A long-term regional variability analysis of wintertime temperature and its deep learning aspects

In present study, the variability in wintertime maximum (Tmax) and minimum (Tmin) temperature patterns over India using observed and deep learning techniques have been assessed. The analysis has been caried out for the period 1979-2018 during the months from November to February. The month of February depicted strongest variability in Tmax and Tmin over Northwest India (NWI) with significant + ve trend for upper half of the country. Wintertime temperature variability was seen to be dominant in the Indo-Gangetic plain area covering some parts of NWI and Northeast India (NEI) for Tmax and Tmin. Also, a gradual increase in the spatial coverage, engulfing majority of South Peninsular India (SPI) and Central India (CI) of the rising Diurnal Temperature Range (DTR) was found from November to January. Decreasing DTR was observed only for January extending along Indo-Gangetic plains. The model Random Forest (RF) performed quite well relative to Long Short-Term Memory model (LSTM) in predicting the winter temperatures (especially for Tmax) during all the considered months. The RF made a robust Tmax forecast during NDJF over all India (RMSE - 0.51, MAPE - 1.4). However, its performance is not up to the mark during the month of February over NEI (RMSE - 1.63, MAPE - 4.5). The maximum fluctuating patterns of temperature have been found during the month of February. The study emphasizes on algorithm-based approaches to study the temperature, so that better understanding could be developed for the meteorological sub-divisions over India.