Multi-Model Multi-Physics Ensemble: A Futuristic Way to Extended Range Prediction System

Abstract

In an endeavor 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 sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP 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 forced with real-time bias-corrected sea-surface temperature 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 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

“…Ensemble forecasting (Buizza et al., 2019), where multiple realizations for a time and location are generated to account for future uncertainties, is part of the state‐of‐the‐art early warning services. Current ensembles are commonly obtained by applying different perturbations to the control (unperturbed) forecast, and should preferably be based on multiple models (Sahai et al., 2021). Ensemble forecasting is used to predict flooding (Alfieri et al., 2014; Ramos et al., 2007), droughts (Colossi & Tucci, 2020); and in hydro‐climate services, for example, for water resources management applications (Macian‐Sorribes et al., 2020; Pechlivanidis et al., 2020), agriculture (Villani et al., 2021) and hydropower (Contreras et al., 2020).…”

Northward propagation of convection over the Indian region in multiphysics multimodel ensemble

“…Using the National Centers for Environmental Prediction (NCEP) Coupled Forecast System (CFS) model version 2 (CFSv2) and its atmospheric component Global Forecast System (GFS) model with real‐time bias‐corrected sea‐surface temperature from CFS (Saha et al ., 2014), simulations are carried out for 15 years (2001–2015) (Sahai et al ., 2021; Kaur et al ., 2022). Every 1st, 6th, 11th, 16th, 21st, and 26th day of each month from May to September the models are initialized with NCEP climate forecast system reanalysis (Saha et al ., 2014).…”

“…Three convective parametrization schemes in combination with two microphysics parametrizations give rise to six different physics combinations, which are used for generating the multiphysics ensemble (Sahai et al ., 2021). The microphysics schemes used are Zhao and Carr (zc) (Zhao and Carr, 1997) and Ferrier (fer) (Ferrier et al ., 2002), and the deep convection schemes include simplified Arakawa–Schubert (sas) (Pan and Wu, 1995) and the revised Arakawa–Schubert (nsas) (Han and Pan, 2011).…”

“…However, the ocean resolution remains the same for the entire time period for CFSv2. Three convective parametrization schemes in combination with two microphysics parametrizations give rise to six different physics combinations, which are used for generating the multiphysics ensemble (Sahai et al, 2021).…”

“…MPMME is a part of the development of a new extended‐range ensemble forecasting framework under the National Monsoon Mission project (Rao et al ., 2019). The superiority of the MPMME strategy in simulating mean rainfall characteristics compared with the existing operational version for extended‐range prediction is documented in earlier studies (Sahai et al ., 2021; Kaur et al ., 2022). MPMME is under development, and preliminary simulations are generated from unperturbed ICs using a multiphysics approach along with the multimodel framework.…”