NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process

• Published in: Geoscientific Model Development Vol. 16; no. 17; pp. 5035 - 5048
• Main Authors: Diez-Sierra, Javier, Navas, Salvador, del Jesus, Manuel
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 01-09-2023; Copernicus Publications
• Subjects: Aggregation; Case studies; Climate studies; Daily rainfall; Ecological aggregations; Elastomers; Extreme weather; Ground stations; Hourly rainfall; Hydrology; Libraries; Mathematical models; Neoprene; Operating systems; Precipitation; Python; Rain; Rain and rainfall; Rainfall; Rainfall measurement; Rainfall simulators; Records; Software; Statistical methods; Storms; Time series; Vulnerability
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-16-5035-2023

Long time series of rainfall at different levels of aggregation (daily or hourly in most cases) constitute the basic input for hydrological, hydraulic and climate studies. However, oftentimes the length, completeness, time resolution or spatial coverage of the available records falls short of the minimum requirements to build robust estimations. Here, we introduce NEOPRENE, a Python library to generate synthetic time series of rainfall. NEOPRENE simulates multi-site synthetic rainfall that reproduces observed statistics at different time aggregations. Three case studies exemplify the use of the library, focusing on extreme rainfall, as well as on disaggregating daily rainfall observations into hourly rainfall records. NEOPRENE is distributed from GitHub with an open license (GPLv3), free for research and commercial purposes alike. We also provide Jupyter notebooks with the example use cases to promote its adoption by researchers and practitioners involved in vulnerability, impact and adaptation studies.