Understanding spatio-temporal relationships between rainfall and convective clouds during Indian Monsoon through a discrete lens

Abstract:

The Indian monsoon, a multi-variable process causing heavy rains during June-September every year, is very heterogeneous in space and time. We study the relationship between rainfall and Outgoing Longwave Radiation (OLR) – a proxy for convective cloud cover – over seven monsoon seasons of 2004-2010 to identify, classify and visualize spatial patterns of daily rainfall and convective clouds and their day-to-day variation. For this, we use a discrete and spatio-temporally coherent representation of the data, created using a statistical model based on Markov Random Field. Our approach allows the clustering of days with similar spatial distributions of rainfall and OLR, into a small number of spatial patterns, which are the modes of such distributions. We find that eight daily spatial patterns each in rainfall and OLR, and seven joint patterns of rainfall and OLR, can describe over 90% of all days, and each pattern appears on several days of every season. Studying the relationship between OLR and rainfall using these patterns, we find that OLR generally has a strong negative correlation with precipitation, but with significant spatial variations. In particular, peninsular India (except for the west coast) is under significant convective cloud cover over a majority of days but remains rainless. We also find that almost all the monsoon rainfall co-occurs with low OLR, and only a small amount of rainfall in Eastern and North-eastern India in June seems to occur from shallow clouds. To study day-to-day variations of both quantities, we identify spatial patterns in the temporal gradients (one-day anomalies) computed from the observations. We find that changes in convective cloud activity across India most commonly occurs due to the establishment of a north-south OLR gradient which persists for 1-2 days and shifts the convective cloud cover from light to deep or vice versa, depending on its direction. Such changes are also accompanied by changes in the spatial distribution of precipitation. Thus this work identifies spatial and temporal relationships between OLR and rainfall through a highly reduced description of the data. The present work thus provides a highly reduced description of the complex spatial patterns and their day-to-day variations and could form a useful tool for future simplified descriptions of this process.