Singh, M and Acharya, N and Patel, P and Jamshidi, S and Yang, Z-L and Kumar, B and Rao, S and Gill, SS and Chattopadhyay, R and Nanjundiah, RS and Niyogi, D (2023) A modified deeA modified deep learning weather prediction using cubed sphere for global precipitationp learning weather prediction using cubed sphere for global precipitation. In: Frontiers in Climate, 4 .
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Abstract
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 “modified DLWP-CS” (MDLWP-CS). In this study, we transform the architecture from a temporal to a spatio-temporal mapping (multivariate setup), wherein precursor(s) of precipitation can be used as input. As a proof of concept, as a first simple case, a 2-m 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, which is the Global Forecast System (GFS). The fidelity of MDLWP-CS is much better compared to linear regression and the results are equivalent to GFS output in terms of daily precipitation prediction with 1 day lag. These results provide an encouraging framework for an efficient DT that can facilitate speedy, high fidelity precipitation predictions. Copyright © 2023 Singh, Acharya, Patel, Jamshidi, Yang, Kumar, Rao, Gill, Chattopadhyay, Nanjundiah and Niyogi.
| Item Type: | Journal Article |
|---|---|
| Publication: | Frontiers in Climate |
| Publisher: | Frontiers Media S.A. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | cubed sphere; deep convolutional neural networks; digital twins; precipitation; U-NET; weather prediction |
| Department/Centre: | Division of Mechanical Sciences > Centre for Atmospheric & Oceanic Sciences |
| Date Deposited: | 16 Feb 2023 05:11 |
| Last Modified: | 16 Feb 2023 05:11 |
| URI: | https://eprints.iisc.ac.in/id/eprint/80299 |
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