A cross-scale framework for integrating multi-source data in Earth system sciences. (May 2021)

• Record Type: Journal Article
• Authors: Markonis, Yannis; Pappas, Christoforos; Hanel, Martin; Papalexiou, Simon Michael
• Is Part Of: Environmental modelling & software. Volume 139(2021)
• Publication Date: 2021-05
• Subjects: Dataset validation -- Multiscale statistics -- Earth system models -- Earth system observations -- Cross-scale analysis -- Multi-source data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.104997
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system processes at different spatiotemporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. Here, we propose a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatiotemporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatiotemporal continuum. The proposed cross-scale framework for integrating multi-source data in Earth system sciences is already developed as a stand-alone R package that is freely available to download. Highlights: A cross-scale framework for comparing datasets from different sources in all space and time scales. Comes with a simple, yet efficient, graphical tool in R that can be useful in data validation and integration studies. An example with climatic data coming from ground stations, radars, satellites and model simulations is presented.