A GIS-big data model for improving the coverage and analysis processes of territory observation, and integrating ground-based observations with retrospective meteorological data

摘要: This paper presents Kawak, a GIS-bigdata model for merging retrospective meteorological data with ground-based observations to achieve comprehensive territorial coverage. This model identifies areas without active ground-based stations and creates virtual stations for these areas. Kawak incorporates AI algorithms for conducting spatio-temporal studies, enabling seamless merging and exploration of climate patterns. We implemented Kawak in a prototype and conducted an exploratory case study by fusing temperature records from MERRA-2 with EMAS ground-based observations in Mexico over 33 years. The findings include: (i) Missing data and outliers in ground-based observations increased over time; (ii) Ground-station territorial coverage gradually reduced; (iii) Differences between ground-based and reanalysis observations were 1.95 and 1.91 Celsius degrees for maximum and minimum yearly …