Development of CFS based Grand Ensemble Prediction System for the Extended Range Forecasting of Indian Summer Monsoon

Realizing the importance and usefulness of the extended range (ER) monsoon forecast up to 2-3 weeks, the Ministry of Earth Sciences, Government of India in collaboration with National Center for Environmental Prediction, USA has adopted Climate Forecast System (CFS) model as the base model to improve the monsoon prediction capabilities over Indian Monsoon Region. Recognizing the demand for the multi-model ensemble (MME) prediction system by the operational user community, a CFS based Grand MME prediction system (CGMME) has been devised in this study. The ensemble members are generated not only by perturbing the initial condition, but also using different configurations of the same model, i.e., the CFSv2. Each of these configurations of CFSv2 is created to address the role of different physical mechanisms known to have control on the error growth in the extended range forecasts in the 15-20 day time scale. The final formulation of CGMME is based on 21 ensembles of the standalone atmospheric component of the CFS, i.e., Global Forecast System (GFS) forced with bias corrected forecasted SST from CFS, 11 low resolution CFST126 (~100km) and 11 high resolution CFST382 (~38km). Thus, we develop the multi-model consensus forecast for the extended range prediction of the Indian summer monsoon using a suite of different variants of CFS model at different resolution, physics, coupled and stand-alone atmospheric component etc. This coordinated international effort lead towards the development of specific tailor made regional forecast products over Indian region. The skill of deterministic and probabilistic categorical rainfall forecast as well the verification of large-scale low frequency monsoon intraseasonal oscillations (MISO) has been carried out using hindcast from 2001-2012 during the monsoon season in which all models are initialized at every five days starting from 16May to 28 September. The skill of deterministic forecast from CGMME is better than the best participating single model ensemble configuration (SME). The CGMME approach is believed to quantify the uncertainty in both initial conditions and model formulation. The main improvement is attained in the probabilistic forecast which is because of an increase in the ensemble spread, thereby reducing the error due to over-confident ensembles in a single model configuration. For probabilistic forecast, three tercile ranges are determined by ranking method based on the percentage of ensemble members from all the participating models falls in those three categories. Hence CGMME further added value to both deterministic and probability forecast compared to raw SME's and this better skill is probably flows from large spread and improved spread-error relationship. This CGMME system is currently capable of generating ER prediction in real time and successfully delivering its experimental operational ER forecast of Indian summer monsoon for the last few years.