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Predicting Indian summer monsoon onset over Kerala on extended range using an Ensemble Prediction System based on CFSv2
Predicting Indian summer monsoon onset over Kerala on extended range using an Ensemble Prediction System based on CFSv2
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Thursday, 8 January 2015
The beginning of Indian summer monsoon (ISM) season is marked by its onset over Kerala, which is situated at the southern tip of Indian subcontinent. The prediction of monsoon onset over Kerala (MOK) at least 2-3 weeks in advance is very much crucial due to its socio-economic impact. Operationally, the India Meteorological Department (IMD) declares monsoon onset over Kerala (MOK) based on the rainfall over various stations over Kerala, the value of outgoing long wave radiation (OLR) as well as the strength and vertical extent of the lower tropospheric zonal wind (Pai and Rajeevan 2009). In this study, the emphasis is to devise a criterion for the operational real-time prediction of MOK utilizing the circulation as well as rainfall from the indigenously developed Grand Ensemble Prediction System (GEPS) based on the Climate Forecast System (CFS) model version 2 developed at National Centre for Environmental Prediction, USA. This endeavour is a part of the ongoing operational real-time extended range prediction of ISM initiated at the Indian Institute of Tropical Meteorology (IITM), India under the National Monsoon Mission Project, Govt. of India. The multi-model ensemble (MME) from CFS GEPS (hereafter termed as CGEPS; Abhilash et al. 2014) is developed based on three different variants of CFS model at different resolutions, physics, coupled and stand-alone atmospheric component etc. Total 43 ensemble members have been produced independently from 3 variants of CFS model to generate the CGEPS and forecast consensus is done by making simple average among the members. For predicting MOK, the extended range forecasts from 16 May initial condition during the period 2001-2014 have been used. Here we devise a criterion for predicting MOK based on two indices – one from rainfall over Kerala (hereafter termed as ROK index) and another based on the strength of Low level Jet (hereafter termed as LLJ index). ROK is defined as the rainfall area averaged over 74°-78°E; 8°-12°N; whereas LLJ is defined as the zonal wind at 850 hPa averaged over 55°-75°E; 5°-12°N. 30 day average values of forecasted ROK and LLJ starting from 17 May has been calculated. MOK is defined on the date at which both ROK and LLJ > 50% and either of them is > 70% of their average value, for the next consecutive 5 days. MOK date has been calculated for all 43 members of CGEPS and the mean of all of them is given as the final predicted MOK date. It is found that the predicted MOK dates during 2001-2014 are in agreement with the MOK dates declared by IMD. During most of the years, the difference between the actual and predicted MOK is <= days, with an exception in 2002 and 2003 where the difference is 8 and 9 days respectively. The composite analysis of rainfall and low level circulation during MOK indicates that the MME could predict the spatial pattern associated with MOK realistically, compared to observations. The model could reasonably predict the evolution of various dynamical and thermo-dynamical parameters during MOK. Although the model has an inherent cold temperature bias, it exhibits a stronger-than-observed meridional (north-south) gradient of tropospheric temperature (TT gradient; Xavier et al. 2007) during MOK. Thus, it could be concluded that the MME based on CGEPS has reliable potential in the real-time prediction of MOK on extended range.
References Abhilash S, Sahai AK, Borah N, Joseph S, Chattopadhyay R, Sharmila S, Rajeevan M, Mapes B and Kumar A (2014) Improved Spread-Error Relationship and Probabilistic Prediction from CFS based Grand Ensemble Prediction System, submitted to BAMS Pai DS and Rajeevan M (2009) Summer monsoon onset over Kerala: New Definition and prediction. J. Earth Syst. Sci. 118, 123-135 Xavier PK, Marzin C and Goswami BN (2007) An objective definition of the Indian summer monsoon season and a new perspective on ENSO-monsoon relationship. Quart J Meteorol Soc 133: 749-764