MODELING PROCESS CHAIN OF METEOROLOGICAL REANALYSIS PRECIPITATION DATA USING WORK CONTEXT

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.