2023
DOI: 10.5194/gmd-16-5035-2023
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NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process

Javier Diez-Sierra,
Salvador Navas,
Manuel del Jesus

Abstract: 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 multi-site synthetic rainfall that reproduces… Show more

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