Assessment of Spatio-temporal Climatological trends of ozone over the Indian region using Machine Learning

Abstract 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.