The empirical orthogonal functions have been obtained for the individual summer monsoon (June through September) months using the grid point values of monthly 700 mb geopotential heights over Indian region. The data for 21 summer monsoon months for the years 1958 to 1978 have been used in the present computation.

The major variance reduction is due to the first three dominant functions accounting over 80% of the total variance in each month. The variance reduction only due to the first function ranges from 45 to 65%.

The first function has in-pbase oscillation throughout the area indicating that the area under study is homogeneous and the centre of the oscillation lies over northwest India. The amplitudes of the first function also show generally quasipers stence in their sign within a season. The second function has two centres of action over the region of monsoon trough which are in phase. The third function has also two centres oriented in the east-west direction but they are in the opposite phase.

Fairly large values of correlation coefficients between the patterns of the different monsoon months suggest that the patterns for these months corresponding to the first and the second functions respectively are quite similar. The patterns for these months also evolve with time in a related way. The spectrum analysis to the time series of amplitudes indicates the presence of the quasi-periodicity of 3 years during these monsoon months. The amplitudes corresponding to the dominant functions are found to be significantly related with the rainfall of central and western parts of India

The stochastic dynamic method of weather prediction (SDP) has been suggested recently for better understanding of the numerical weather prediction. The SDP is described using a simple one-dimensional advection equation. The salient features of the method, its scope and limitations, are discussed.

The Indian Climate Research Programme (ICRP) focuses on the study of climate variability and its impact on agriculture. To address the role of the Bay of Bengal in monsoon variability, a process study was organised during July–August 1999, deploying research ships, buoys, INSAT, coastal radar and conventional observational systems to collect information about the coupled ocean-atmosphere system over the warm waters of the Bay of Bengal. The paper gives the background of the ICRP and the organisation and implementation of the Bay of Bengal Monsoon Experiment (BOBMEX) in its field phase.

Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.