Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

• Published in: Climate dynamics 2014-05, Vol.42 (9-10), p.2801-2815
• Main Authors: Abhilash, S, Sahai, A. K, Borah, N, Chattopadhyay, R, Joseph, S, Sharmila, S, De, S, Goswami, B. N, Kumar, Arun
• Format: Article
• Language: English
• Subjects: Analysis; Climate models; Climate system; Climatology; Climatology. Bioclimatology. Climate change; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Environmental aspects; Environmental monitoring; Exact sciences and technology; External geophysics; Forecasts and trends; Geophysics. Techniques, methods, instrumentation and models; Geophysics/Geodesy; Madden-Julian oscillation; Meteorology; monitoring; monsoon season; Monsoons; Oceanography; Oscillators; prediction; probability; rain; Rainfall; Seasons; Tidal waves; Topical Collection on Climate Forecast System Version 2 (CFSv2); Weather forecasting
• ISSN: 0930-7575; 1432-0894
• DOI: 10.1007/s00382-013-2045-9

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001–2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.