Jayanti Pal

The influence of El Niño on the Indian summer monsoon rainfall anomaly: a diagnostic study of the 1982/83 and 1997/98 events

AbstractThe Indian summer monsoon of 1982 and 1997 depicts disparities, however, maximum sea surface temperature anomaly over Niño 3 region is observed in the following winter of both the years. The inter-annual variation of sea surface temperature anomaly shows maximum peak during 1982/83 and 1997/98 El Niño events. The inter-annual variation of multivariate ENSO index also supports the above observation. The analyses of the entire tropical Pacific basin including the equatorial region reveal an anomalous behavior of the mean sea level pressure (MSLP) and the convective activities. The observations further reveal that the negative anomaly in monsoon rainfall over India prevails throughout the monsoon season except for the month of August in 1982, while in the year 1997 the monsoon rainfall anomaly shows random variations. The comparison between the summer monsoon rainfall of 1982 and 1997 depicts that the magnitude of the positive anomaly is same in the month of August. The condition over tropical Pacific during 1982/83 and 1997/98 has been investigated through the variation of outgoing long wave radiation (OLR), MSLP and pressure vertical velocity. The time–longitude plots of OLR and MSLP reveal the changes in pressure distribution and convective pattern over the tropical equatorial Pacific. The zonal and meridional cross section of pressure vertical velocity over the tropical Pacific and tropical Indian Ocean facilitates to understand the strength of the vertical motion during the monsoons of 1982 and 1997.

Nowcasting Bordoichila with a composite stability index

In operational forecast, the stability indices either individually or in combination are utilized to assess the predictability of local severe storms over a region. The objective of the present study is to identify such stability indices to assess the predictability of Bordoichila of Guwahati, India, during the pre-monsoon season (April–May) aiming to formulate a composite stability index using the most pertinent indices for nowcasting Bordoichila with considerable precision. Bordoichila, meaning the angry daughter of Assam, represents local severe storms of Guwahati during the pre-monsoon season. Precise forecast of Bordoichila is essential to mitigate the associated catastrophe over Guwahati. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant stability indices for the prevalence of Bordoichila. The method of normal probability distribution is implemented to identify the threshold ranges of the selected indices. The stability indices that are selected with appropriate ranges are lifted index, Showalter index (SI), cross total index (CTI), vertical total index, totals total, convective available potential energy, convective inhibition energy, SWEAT and bulk Richardson number. The forecast skill scores are estimated with the selected indices. The best predictor indices identified for the prevalence of Bordoichila are the cross total index (CTI) and Showalter index (SI). A composite stability index, Bordoichila prediction index, is formulated with CTI and SI with proper weightages. The forecast with BPI is validated with the observations of India Meteorological Department for the year 2007 and is implemented for real-time forecast for the years 2009 and 2011.

Cloud — Aerosol interaction during lightning activity over land and ocean: Precipitation pattern assessment

The present study attempts to identify the land - ocean contrast in cloud - aerosol relation during lightning and non-lightning days and its effect on subsequent precipitation pattern. The thermal hypothesis in view of Convective Available Potential Energy (CAPE) behind the land - ocean contrast is observed to be insignificant in the present study region. The result shows that the lightning activities are significantly and positively correlated with aerosols over both land and ocean in case of low aerosol loading whereas for high aerosol loading the correlation is significant but, only over land. The study attempts to comprehend the mechanism through which the aerosol and lightning interact using the concept of aerosol indirect effect that includes the study of cloud effective radius, cloud fraction and precipitation rate. The result shows that the increase in lightning activity over ocean might have been caused due to the first aerosol indirect effect, while over land the aerosol indirect effect might have been suppressed due to lightning. Thus, depending on the region and relation between cloud parameters it is observed that the precipitation rate decreases (increases) over ocean during lightning (non-lightning) days. On the other hand during non-lightning days, the precipitation rate decreases over land.

Long Range Forecast of Indian Summer Monsoon Rainfall using Artificial Neural Network Model

Correspondence Sutapa Chaudhuri, Department of Atmospheric Sciences, University of Calcutta, 51/2, Hazra Road, Kolkata—700 019, India. Email: sutapa.chaudhuri@gmail.com 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.