Spatio-temporal relationships between rainfall and convective clouds during Indian Monsoon through a discrete lens
Arjun Sharma, Adway Mitra, Vishal Vasan, Rama Govindarajan

TL;DR
This study uses a discrete statistical model to analyze the spatio-temporal relationship between rainfall and convective clouds during the Indian Monsoon, revealing dominant patterns and their variations over 2004-2010.
Contribution
It introduces a Markov Random Field-based clustering approach to identify and visualize dominant spatial patterns of rainfall and cloud cover during the monsoon.
Findings
Eight rainfall and OLR patterns explain over 90% of days.
OLR generally negatively correlates with rainfall, with spatial variations.
Day-to-day changes are driven by north-south OLR gradients.
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, convective cloud cover) for monsoon between 2004-2010. To identify, classify and visualize spatial patterns of rainfall and OLR we use a discrete and spatio-temporally coherent representation of the data, created using a statistical model based on Markov Random Field. Our approach clusters the days with similar spatial distributions of rainfall and OLR into a small number of spatial patterns. We find that eight daily spatial patterns each in rainfall and OLR, and seven joint patterns of rainfall and OLR, describe over 90\% of all days. Through these patterns, we find that OLR generally has a strong negative correlation with precipitation, but with significant spatial…
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