A modified deep learning weather prediction using cubed sphere for global precipitation - Singh, Manmeet; Acharya, Nachiketa; Patel, Pratiman; Jamshidi, Sajad; Yang, Zong-Liang; Kumar, Bipin; Rao, Suryachandra; Gill, Sukhpal Singh; Chattopadhyay, Rajib; Nanjundiah, Ravi S.; Niyogi, Dev (Frontiers in Climate, 2023)
Deep learning (DL), a potent technology to develop Digital Twin (DT), for weather prediction using cubed spheres (DLWP-CS) was recently proposed to facilitate data-driven simulations of global weather fields. DLWP-CS is a temporal mapping algorithm wherein time-stepping is performed through U-NET. Although DLWP-CS has shown impressive results for fields such as temperature and geopotential height, this technique is complicated and computationally challenging for a complex, non-linear field such as precipitation which depends on other prognostic environmental co-variables. To address this challenge, we modify the DLWP-CS and call our technique as “modified DLWP-CS” (MDLWP-CS). In this, we transform the architecture from a temporal to a spatio-temporal mapping (multivariate set up), wherein, precursor(s) of precipitation can be used as input. As a proof of concept, as a first simple case, 2-meter surface air temperature is used to predict precipitation using MDLWP-CS. The model is trained using hourly ERA-5 reanalysis and the resulting experimental findings are compared to two benchmark models, viz, the linear regression and an operational numerical weather prediction model; the Global Forecast System (GFS). The fidelity of MDLWP-CS is much better compared to linear regression and results are equivalent to GFS output in terms of daily precipitation prediction with one day lag. These results provide an encouraging framework for an efficient DT that can facilitate speedy, high fidelity precipitation predictions.