14 Apr 2010SASCOF-I Meeting Apr 2010 PREDICTION OF MONSOON 2010 Indian Institute of Tropical Meteorology Seasonal Forecasting of Indian Summer Monsoon Using Empirical Models

14 Apr 2010SASCOF-I Meeting Apr 2010 MODEL- 1 BAWISKAR S M CHIPADE M D PURANIK P V A simple regression equation based on the series of AISMR and effective K.E. of waves 1, 3 & 4 is given of 850hPa wind for February. Ref : “Energetics of lower tropospheric planetary waves over mid latitudes: Precursor for Indian summer monsoon” by S.M.Bawiskar et al J. Earth Syst. Sci. October No

14 Apr 2010SASCOF-I Meeting Apr 2010

14 Apr 2010SASCOF-I Meeting Apr 2010 MODEL -2 S.S.DUGAM S.B.KAKADE

14 Apr 2010SASCOF-I Meeting Apr 2010 Actual and Estimated Rainfall

14 Apr 2010SASCOF-I Meeting Apr 2010 MODEL -3 A.A Munot and K. Krishnakumar REF: Long range forecast of India summer monsoon rainfall: Munot, A. A. and Krishna Kumar, K., Journal of Earth system sciences, 2007, Predictors Month/ Season Spatial Domain CC with ISMR( ) P1: 1000 hPa temperaturePJJA20N-25N,62.5E-67.5E0.51 P2: 850 hPa temperaturePJJA30N-35N,112.5E-117.5E-0.63 P3: 700 hPa temperaturePOCT15N-20N,15E-25E 0.65 P4: 200 hPa meridional windPNOV10N-17.5N,112.5E-117.5E-0.64

14 Apr 2010SASCOF-I Meeting Apr 2010 The forecasted values explain 81% of the observed variance, cc=0.9, RMSE 41 mm.

14 Apr 2010SASCOF-I Meeting Apr 2010 MODEL -4 A. K. Sahai Predictor is global SST. THE PREDICTION SCHEME IS BASED ON Sahai AK, Grimm AM, Satyan V, and Pant GB (2002) Prospects of prediction of Indian summer monsoon Rainfall using globl SST anomalies, IITM Research Report, No. RR-093. Sahai AK, Grimm AM, Satyan V, and Pant GB (2003) Long lead prediction of Indian summer monsoon rainfall from Global SST evolution, Clim. Dyn. 16, Sahai AK, Mandke SK, Shinde MA, Chattopadhyay R, Joseph S and Goswami BN (2007) Experimental seasonal forecast of Indian summer monsoon 2007: statistical and dynamical models, IITM Research Report, No. RR Sahai AK, Chattopadhyay R, and Goswami BN (2008) A SST based large multi- model ensemble forecasting system for Indian summer monsoon rainfall, Geo. Res. Let., 35, L19705, DOI /2008/GL ISSN

14 Apr 2010SASCOF-I Meeting Apr 2010 Statistical Forecast 2010 All India seasonal mean (% departure) Bawiskar et al. +12% ±5 Dugam et al. -6% ±4 Munot et al. +2% ±4 Sahai et al. -9.5% ±7

14 Apr 2010SASCOF-I Meeting Apr 2010 Scientific Basis Indian summer monsoon has a major component of variability that operates on subcontinental and seasonal scales. The principal scientific basis of any seasonal climate forecasting model is the premise that the lower-boundary forcings (SST, sea-ice cover, land-surface temperature, albedo, vegetation cover and type, soil moisture, snow cover, etc.), which evolve on a slower time-scale than the weather systems themselves, can give rise to significant predictability of statistical characteristics of large scale atmospheric events, in the tropics (Charney and Shukla, 1981). Parameters representing these conditions, global as well as regional, provide the handle for seasonal prediction.

14 Apr 2010SASCOF-I Meeting Apr 2010 Statistical Seasonal Prediction Approach Forecasts of seasonal anomalies of a particular variable over a particular region are attempted, by statistical methods, when significant links are found in pre-season teleconnections (predictors) with that variable (predictand). For a consistent prediction scheme the following criteria should ideally be satisfied: 1. 1.the predictor should possess a spatial scale which encompasses the phenomenon to be predicted; 2. 2.the predictor should possess a timescale that is very much longer than that which is being predicted in order to provide sufficient lead time; 3. 3.the predictor should be the active phenomenon and the phenomenon to be predicted should be passive, i.e., a reasonable ‘cause and effect’ relationship should be apparent; 4. 4.the statistical relationships need to be both stationary and significant.

