Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System - Sahai, Atul K.; Kaur, Manpreet; Joseph, Susmitha; Dey, Avijit; Phani, R.; Mandal, Raju; Chattopadhyay, Rajib (Frontiers in Climate, 2021)
In an endeavour to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected SST forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skilful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this extended range prediction system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system (GFS) forced with real-time bias-corrected SST from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to three weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.