have the following interpretation: p: determines the influence of the previous months of the same year on the forecasting month. It is known as the time series inertia.…”

Section: 3mentioning

confidence: 99%

“…A recent study applied a decomposition of the 7 Be time series into a trend-cycle, a seasonal and an irregular component in order to separate the inter-and intra-annual patterns of 7 Be variability (Bas et al, 2016). The results of this study showed the need to apply time series analysis to correlated data in order to separate the different sources of variability of 7 Be concentrations and to develop a forecasting model.…”

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confidence: 97%

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Forecasting 7 BE concentrations in surface air using time series analysis

Bas

Ortiz

Ballesteros

et al. 2017

Atmospheric Environment

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7 Be is a cosmogenic radionuclide widely used as an atmospheric tracer, whose evaluation and forecasting can provide valuable information on changes in the atmospheric behavior. In this study, measurements of 7 Be concentrations were made each month during the period 2007-2015 from samples of atmospheric aerosols filtered from the air. The aim was to propose a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to develop an explanatory and predictive model of 7 Be air concentrations. The Root Mean Square Error (RMSE) and the Adapted Mean Absolute Percentage Error (AMAPE) were selected to measure forecasting accuracy in identifying the best historical data time window to explain 7 Be concentrations. A measure based on the variance of forecast errors was calculated to determine the impact of the model uncertainty on forecasts. We concluded that the SARIMA method is a powerful explanatory and predictive technique for explaining 7 Be air concentrations in a long-term series of at least eight years of historical data to forecast 7 Be concentration trends up to one year in advance.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: 3mentioning

confidence: 99%

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