14 Apr 2010SASCOF-I Meeting Apr 2010 Predictor is global SST. THE PREDICTION SCHEME IS BASED ON Sahai AK, Grimm AM, Satyan V, and Pant GB (2002) Prospects of prediction of Indian summer monsoon Rainfall using globl SST anomalies, IITM Research Report, No. RR-093. Sahai AK, Grimm AM, Satyan V, and Pant GB (2003) Long lead prediction of Indian summer monsoon rainfall from Global SST evolution, Clim. Dyn. 16, Sahai AK, Mandke SK, Shinde MA, Chattopadhyay R, Joseph S and Goswami BN (2007) Experimental seasonal forecast of Indian summer monsoon 2007: statistical and dynamical models, IITM Research Report, No. RR Sahai AK, Chattopadhyay R, and Goswami BN (2008) A SST based large multi- model ensemble forecasting system for Indian summer monsoon rainfall, Geo. Res. Let., 35, L19705, DOI /2008/GL ISSN

14 Apr 2010SASCOF-I Meeting Apr 2010 Predicted and observed ISMR anomalies for the model verification period ( ). Sahai et.al. 2002

14 Apr 2010SASCOF-I Meeting Apr 2010 Real Time performance of the model Sahai et. al., IITM Res. Rep. 2007

14 Apr 2010SASCOF-I Meeting Apr 2010  Slowly varying large scale external boundary forcing arising from ocean-atmosphere interactions can give reasonable real-time forecast of ISMR. However it is limited by ‘internally’ generated IAV arising from convective feedback and scale interactions involving fast processes. Many studies have brought out that the challenge in predicting the Indian summer monsoon arises from the fact that ‘internal’ IAV contributes to a large fraction of IAV of the Indian summer monsoon. (Goswami 1998, Kang et al. 2004, Cherchi and Navarra 2003, Goswami and Xavier, 2005)

14 Apr 2010SASCOF-I Meeting Apr 2010 Set 1

14 Apr 2010SASCOF-I Meeting Apr 2010 Set 2

14 Apr 2010SASCOF-I Meeting Apr 2010 Decadal and Secular variability of predictability

14 Apr 2010SASCOF-I Meeting Apr 2010 Set 1 and Set 2 both have similar prediction skill in the hindcast mode (CC≈0.62,RMSE≈7%), however they predict 2002 and 2004 with different skill Set 1 prediction-17%-6% Set 2 Prediction+11%+9% Observed-19%-13%

14 Apr 2010SASCOF-I Meeting Apr 2010 The seminal role played by ‘internal’ variability in the predictability of AIR demands a probabilistic approach for prediction of AIR. As a result of limited skill of current dynamical models in predicting AIR, empirical models are still needed to provide useful guidance (Rajeevan et al. 2007). Sahai AK, Chattopadhyay R, and Goswami BN (2008) A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall, Geo. Res. Let., 35, L19705, DOI /2008/GL ISSN

14 Apr 2010SASCOF-I Meeting Apr 2010 The ROC curve showing the verification of the forecasted category DROUGHTS ARE HIGHLY PREDICTABLE Sahai et. al., 2008, GRL

14 Apr 2010SASCOF-I Meeting Apr 2010 YEARBELOW -10 BET -10 AND -4 NORMALBET 10 AND 4 ABOVE

14 Apr 2010SASCOF-I Meeting Apr Seasonal Outlook: Probabilistic Approach

14 Apr 2010SASCOF-I Meeting Apr 2010 PROBABILITY YEAR BELOW -10 BET -10 AND -4 NORMAL BET 10 AND 4 ABOVE YEAR BELOW ZERO ABOVE ZERO % Prediction for 2010

14 Apr 2010SASCOF-I Meeting Apr 2010 Summary Seasonal outlook:  Model predicts below normal to deficient monsoon.  Based on the past performance of this model it can be concluded that monsoon activity during the year 2010 will be below normal.  The probability distribution shows that there may be few events of good rainfall.

14 Apr 2010SASCOF-I Meeting Apr 2010