A robust deep learning model can potentially forecast accurate Indian Ocean Dipole and El-Niño-Southern Oscillation indices for climate prediction and informed decision-making in the Indian subcontinent.
Authors
Harshit Tiwari, Researcher, Centre Tecnològic de Telecomunicacions de Catalunya, Barcelona, Spain; Formerly, Department of Applied Sciences, National Institute of Technology, Delhi, India
Prashant Kumar, Department of Applied Sciences, National Institute of Technology, Delhi, India
Ramakant Prasad, Department of Applied Sciences, National Institute of Technology, Delhi, India
Kamlesh Kumar Saha, Department of Applied Sciences, National Institute of Technology, Delhi, India
Anurag Singh, Department of Computer Science and Engineering, National Institute of Technology, Delhi, India
Hocine Cherifi, ICB UMR 6303 CNRS, University of Burgundy, Dijon, France
Rajni, Associate Professor, Jindal Global Business school, O. P. Jindal Global University, Haryana, 131001, India
Summary
Accurate forecasting of the Indian Ocean Dipole (IOD) and El-Niño-Southern Oscillation (NINO3.4) is crucial for understanding regional weather patterns in the Indian subcontinent. Addressing the challenges associated with IOD and NINO3.4 prediction, a robust multi-task autoregressive deep learning model is introduced for precise forecasting of these indices and key grid projections sea surface temperature (SST), surface-level pressure gradient (SLG), and horizontal wind velocity (U-Comp) over a short to mid-term window (20 months).
Utilizing spatiotemporal (SST, SLG, U-Comp) and temporal (IOD and NINO3.4) modalities, the proposed model predicts future IOD and NINO3.4, as well as SST, SLG, and U-Comp, in an autoregressive scheme. The multi-task learning component regularizes the model, effectively capturing the evolving dynamics of global patterns conditioned on IOD and NINO3.4.
The comprehensive evaluation explores various task settings, including a duo-setting that predicts IOD or NINO3.4 with spatiotemporal information, showcasing notable proficiency. In a multi-task environment, where both temporal IOD, NINO3.4, and spatiotemporal SST, SLG, U-Comp are predicted, the model successfully forecasts IOD and NINO3.4 indices alongside grid projections with modest accuracy in root mean square error (RMSE). To enhance the model’s interpretability regarding spatiotemporal dynamics, a tailored version of Grad-CAM is employed, providing critical insights for climate prediction. This research advances climate prediction models, offering a comprehensive framework with significant implications for informed decision-making in the Indian subcontinent’s climatic context.
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