Abstract
Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Available observations generated by automated weather stations or meteorological observatories are often limited in spatial resolution resulting in misrepresentation or absence of rainfall information at these levels. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex spatio-temporal process leading to non-linear or chaotic spatio-temporal variations, no single downscaling method can be considered efficient enough. In the domains dominated by complex topographies, quasi-periodicities, and non-linearities, deep learning (DL)–based methods provide an efficient solution in downscaling rainfall data for regional climate forecasting and real-time rainfall observation data at high spatial resolutions. We employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods in this work. Summer monsoon season data from India Meteorological Department (IMD) and the tropical rainfall measuring mission (TRMM) data set were downscaled up to 4 times higher resolution using these methods. High-resolution data derived from deep learning-based models provide better results than linear interpolation for up to 4 times higher resolution. Among the three algorithms, namely, SRCNN, stacked SRCNN, and DeepSD, employed here, the best spatial distribution of rainfall amplitude and minimum root-mean-square error is produced by DeepSD-based downscaling. Hence, the use of the DeepSD algorithm is advocated for future use. We found that spatial discontinuity in amplitude and intensity rainfall patterns is the main obstacle in the downscaling of precipitation. Furthermore, we applied these methods for model data post-processing, in particular, ERA5 reanalysis data. Downscaled ERA5 rainfall data show a much better distribution of spatial covariance and temporal variance when compared with observation. This study is the first step towards developing deep learning-based weather data downscaling model for Indian summer monsoon rainfall data.
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Data availability
The details of HPC can be found on prtyush.tropmet.res.in. IMD rainfall data is obtained from India Meteorological Department, Pune. The TRMM data is available at https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_Daily_7/summary. The ERA5 rainfall data is downloaded from https://cds.climate.copernicus.eu/.
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Acknowledgment
IITM is funded by the Ministry of Earth Sciences, Government of India. The authors would like to thank Dr. David John Gagne, NCAR, for a constructive discussion and suggestions on this manuscript.
Funding
This work is a part of a student project completed at IITM. No additional funding support was received. This work was done on the HPC facility provided by the Ministry of Earth Sciences at IITM Pune.
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Concept and design: B.K., R.C., and M.S. conceptualize the idea of problem statement and finalized the algorithms for methods used in this study.
Drafting of manuscript: B.K. and R.C. mainly contributed in manuscript writing. M.S. also helped to finalize the text for manuscript.
Code development: N.C. and K.D. developed the code. M.S. and A.B. supervised the code development and plotting of figures.
Acquisition of data: R.C. and M.S. contributed in data collection and pre-processing.
Critical revision: R.C. and M.S. did the critical revision of the manuscript.
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Kumar, B., Chattopadhyay, R., Singh, M. et al. Deep learning–based downscaling of summer monsoon rainfall data over Indian region. Theor Appl Climatol 143, 1145–1156 (2021). https://doi.org/10.1007/s00704-020-03489-6
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DOI: https://doi.org/10.1007/s00704-020-03489-6