Agriculture is the backbone of the Indian economy and contributes ∼16% of gross domestic product and about 10% of total exports. Hence, accurate and timely forecasting of monthly Indian summer monsoon rainfall is very much in demand for economic planning and agricultural practices. Several methods and models, comprising dynamic and statistical models and combinations of the two, exist for monsoon forecasting. Here, a multi-model ensemble approach, combined with an artificial neural networking technique, was used to develop a soft-computing ensemble algorithm (SEA) to forecast the monthly and seasonal rainfall over the Indian subcontinent. Forecasts using January to May initial conditions along with observations during 1982-2014 were used to develop the model. The SEA compares well with observations.
Precipitable water over eastern and western parts of India have been computed using Monex-79 data from surface to 300 mb at a vertical interval of 50 mb. Fluctuations in precipitable water have been examined in relation to depressions and cyclones. The spectral analysis of eddy precipitable water was performed. Vertical variation of periodicity in different layers have been observed.
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