NEOPRENE v1.0.1：一个基于Neyman-Scott过程生成空间降雨的Python库

IF 4 3区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Geoscientific Model Development Pub Date : 2023-09-01 DOI:10.5194/gmd-16-5035-2023

J. Díez-Sierra, Salvador Navas, Manuel del Jesus

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引用次数: 2

摘要

摘要不同聚集水平的长时间降雨序列(大多数情况下为每日或每小时)构成水文、水力和气候研究的基本输入。然而，可用记录的长度、完整性、时间分辨率或空间覆盖范围往往达不到构建可靠估计的最低要求。在这里，我们介绍一个Python库NEOPRENE，它可以生成降雨的合成时间序列。NEOPRENE模拟多地点合成降雨，再现在不同时间聚集的观测统计数据。三个案例研究举例说明了图书馆的使用，重点是极端降雨，以及将每日降雨量观测分解为每小时降雨量记录。NEOPRENE以开放许可(GPLv3)从GitHub发布，免费用于研究和商业目的。我们还为Jupyter笔记本提供了示例用例，以促进涉及脆弱性、影响和适应研究的研究人员和实践者采用Jupyter笔记本。

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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 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.

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来源期刊

Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-

CiteScore

8.60

自引率

9.80%

发文量

352

审稿时长

6-12 weeks

期刊介绍： Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.