NCC Report No. 1 - (IMD), Pune

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Table – 3 : Inter-correlation among the predictors used in SET-I & SET-II. For the computation ofthe C.Cs the data for the period 1958-2000 were used. C.C. values significant at and above 5%level are shown in bold letters.A1/J1 A2/J2 A3/J3 A4 A5 A6 J4 J5 J6A1/J1 1.00A2/J2 0.02 1.00A3/J3 -0.29 0.18 1.00A4 -0.04 0.16 0.23 1.00A5 0.05 -0.06 0.04 -0.69 1.00A6 0.22 -0.47 -0.07 -0.06 -0.01 1.00J4 0.24 -0.53 -0.18 -0.14 -0.03 0.61 1.00J5 0.22 -0.06 -0.17 -0.39 0.46 0.21 0.01 1.00J6 0.08 -0.47 -0.04 -0.20 0.06 0.17 0.12 0.03 1.00Table - 4a : Best 5 models selected by Jacknife method on SET –I and theirerror statistics for the period 1951-2000.Regression Model<strong>No</strong> ofPredictorsError Measures(1951-2000)PredictorsR RMSE RModel 1 4 A1, A2, A4and A5 0.47 7.59 0.71Model 2 3 A1, A2 and A5 0.43 7.91 0.68Model 3 5 A1, A2, A3, A4 and A5 0.43 7.76 0.70Model 4 5 A1, A2, A3, A5 and A6 0.43 7.75 0.70Model 5 4 A1, A2, A4, and A6 0.40 8.02 0.67Table - 4b : Best 5 models selected by Jacknife method on SET -II and their errorstatistics for the period 1958-2000.Regression Model<strong>No</strong> ofPredictorsError Measures(1958-2000)PredictorsR RMSE RModel 1 5 J1, J3, J4, J5 and J6 0.56 6.76 0.78Model 2 4 J1, J3,J4 and J5 0.55 6.88 0.77Model 3 5 J1, J2, J3, J5 and J6 0.55 6.80 0.78Model 4 6 J1, J2, J3, J4, J5 and J6 0.55 6.74 0.78Model 5 4 J1, J2, J5and J6 0.54 6.99 0.7636
Table – 5 : Model statistics of the Ensemble Multiple Regression Models (EMR-I and EMR-II).The combination of predictors in the selected 5 MR models used forthe ensemble models are given in Table-3 a & b.ModelC.CEMR – IBias(% of LPA)RMSE(% of LPA)C.CEMR - IIBias(% ofLPA)RMSE(% ofLPA)1974-20041981-20041974-20041981-20041974-20041981-20041981-20041981-20041981-2004Model 1 0.84 0.84 2.56 4.11 6.78 7.34 0.81 -1.25 6.04Model 2 0.82 0.80 3.42 4.67 7.27 7.96 0.73 -0.08 6.82Model 3 0.85 0.84 2.35 3.80 6.94 7.73 0.76 0.53 6.67Model 4 0.82 0.86 2.52 4.12 6.79 7.18 0.83 -0.99 5.91Model 5 0.85 0.81 3.22 4.54 7.41 8.37 0.78 1.26 6.60Ensemble 0.84 0.84 2.81 4.23 6.82 7.54 0.81 -0.10 5.89Table – 6 : Model statistics of the Principle Component Regression Models (PCR-I and PCR-II).Model Performance ParametersModel C.CBiasRMSE(% of LPA) (% of LPA)1974-20041981-20041974-20041981-20041974-20041981-2004PCR-I 0.81 0.86 3.85 5.52 7.63 7.93PCR-II 0.81 0.47 6.23Table – 7 : Model statistics of the Projection Pursuit Regression Models (PPR-I and PPR-II).ModelC.CModel Performance ParametersBias(% of LPA)RMSE(% of LPA)1974-20041981-20041974-20041981-20041974-20041981-2004PPR-I 0.81 0.82 1.52 3.03 6.70 6.89PPR-II 0.78 -0.92 6.4237
Page 2: NEW STATISTICAL MODELS FORLONG RANG
Page 5 and 6: For the long range forecasting (LRF
Page 9 and 10: computed as the average of surface
Page 11: tropical Pacific Ocean in boreal wi
Page 14 and 15: to a model with smallest model size
Page 16 and 17: using the best development period a
Page 18 and 19: the most “interesting” projecti
Page 20 and 21: Where, Net j denotes the weighted s
Page 22 and 23: The climatological forecast for a g
Page 24 and 25: period of 1981-2004 were 6.89% and
Page 26 and 27: chance of failure but no guarantee
Page 28 and 29: EMR-II were based on the data of 19
Page 30 and 31: ReferencesAngell J. K., 1981, Compa
Page 32 and 33: Iyengar R.N and Raghukanth, S.T.G.,
Page 34 and 35: Parthasarathy, B., Rupa Kumar, K. a
Page 36 and 37: Walker, G. T., 1923, Correlation in
Page 40 and 41: Table - 8: Model statistics of the
Page 42 and 43: SE INDIAN OCEAN SST ANOMALYSTD. ISM
Page 44 and 45: N. ATLANTIC MSLP ANOMALYSTD. ISMR32
Page 46 and 47: ADJUSTED R-SQUARE Vs MODEL SIZE: DA
Page 48 and 49: PERFORMANCE OF ENSEMBLE MODEL: JUNE
Page 50 and 51: 25PERFORMANCE OF ANN MODELS FOR THE

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