The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

sub-EASMs behave differently when land factors included; and (5) Do the statistical models
based on observational data have good agreement with climate model simulations and can
these climate models elucidate the physical mechanisms governing monsoon strength and
seasonality.
Spatial correlation, Empirical Orthogonal Function (EOF), and composite analyses will be
used to demonstrate the relationships between the EASM and the surrounding land surface
factors such as density of vegetation cover, snow cover, soil moisture, and irrigation extent.
Multivariate linear and non-linear regressions using stepwise methods will be applied to create
the forecast models for the EASM. <strong>The</strong>se will be compared against EOF analyses for
consistency. Observed relationships affecting the EASM will also be compared with results
from general circulation model experiments in order to illuminate the physical mechanisms
involved.
Seasonal Prediction of Rainfall over Peninsular India using a Principal Component Regression
Model
Speaker: Lorna R. Nayagam
Lorna R. Nayagam
Department of Atmospheric Sciences, Cochin University of Science and Technology
lorna@cusat.ac.in
Rajesh J.
Department of Atmospheric Sciences, Cochin University of Science and Technology
H. S. Ram Mohan
Department of Atmospheric Sciences, Cochin University of Science and Technology
<strong>The</strong> aim of the study is to develop a principal component regression model to predict the
summer monsoon rainfall over Peninsular India (PIR). Correlation between PIR and several
global parameters were found and those areas having 99% significance were selected for
making indices. <strong>The</strong> consistency of the relationship between these indices and PIR was
checked by doing a 15-year sliding window correlation (significant at 5% level). Seven indices
having high correlation were selected. Indices are relative humidity at 925hPa (apr), specific
humidity at 850hPa (apr), sea level pressure (may), zonal wind at 100 hPa (apr), 30 hPa (feb),
70 hPa (jan) and meridional wind at 10 hPa (jan) over different spatial locations. PCA is done
using these seven indices, for the period 1977-2005. Six components with eigen value greater
than two which explains the maximum variance were selected for further analysis. A multiple
linear regression model was developed using these components for the period 1977-1998.
<strong>The</strong> model has a multiple correlation of 0.917 and coefficient of determination of 84 %. <strong>The</strong> VIF
analysis of all the parameters that is retained in the model have values less than 2 indicating
an insignificant level of multicollinearity and has a Durbin Watson value of 1.54. <strong>The</strong> model
performance was assessed for 7 years (1999-2005) using statistical measures such as RMSE,
BIAS and ABSE. <strong>The</strong> model has a RMSE of 6.71 %, BIAS of 0.13% and ABSE of 5.28% of
long-term average. Climatological predictions were also made and found that RMSE, BIAS
and ABSE are 16.75%, -14.08% and 14.52% of long term average, respectively.
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