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Title of Journal: Clim Dyn

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Abbravation: Climate Dynamics

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Springer Berlin Heidelberg

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DOI

10.1006/bcmd.1997.0160

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1432-0894

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Prediction and monitoring of monsoon intraseasonal

Authors: S Abhilash A K Sahai N Borah R Chattopadhyay S Joseph S Sharmila S De B N Goswami Arun Kumar
Publish Date: 2014/01/21
Volume: 42, Issue: 9-10, Pages: 2801-2815
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Abstract

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 5day 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 signaltonoise 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 largescale 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 largescale 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 leadThis paper is a contribution to the Topical Collection on Climate Forecast System Version 2 CFSv2 CFSv2 is a coupled global climate model and was implemented by National Centers for Environmental Prediction NCEP in seasonal forecasting operations in March 2011 This Topical Collection is coordinated by Jin Huang Arun Kumar Jim Kinter and Annarita MariottiIITM is fully supported by the Ministry of Earth Sciences Government of India New Delhi India We thank NCEP for reanalysis datasets and for transferring the CFS system under the MoU between MoES and NOAA We also thank IMD for daily rainfall data SS thanks the Council of Scientific and Industrial Research New Delhi for a research fellowship

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