no code implementations • 2 Jul 2022 • Bipin Kumar, Kaustubh Atey, Bhupendra Bahadur Singh, Rajib Chattopadhyay, Nachiket Acharya, Manmeet Singh, Ravi S. Nanjundiah, Suryachandra A. Rao
To test the efficacy of different DL approaches, we apply four different methods of downscaling and evaluate their performance.
no code implementations • 24 Dec 2021 • Manmeet Singh, Bipin Kumar, Rajib Chattopadhyay, K Amarjyothi, Anup K Sutar, Sukanta Roy, Suryachandra A Rao, Ravi S. Nanjundiah
This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied.
no code implementations • 20 Jun 2021 • Manmeet Singh, Bipin Kumar, Suryachandra Rao, Sukhpal Singh Gill, Rajib Chattopadhyay, Ravi S Nanjundiah, Dev Niyogi
This study is a proof-of-concept showing that residual learning-based UNET can unravel physical relationships to target precipitation, and those physical constraints can be used in the dynamical operational models towards improved precipitation forecasts.
no code implementations • 23 Nov 2020 • Bipin Kumar, Rajib Chattopadhyay, Manmeet Singh, Niraj Chaudhari, Karthik Kodari, Amit Barve
In this work, we employed three deep learning-based algorithms derived from the super-resolution convolutional neural network (SRCNN) methods, to precipitation data, in particular, IMD and TRMM data to produce 4x-times high-resolution downscaled rainfall data during the summer monsoon season.