[1] Daily gridded (1°Â 1°) rainfall data prepared by the India Meteorological Department for the period have been used in this study to examine possible changes in the frequency of rain events in India in terms of their duration and intensity per day. So far as the duration is concerned, a rain event is classified as short, long, dry, or prolonged dry spell. Similarly in terms of intensity, a rainy day is considered as low, moderate, or heavy. Changes in the frequency of these events have great relevance from the point of view of climate change. Threshold and limiting values for defining the heavy and moderate rain days are calculated in accordance with the gamma probability distribution. Results show that the frequencies of moderate and low rain days considered over the entire country have significantly decreased in the last half century. On the basis of the duration of rain events it is inferred that long spells show a significant decreasing trend over India as a whole while short and dry spells indicate an increasing tendency with 5% significance. The characteristics of rain events are also examined over six homogenous rainfall zones separately since the spatial distribution of rainfall over India shows large variability. In this study, the changes in the frequencies of different categories of rain events suggest weakening of the summer monsoon circulation over India. This hypothesis of a weakening of monsoon circulation is supported by significant reduction in the 850 hPa wind fields in the National

A multi-predictor logistic regression model has been developed for probabilistic forecasts of domain average rainfall on monthly timescale for three study regions namely, India as a whole, and two homogeneous meteorological subdivisions of India, i.e. Orissa on the east coast and Gujarat on the west coast. The time series of the monthly total observed rainfall as the predictand variable was constructed from the gridded (1°× 1°) daily rainfall produced by India Meteorological Department, and those of the predictor data sets from 1-month lead forecasts of several atmospheric and oceanic variables produced by the 'Development of a European Multi-model Ensemble system for seasonal to inTERannual prediction (DEMETER)' project of European Centre for Medium-Range Weather Forecasts (ECMWF). Multi-model ensembles of nine-member retrospective forecasts for the month of August generated by three constituent models of the DEMETER system, viz., ECMWF, United Kingdom Meteorological Office (UKMO) and Meteo France are used. The predictor variables (totally 36 in number) include direct model-predicted total precipitation and its inter-member standard deviation. A twostage procedure has been designed, where logistic regression is first computed for each individual variable and then for the variables ranked on the basis of Brier scores. The top-ranked variables (up to four) are used for fitting the multiple logistic regression model in a stepwise manner. The fitted model provides estimates of probability of the value of an observation exceeding a specified quantile (such as median) of the statistical distribution of the predictand variable. The model shows good performance in capturing the extreme rainfall years and appears to perform better than the direct model forecasts of total precipitation in respect of such years.

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The network covers a wide domain and addresses multiple aspects in agriculture, such as soil moisture, temperature, and humidity. Therefore, issues of precision agriculture at the output of the network are analyzed using wireless technology. The system is equipped with sensors for soil moisture and DHT11 for relative temperature and humidity. Current wireless sensor networks are widely used in a range of applications, such as precision agriculture, healthcare, and smart cities.