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Forecasting Indian monsoon rainfall using regional circulation fields as predictors : an ensemble neural network approach

University of British Columbia

Pre-monsoon principal components of circulation fields covering the South Asian subcontinent were used as predictors for all-India summer monsoon rainfall (AISMR) during the period 1958- 1998. Predictive skill and stationarity of non-linear ensemble neural network and linear multiple regression model relationships were assessed using a Monte Carlo-based resampling procedure. Monsoon precursor signals represented by the PCs were investigated using a model sensitivity analysis and comparisons were made with recent observational and general circulation modelling studies. Results suggest the presence of coherent, stable predictive relationships between AISMR and the SLP field during November (median r=0.5, p=0.05), as well as between AISMR and the 200 hPa geopotential height field during May (median r=0.67, p < 0.005). The latter relationship was essentially linear, and appears to be related to the winter-summer transition between a flow regime dominated by the subtropical westerly jet stream to one dominated by the tropical easterly jet stream, as well as to changes in strength and position of the upper tropospheric Tibetan anticyclone. The former relationship was nonlinear, appearing in essentially the same form at the 850 hPa level (median r=0.37, p=0.06). Possibly related to anomalous SSTs in the Arabian Sea, further work is required to determine physical mechanisms responsible for predictive skill at this lead-time. Weaker relationships were observed between summer monsoon rainfall and PCs of the SLP field during May (median r=0.47, p=0.10) and PCs of the 850 hPa geopotential height field during January (median r=0.40, p=0.10). May SLP-rainfall relationships were not significant until the early 1970s but remained relatively stable for the remainder of the record. Spatially, maximum correlations and model sensitivities were centred over northwest India and Pakistan, suggesting a link with pre-monsoon heating and development of the heat low over Pakistan. Relationships between January 850 hPa geopotential heights and rainfall were nonstationary, only showing significant correlations during portions of the record. Significant differences in skill between neural network and multiple linear regression models were present at this level and lead-time, consistent with indications of nonlinearity and interactions between inputs suggested by the sensitivity analysis. Further work is required to determine whether these relationships have physical meaning or whether they are simply a statistical artifact.

Thesis/Dissertation

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2009-07-09

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http://hdl.handle.net/2429/10555

Geography

University of British Columbia

UBCV

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