The present study attempts to detect anomalies and evaluate statistical climatological trends of recorded total columnar ozone (RTCO) over the Indian sites. The 30–54 years of RTCO data recorded by the Dobson Spectrophotometer obtained from the India Meteorological Department (IMD) is used. TCO Anomalies are detected using predicted TCO (PTCO) from a Long Short-Term Memory (LSTM) based neural network model. The percentage of anomalies detected by the current model are 1.20% (2%), 0.76% (1.76%), 0.97% (0.72%) and 1.07% (1.60%) for Dobson Spectrophotometer (Satellite measured TCO) over New Delhi, Kodaikanal, Pune and Varanasi respectively. After removing anomalies, the PTCO by the neural network (NN) model correlates a minimum of 83% (New Delhi) and a maximum of 94% (Pune) with RTCO measurements, which demonstrates the accuracy of the present model in predicting the TCO. Using the anomaly removed long-term RTCO measurements, statistical climatological trends are estimated using Mann–Kendall (MK) test-based Sen’s slope to evaluate the significance of the linear fit. Results of linear regression (MK test) based linear fit reported an increasing TCO trend over New Delhi and decreasing TCO trend over Varanasi with slope of 0.22 (0.21) DU year⁻¹ and -0.40 (-0.46) DU year⁻¹ respectively. However, the MK-based statistical test shows no trend over Kodaikanal and Pune.

本研究试图检测异常并评估印度站点上已记录的总柱状臭氧（RTCO）的统计气候趋势。使用从印度气象局（IMD）获得的Dobson分光光度计记录的30–54年的RTCO数据。使用基于长期短期记忆（LSTM）的神经网络模型中的预测TCO（PTCO）来检测TCO异常。当前模型检测到的异常百分比在多代逊分光光度计（卫星测得的总拥有成本）在Kodaikanal的新德里为1.20％（2％），0.76％（1.76％），0.97％（0.72％）和1.07％（1.60％） ，浦那和瓦拉纳西。在消除异常之后，神经网络（NN）模型的PTCO与RTCO测量值相关联的最小值为83％（新德里），最大值为94％（Pune），这证明了本模型在预测TCO方面的准确性。使用异常去除的长期RTCO测量值，使用基于Mann–Kendall（MK）测试的Sen斜率估算统计气候趋势，以评估线性拟合的重要性。基于线性回归（MK检验）的线性拟合结果显示，新德里的TCO趋势增加，瓦拉纳西的TCO趋势降低，DU年的斜率为0.22（0.21）^-1年的-1和-0.40（-0.46）DU年^-1。但是，基于MK的统计检验显示，与Kodaikanal和Pune相比，没有任何趋势。