No abstract

No abstract

Impact of bias correction of sea surface temperature (SST) forecast on extended range (ER, ∼3-4 weeks) prediction skill is studied using the bias-corrected forecasted SST from Climate Forecast System version 2 (CFSv2) as the boundary condition for running the Global Forecast System version 2 (GFSv2) model. Potential predictability limit is comparable (∼16 days) for both bias-corrected GFSv2 (GFSv2bc) and CFSv2. Prediction skills of active and break spells and of low-frequency monsoon intraseasonal oscillations is higher for GFSv2bc at all lead pentads. Although initially same, predictability error after 14 days grows slightly faster for GFSv2bc compared to CFSv2. Bias correction in SST has minimal impact in short-to-medium range, while substantial influence is felt in ER between 12-18 days.

This study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is developed, which includes ensemble-based uncertainty estimates. Statistical skill of this CFS-based Grand Ensemble Prediction System (CGEPS) is better than the best participating SME configuration, because increased ensemble spread reduces overconfidence errors.

No abstract