Ravi S Nanjundiah
Articles written in Journal of Earth System Science
Volume 126 Issue 4 June 2017 Article ID 0054
Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.
Volume 126 Issue 8 December 2017 Article ID 0111
In boreal summer (June–September), most of the Indian land and its surroundings experience rainrates exceeding 6 mm day−1 with considerable spatial variability. Over southern Bay of Bengal (BoB) along the east coast of the Indian peninsula (henceforth referred to as the Bay of Bengal cold pool or BoB-CP), the rain intensity is significantly lower (<2 mm day−1) than its surroundings. This low rainfall occurs despite the fact that the sea surface temperature in this region is well above the threshold for convection and the mean vorticity of the boundary layer is cyclonic with a magnitude comparable to that over the central Indian monsoon trough where the rainrate is about 10 mm day−1. It is also noteworthy that the seasonal cycle of convection over the BoB-CP shows a primary peak in November and a secondary peak in May. This is in contrast to the peak in June–July over most of the oceanic locations surrounding the BoB-CP. In this study, we investigate the role of the Western Ghat (WG) mountains in an Atmospheric General Circulation Model (AGCM) to understand this paradox. Decade-long simulations of the AGCM were carried out with varying (from 0 to 2 times the present) heights of the WG. We find that the lee waves generated by the strong westerlies in the lower troposphere in the presence of the WG mountains cause descent over the BoB-CP. Thus, an increase in the height of the WG strengthens the lee waves and reduces rainfall over the BoB-CP. More interestingly in the absence of WG mountains, the BoB-CP shows a rainfall maxima in the boreal summer similar to that over its surrounding oceans. The WG also impacts the climate over the middle and high latitude regions by modifying the upper tropospheric circulation. The results of this study underline the importance of narrow mountains like the WG in the tropics in determining the global climate and possibly calls for a better representation of such mountains in climate models.