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This study develops an artificial neural network (ANN) model with a nonlinear perceptron rule for use in the long‐range forecasting (LRF) of Indian summer monsoon rainfall (ISMR). In developing the model, two predictor sets are adopted from the India Meteorological Department (IMD), SET‐I and SET‐II, to prepare the input matrix of the model, while the output is ISMR. The data used were collected over the period 1980–2017. The model is trained with input data from 1980 to 2012, and the skill of the model is estimated by validating the model output with observation during the period 2013–2017. The result reveals that that second‐stage forecast is better than first‐stage forecast due to the incorporation of a North Atlantic sea surface pressure anomaly and a North Central Pacific zonal wind anomaly at 850 hPa in the input matrix. The study further reveals that the multilayer perceptron (MLP) model with a back‐propagation algorithm is best among the ANN models used in the study. The prediction capability of the ANN model is also checked by comparing it with a multiple nonlinear regression (MNLR) model developed with the two predictor sets. The robustness of the prediction accuracy is estimated by computing Willmott's index for each of the ANN and MNLR models. [ABSTRACT FROM AUTHOR]

ABSTRACT: The present research was aimed at identifying the influence of the southern annular mode on the Indian summer monsoon ( ISM) by investigating the inter-annual as well as seasonal variation in the southern annular mode index ( SAMI) and the ISM rainfall. It is shown that each month of the ISM is influenced by the southern annular mode of different months. The association between the June SAMI and July rainfall was found to be significant as well as important. The investigation was carried out with the NCEP/ NCAR reanalysis dataset. The result shows that the monsoon rainfall anomaly is significantly and negatively correlated with the SAMI. The influence of the June SAMI is estimated with sea surface temperature ( SST), horizontal wind at 850 hPa pressure level, vertical wind and moisture convergence. The result shows the existence of warm sea surface at 30 ° S during high SAMI years which is surmised to control the cross-equatorial flow during the ISM. It was also observed that the enhanced southerly flow over the south of India is divided into two parts. The major flow is observed towards the western Arabian Sea, subsequently turning clockwise and moving towards the western part of India through the north Arabian Sea. The result further shows that an intense ascending motion from the equator to 30 ° N along the western coast coincides with the low rainfall region. These conditions may lead to an enhanced divergence zone over the western and northeastern region of India which, in turn, may result in less rainfall over India. [ABSTRACT FROM AUTHOR]

Precise forecasting of the track and intensity of tropical cyclones remains one of the top priorities for the meteorological community. In the present study multilayer feed forward neural nets with different architectures are developed to identify the best neural net for forecasting the track and intensity of tropical cyclones over the North Indian Ocean (NIO)with 6, 12 and 24 h lead time. Forecast errors are estimated with each neural net. The result reveals that the neural net architecture 1 (NNA 1) with 10 input layers, 2 hidden layers, 5 hidden nodes and 2 output layers provides the best forecast for both the track and the intensity of the tropical cyclones over NIO. Two cyclones of the same category in Saffir Simpson Hurricane Scale, namely Nargis and Phet, that occurred over the Bay of Bengal and the Arabian Sea of the NIO basin are considered in the present study for validation. The result reveals that the prediction errors (%) with NNA 1 model in estimating the intensity of the cyclones Nargis and Phet during the validation are 3.37, 8.29 and 9.74 as well as 6.38, 11.26 and 18.72 with 6, 12 and 24 h lead time, respectively. The mean track errors for 6, 12 and 24 h forecasts are observed to be 45, 69 and 89 km for cyclone Nargis and 54, 87 and 98 km for cyclone Phet. NNA 1 model is observed to perform better than NNA 2 and NNA 3 models and the existing numerical models. [ABSTRACT FROM AUTHOR]

The aim of the present study is to forecast thunderstorms over Kolkata (22°32′N, 88°20′E), India, during the pre-monsoon season (April-May) with graph spectral distance and entropy analysis. Graph vertices represent points connected by lines or edges, and lifting condensation level, convective condensation level, level of free convection, freezing level, level of neutral buoyancy and the surface level are taken as the input of the graph vertices. The variation in the most probable distance between the vertices is investigated. The result reveals a particular orientation of the vertex distances for thunderstorm days which is significantly different from the non-thunderstorm days. The reference graphs for thunderstorm and non-thunderstorm days are formed using the most probable vertex distances. The spectral distance between the reference graph and the graphs corresponding to thunderstorms are computed with the data collected during the period 1997-2009. The entropies, or the measure of disorderliness or uncertainty, are estimated for the graph distance matrices. The result shows that the thunderstorm days possess lower distance entropy than the non-thunderstorm days. This indicates that the reference graph that has been constructed for thunderstorms is more consistent. The result further depicts that the forecast accuracy through the present method is 98% with 1 h lead time, whereas the accuracy is 93% with 6 h lead time. The forecast is validated with the India Meteorological Department observations for the years 2007, 2008 and 2009. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]