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

Abstract: 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 multisite 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.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Geoscientific Model Development, 2023, 16(17), 5035-5048

Editorial: Copernicus Publ. para European Geosciences Union

 Año de publicación: 2023

Nº de páginas: 14

Tipo de publicación: Artículo de Revista

 DOI: 10.5194/gmd-16-5035-2023

ISSN: 1991-959X,1991-9603

Proyecto español: RTI2018-096449-B-I00

Autores/as

JAVIER DIEZ SIERRA