Precipitation

Koutsoyiannis, D; Langousis, A

dc.contributor.author	Koutsoyiannis, D	en
dc.contributor.author	Langousis, A	en
dc.date.accessioned	2014-03-01T02:54:06Z
dc.date.available	2014-03-01T02:54:06Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36611
dc.title	Precipitation	en
heal.type	conferenceItem	en
heal.publicationDate	2011	en
heal.abstract	The study of precipitation has been closely linked to the birth of science, by the turn of the 7th century BC. Yet, it continues to be a fascinating research area, since several aspects of precipitation generation and evolution have not been understood, explained and described satisfactorily. Several problems, contradictions and even fallacies related to the perception and modelling of precipitation still exist. The huge diversity and complexity of precipitation, including its forms, extent, intermittency, intensity, and temporal and spatial distribution, do not allow easy descriptions. For example, while atmospheric thermodynamics may suffice to explain the formation of clouds, it fails to provide a solid framework for accurate deterministic predictions of the intensity and spatial extent of storms. Hence, uncertainty is prominent and its understanding and modelling unavoidably relies on probabilistic, statistical and stochastic descriptions. However, the classical statistical models and methods may not be appropriate for precipitation, which exhibits peculiar behaviours including Hurst-Kolmogorov dynamics and multifractality. This triggered the development of some of the finest stochastic methodologies to describe these behaviours. Inevitably, because deduction based on deterministic laws becomes problematic, as far as precipitation is concerned, the need for observation of precipitation becomes evident. Modern remote sensing technologies (radars and satellites) have greatly assisted the observation of precipitation over the globe, whereas modern stochastic techniques have made the utilization of traditional raingauge measurements easier and more accurate. This chapter reviews existing knowledge in the area of precipitation. Interest is in the small- and large-scale physical mechanisms that govern the process of precipitation, technologies and methods to estimate precipitation in both space and time, and stochastic approaches to model the variable character of precipitation and assess the distribution of its extremes.	en