Curating and Visualizing Dense Networks of Monsoon Precipitation Data: Integrating Computer Science Into Forward Looking Climate Services Development

Abstract

Monsoon precipitation demonstrates a wide range of spatial and temporal variability in the U.S. Southwest. A variety of precipitation monitoring networks, including official networks, municipal flood control districts, and citizen science observers, can help improve our characterization and understanding of the monsoon. The data management challenges of integrating these diverse data sources can be formidable. Computer science and data management techniques provide a pathway for the design of forward looking climate services, especially those developed in collaboration with experts in this field. In this paper we present such a collaboration, integrating natural, social and computer science expertise. We document how we identified data networks and their sources and the computer science and data management workflow we employed to integrate and curate these data. We also present the web based data visualization tool and API that we developed as part of this process (monsoon.environment.arizona.edu). We use case study examples from the Tucson, AZ region to demonstrate the visualizer. We also discuss how this type of collaboration could be extended to existing or potential stakeholder collaborations, as we facilitate access to a curated set of data that gives an increasingly granular perspective on monsoon precipitation variability. We also discuss what this collaborative approach integrating natural, social and computer science perspectives can add to the evolution of climate services.