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Forecasting of some greenhouse gases content trend in the air of the Russian Arctic region
Atmospheric Pollution Research ( IF 4.5 ) Pub Date : 2020-10-13 , DOI: 10.1016/j.apr.2020.10.009
Elena Baglaeva , Alexander Buevich , Alexander Sergeev , Anna Rakhmatova , Andrey Shichkin

Increasing the number of heat extremes is the global problem associated with the greenhouse gas (GHG) emissions. The cause-effect relationships between the increase in average temperature and the greenhouse gases contents in the atmosphere are not completely determined. The monitoring data of the gases (methane, water vapor, carbon monoxide and carbon dioxide) and meteorological conditions on the Russia Arctic area, Belyy Island in summer seasons 2015–2017 was used to investigate the cycles of the dynamic change in the variability of the GHG contents. The linear trend of the average values of the temperature or the GHG content in the summer seasons during the three years was not detected. The highest daily mean temperature (283K) in the summer season was corresponding to the hottest 2016. The daily mean GHG concentrations did not differ within the mean plus or minus standard deviation every year. Analysis of the time series of GHG concentrations showed that the methane and water vapor data associated with the temperature contain decade, week, three- and one-day components. The 24- and 72-h time intervals were taken into account to forecast of the seasonal dynamics of the fluctuations by non-linear autoregressive neural network model which provided high predictive accuracy. For a one-day forecast, the correlation coefficients between the predicted and observed GHG contents were near 0.9.



中文翻译:

俄罗斯北极地区空气中某些温室气体含量趋势的预测

极端热量的增加是与温室气体(GHG)排放相关的全球性问题。平均温度升高与大气中温室气体含量之间的因果关系尚未完全确定。使用2015-2017年夏季Belyy岛俄罗斯北极地区的气体(甲烷,水蒸气,一氧化碳和二氧化碳)和气象条件的监测数据,调查了大气变化的动态变化周期。温室气体含量。未检测到三年中夏季夏季温度或GHG含量平均值的线性趋势。夏季最高的每日平均温度(283K)对应于最热的2016年。每天的平均GHG浓度在每年的平均正负标准偏差内没有差异。对温室气体浓度的时间序列进行的分析表明,与温度相关的甲烷和水蒸气数据包含十,十,三,一日。非线性自回归神经网络模型考虑了24小时和72小时的时间间隔来预测波动的季节动态,从而提供了较高的预测精度。对于一日预报,预测的GHG含量与观察到的GHG含量之间的相关系数接近0.9。非线性自回归神经网络模型考虑了24小时和72小时的时间间隔来预测波动的季节动态,从而提供了较高的预测精度。对于一日预报,预测的GHG含量与观察到的GHG含量之间的相关系数接近0.9。非线性自回归神经网络模型考虑了24小时和72小时的时间间隔来预测波动的季节动态,从而提供了较高的预测精度。对于一日预报,预测的GHG含量与观察到的GHG含量之间的相关系数接近0.9。

更新日期:2020-10-13
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