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Spatio‐temporal relationships between rainfall and convective clouds during Indian Monsoon through a discrete lens
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-09-24 , DOI: 10.1002/joc.6812
Arjun Sharma 1 , Adway Mitra 2 , Vishal Vasan 3 , Rama Govindarajan 3
Affiliation  

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 variations. In particular, peninsular India (except west coast) is under significant convective cloud cover over a majority of days but remains rainless. We also find that much of the monsoon rainfall co-occurs with low OLR, but some amount of rainfall in Eastern and North-western India in June occurs on OLR days, presumably from shallow clouds. To study day-to-day variations of both quantities, we identify spatial patterns in the temporal gradients computed from the observations. We find that changes in convective cloud activity across India most commonly occur 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. Such changes are also accompanied by changes in the spatial distribution of precipitation. 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.

中文翻译:

通过离散镜头观察印度季风期间降雨和对流云的时空关系

印度季风是一个多变量的过程,每年在 6-9 月期间引起大雨,在空间和时间上非常不均匀。我们研究了 2004-2010 年季风降雨量与外向长波辐射(OLR,对流云量)之间的关系。为了识别、分类和可视化降雨和 OLR 的空间模式,我们使用数据的离散和时空相干表示,使用基于马尔可夫随机场的统计模型创建。我们的方法将具有相似降雨量和 OLR 空间分布的日子聚类为少量空间模式。我们发现降雨量和 OLR 中各有 8 种每日空间模式,以及降雨量和 OLR 的 7 种联合模式,描述了所有天数的 90% 以上。通过这些图案,我们发现OLR通常与降水呈强烈负相关,但具有显着的空间变化。尤其是印度半岛(西海岸除外)在大部分时间里都处于明显的对流云层之下,但仍然无雨。我们还发现大部分季风降雨与低 OLR 共同发生,但 6 月印度东部和西北部的一些降雨发生在 OLR 日,可能来自浅云。为了研究这两个量的日常变化,我们确定了从观测值计算出的时间梯度中的空间模式。我们发现整个印度的对流云活动的变化最常发生是由于南北 OLR 梯度的建立,该梯度持续了 1-2 天,并将对流云覆盖从浅到深,反之亦然。这种变化也伴随着降水空间分布的变化。因此,目前的工作提供了对复杂空间模式及其日常变化的高度简化的描述,并且可以成为未来对该过程的简化描述的有用工具。
更新日期:2020-09-24
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