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

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Abstract

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.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is a part of Doctoral Thesis of Saurabh Singh. The authors wish special thanks to India Meteorological Department, New Delhi for providing necessary datasets.

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Authors and Affiliations

  1. Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, India

    Saurabh Singh, R. Bhatla & Manas Pant

  2. DST-Mahamana Centre of Excellence in Climate Change Research, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India

    R. Bhatla & Manas Pant

  3. School of Earth, Ocean and Climate Sciences, Indian Institute of Technology, Bhubaneswar, India

    Palash Sinha

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  1. Saurabh Singh
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  2. R. Bhatla
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Contributions

R Bhatla and Saurabh Singh have developed the research idea. Palash Sinha has framed the overall structure and the flow of the research. Saurabh Singh has done the data analysis and wrote the manuscript. Manas Pant has aligned the data-based approaches to the scientific findings and balanced the overall framework. R Bhatla, Palash Sinha and Manas Pant have subsequently supervised and modified the paper.

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Correspondence to R. Bhatla.

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Singh, S., Bhatla, R., Sinha, P. et al. A long-term regional variability analysis of wintertime temperature and its deep learning aspects. Earth Sci Inform 16, 3647–3666 (2023). https://doi.org/10.1007/s12145-023-01106-4

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