MODELING PROCESS CHAIN OF
METEOROLOGICAL REANALYSIS
PRECIPITATION DATA USING WORK CONTEXT
MODELING PROCESS CHAIN OF METEOROLOGICAL REANALYSIS PRECIPITATION DATA USING WORK CONTEXT, 05 (2/3/4):
01-10 (November 2017)DOI: 10.5121/ijitmc.2017.5401
Abstract
In this paper, we propose a models of process chain and knowledge-based of meteorological reanalysis
datasets that help scientists, working in the field of climate and in particular of the rainfall evolution, to
solve uncertainty of spatial resources (data, process) to monitor the rainfall evolution. Indeed, rainfall
evolution mobilizes all research, various methods of meteorological reanalysis datasets processing are
proposed. Meteorological reanalysis datasets available, at present, are voluminous and heterogeneous in
terms of source, spatial and temporal resolutions. The use of these meteorological reanalysis datasets may
solve uncertainty of data. In addition, phenomena such as rainfall evolution require the analysis of time
series of meteorological reanalysis datasets and the development of automated and reusable processing
chains for monitoring rainfall evolution. We propose to formalize these processing chains from modeling
an abstract and concrete models based on existing standards in terms of interoperability. These processing
chains modelled will be capitalized, and diffusible in operational environments. Our modeling approach
uses Work-Context concepts. These concepts need organization of human resources, data, and process in
order to establish a knowledge-based connecting the two latter. This knowledge based will be used to solve
uncertainty of meteorological reanalysis datasets resources for monitoring rainfall evolution.