Debanjana Das

Impact of intra-seasonal oscillations of Indian summer monsoon on biogeochemical constituents of North Indian Ocean

The intra-seasonal perturbations in the atmospheric weather are closely related to the variability in the ocean circulation. NASA Ocean Biogeochemical Model (NOBM) couples the oceanic general circulation and the radiative forcing. The NOBM model products of nitrate, total chlorophyll, and mixed layer depth (MLD) collected during the period from 1998 to 2007 as well as the sea surface temperature (SST), precipitation, outgoing long wave radiation (OLR), and wind are considered in this study to identify the influence of intra-seasonal oscillation (ISO) of Indian summer monsoon (ISM) on the biogeochemical constituents of Bay of Bengal (BOB) (6°–22° N; 80°–100° E) and Arabian Sea (AS) (3°–17° N; 55°–73.5° E) of North Indian Ocean (NIO). The active and break phases are the most significant components of ISO during ISM. The result of the study reveals that the upper ocean biology and chemistry significantly vary during the said phases of ISM. The nitrate, total chlorophyll, and MLD are observed to be strongly correlated with the ISO of ISM. The result shows that, during ISO of ISM, the concentration of nitrate and chlorophyll is strongly and positively correlated both in BOB and AS. However, the correlation is more in AS, endorsing that the Arabian Sea is more nutrient reach than Bay of Bengal. Nitrate and MLD, on the other hand, are strongly but negatively correlated in the said basins of North Indian Ocean (NIO). The forcing behind the variability of the biogeochemical constituents of BOB and AS during active and break phases of ISM is identified through the analyses of SST, precipitation, OLR, and wind.

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.

An investigation on the predictability of thunderstorms over Kolkata, India using fuzzy inference system and graph connectivity

Abstract The purpose of this study was to develop a computing system (CS) with fuzzy membership and graph connectivity approach to estimate the predictability of thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The stability indices are taken to form the inputs of the CS. Ten important stability indices are selected to prepare the input of the fuzzy set. The data analysis during the period from 1997 to 2006 led to identify the ranges of the stability indices through membership function for preparing the fuzzy inputs. The possibility of thunderstorms with the given ranges of the stability indices is validated with the bipartite graph connectivity method. The bipartite graphs are prepared with two sets of vertices, one set for three membership functions (strong, moderate and weak) with the stability indices and the other set includes the three membership functions for the probability of thunderstorms (high, medium and low). The percentages of degree of vertex (ΔG) are computed from a sample set of bipartite graph on thunderstorm days and are assigned as the measure of the likelihood of thunderstorms. The results obtained from graph connectivity analysis are found to be in conformity with the output of fuzzy interface system (FIS). The result reveals that the skill of graph connectivity is better and supports the FIS in estimating the predictability of thunderstorms over Kolkata during the pre-monsoon season. The result further reveals from the minimum degree of vertex connectivity that among the ten selected stability indices, only four indices: lifted index, bulk Richardson number, Boyden index and convective available potential energy, are most relevant for estimating the predictability of thunderstorms over Kolkata, India.